# Interpretable Directed Diversity: Leveraging Model Explanations for
Iterative Crowd Ideation

Yunlong Wang  National University of SingaporeSingapore
<EMAIL_ADDRESS>,  Priyadarshini Venkatesh  University College
LondonLondonUK<EMAIL_ADDRESS>and  Brian Y. Lim  National University of
SingaporeSingapore<EMAIL_ADDRESS>
(2022)

###### Abstract.

Feedback in creativity support tools can help crowdworkers to improve their
ideations. However, current feedback methods require human assessment from
facilitators or peers. This is not scalable to large crowds. We propose
Interpretable Directed Diversity to automatically predict ideation quality and
diversity scores, and provide AI explanations — Attribution, Contrastive
Attribution, and Counterfactual Suggestions — to feedback on why ideations
were scored (low), and how to get higher scores. These explanations provide
multi-faceted feedback as users iteratively improve their ideations. We
conducted formative and controlled user studies to understand the usage and
usefulness of explanations to improve ideation diversity and quality. Users
appreciated that explanation feedback helped focus their efforts and provided
directions for improvement. This resulted in explanations improving diversity
compared to no feedback or feedback with scores only. Hence, our approach
opens opportunities for explainable AI towards scalable and rich feedback for
iterative crowd ideation and creativity support tools.

Explainable AI, Diversity, Collective Creativity, Crowdsourcing

††copyright: acmcopyright††journalyear: 2022††copyright:
rightsretained††conference: CHI Conference on Human Factors in Computing
Systems; April 29-May 5, 2022; New Orleans, LA, USA††booktitle: CHI Conference
on Human Factors in Computing Systems (CHI ‘22), April 29-May 5, 2022, New
Orleans, LA, USA††doi: 10.1145/3491102.3517551††isbn:
978-1-4503-9157-3/22/04††ccs: Human-centered computing Interactive systems and
tools††ccs: Human-centered computing Collaborative and social computing

##  1\. Introduction

Creativity support tools (Frich et al., 2019; Shneiderman, 2007) harness the
power of human creativity through large-scale crowdsourcing for tasks, such as
text editing (Bernstein et al., 2010; Clark et al., 2018; Shah et al., 2018),
iterating designs (Dow et al., 2010), information synthesis (Luther et al.,
2015), action planning of health behavior change (Agapie et al., 2018; Hosio
et al., 2020), and motivational messaging (Ahmed et al., 2019; Cox et al.,
2021; Kocielnik and Hsieh, 2017; de Vries et al., 2016). Among techniques for
creativity support, providing timely and proper feedback is a promising method
to boost crowd ideation creativity (Bharadwaj et al., 2019; Dow et al., 2012;
Duque-Estrada, 2014; Oppenlaender and Hosio, 2019b; Oppenlaender et al., 2020;
Yen et al., 2017). Many feedback methods require human assessment from
facilitators or peers, but this limits their ability to scale to large crowds.
Employing non-expert crowdworkers can scale more than with experts (Duque-
Estrada, 2014), but costs can escalate and they cannot provide feedback in
real-time. Machine learning can be used to automatically provide feedback by
predicting ideation quality and showing examples of high-quality ideations
(Peng et al., 2020). However, besides ideation quality, it is also important
to increase the diversity and reduce redundancy in submitted ideations
(Bjelland and Wood, 2008; Klein, Mark, and Garcia, 2015; Riedl et al., 2010;
Siangliulue et al., 2016). Prior collaborative creativity support methods to
avoid redundancy include employing an adaptive task workflows (Yu and
Nickerson, 2011), constructing a taxonomy of the idea space (Huang and Quinn,
2017), visualizing a concept map of peer ideas (Siangliulue et al., 2016), and
directing ideation towards automatically selected diverse prompts (Cox et al.,
2021). Current methods to drive diversity provide information from a prior or
peer set of ideations, and are not specific to each worker’s ideation. This
limits the relevance of feedback (Yang et al., 2021). Therefore, it is
important to provide contextualized feedback tailored to the worker’s
ideation. In this work, we support both criteria of higher quality and
diversity for crowd ideation, specifically targeting the use case of writing
motivational messages to promote physical activity.

Although AI can predict scores on ideations, showing scores alone has limited
usefulness. Consider receiving feedback in school. To promote learning and
improvement, students not only receive graded assignments, but additional
feedback to indicate problems in their work, tips or examples for improvement,
and opportunities for revision. In the Hattie and Timperley feedback model,
these correspond to a score (Feed Up), critical feedback (Feed Back), useful
tips or examples (Feed Forward) (Hattie and Timperley, 2007). For ideation
writing, we provide each as feedback correspondingly using explainable AI
(XAI) with 1) predicted scores, 2) attribution explanation (highlights) of
problematic terms, 3) contrastive attributions to provide feedback between
revisions, and 4) counterfactual suggestions to provide tips for how to
replace problematic terms. We provide feedback across multiple iterations to
let workers revise their ideations. Unlike most uses of XAI to improve user
understanding and trust of AI (Bansal et al., 2021; Lim et al., 2009; Tsai et
al., 2021; Wang and Yin, 2021), we focus on improving human task performance
with human-XAI collaboration (Wang et al., 2019). Furthermore, due to the
limited use of AI in crowd ideation, the design and effectiveness of XAI for
such tasks is an open question. Our contributions are:

  1. 1.

Interpretable Directed Diversity, an explainable AI approach to predict the
quality and diversity of ideations and generate multi-faceted explanatory
feedback. This enables scalable, real-time, contextual feedback to improve
collective creativity.

  2. 2.

Three explanation types — Attributions, Contrastive Attributions, and
Counterfactual Suggestions — for two criteria, quality and diversity. We
implemented the algorithmic approaches in an interactive crowd ideation
system.

We evaluated the usage, understandability, and usefulness of the explanations
for creative ideation in a formative user study and summative user studies
with ideators and validators. We found that score feedback with attribution,
contrastive attribution, and counterfactual suggestion explanations are
complementary to improve ideation quality and diversity. We discuss the
generalization of our explainable feedback approach to other domains and
explanations.

##  2\. Related Work

###  2.1. Creative Ideation and Feedback

Creative ideation involves complex cognitive processes (Amabile, 1996; Boden,
1996, 2004; Wang and Nickerson, 2017). Memory-based explanation models
describe how people retrieve information relevant to a cue (prompt) from long-
term memory and process it to generate ideas (Adelman et al., 1995; Dougherty
et al., 1999; Lubart, 2001; Nijstad and Stroebe, 2006; Nijstad et al., 2002).
Ideation-based models (Missier et al., 2015) explain how individuals can
generate many ideas through complex thinking processes, including analogical
reasoning (Green et al., 2012; Keller and Ho, 1988; Meheus, 2000), problem
constraining (Smith, 2010), and vertical or lateral thinking (Goel, 2010).
While users may ideate creatively, their initial ideation can be improved with
proper feedback (Dow et al., 2010, 2012, 2009). The efficacy of providing
feedback has been much studied in the domains of education (Hattie and
Timperley, 2007) and management (Ramaprasad, 1983) to improve learning and
decision making. Hattie and Timperley (Hattie and Timperley, 2007) argued that
effective feedback should answer three questions: where am I going, how am I
going, and where to next. Applying goal-setting theory by showing summary
feedback (Locke and Latham, 1990, 2002), Carson and Carson (Carson and Carson,
1993) improved individual creativity by manually reviewing ideations and
providing feedback on the quantity and creativity of distinct ideas. However,
providing feedback often requires intervention from human experts, and this
limits their scalability and rapid iteration. In this work, we propose an
automated approach for creativity support tools to provide feedback for
creative ideation.

Furthermore, we focus on collective creativity from ideations of a crowd of
individuals. While individual creativity is important, society values ideation
novelty more. Boden refers to these as P-creativity and H-creativity,
respectively (Boden, 1996). “An idea is P-creative if it is creative with
respect to the mind of the person concerned, even if others have had that idea
already. An idea is H-creative if it is P-creative and no other person has had
the idea before. H-creativity is more glamorous, but P-creativity is more
fundamental” (Boden, 1996). In crowd ideation, we seek to avoid the
wastefulness of redundant ideas, thus we value H-creativity more. Being
H-creative requires the ideator to study prior ideations to avoid replicating
them. In this work, we support H-creativity by providing automatic prompts and
feedback to guide ideators away from prior ideations.

###  2.2. Feedback in Creativity Support Tools

Creativity Support Tools have been widely studied in HCI to help crowdworkers
to ideate more effectively and at scale (Frich et al., 2019, 2018). Effective
methods to support crowd ideation include sharing peers’ ideas (Siangliulue et
al., 2016), relevant concepts (Bae et al., 2020), and expert guidance (Yang et
al., 2021), contextual framing (Oppenlaender and Hosio, 2019a), constructing
ideation taxonomies (Huang and Quinn, 2017), providing diverse prompts (Cox et
al., 2021), and feedback ratings (Peng et al., 2020). We categorize these
approaches as manual feedback from people and automated feedback from
intelligent systems. Methods using manual feedback investigated who should
provide the feedback (Dow et al., 2012; Oppenlaender and Kuosmanen, 2021) and
how to coordinate ideators (Duque-Estrada, 2014; Oppenlaender et al., 2020).
Using Voyant (Duque-Estrada, 2014), poster designers had access to a non-
expert crowd to receive structural feedback on their designs. Likewise,
CrowdUI (Oppenlaender et al., 2020) enables web designers to elicit visual
feedback from the website’s community of users. While these methods are
adaptable to various domains, they require much human labor to prepare
feedback and are difficult to scale.

In contrast, recent works have developed automatic systems to generate
feedback for mind mapping (Bae et al., 2020), story writing (Clark et al.,
2018), metaphor creation (Gero and Chilton, 2019), and writing supportive
comments for online mental health community (Peng et al., 2020). The feedback
provided in these works were limited to independent examples from peers or
machine-suggested words or sentences, but not contextual to the ideations.
MepsBot (Peng et al., 2020) predicts scores for informational and emotional
support, which is specific to the submitted ideations. However, the
justification for such scores is unclear to users, hence we provide AI
explanations in this work for more actionable feedback.

Finally, prior works focus on individual creativity, so ideations from
multiple ideators may be redundant. Directed Diversity (Cox et al., 2021)
addressed this problem by automatically selecting diverse prompts for
ideation, but did not provide feedback after ideation. In this work, we
address the difficult problem of providing contextual feedback for collective
ideation, where each ideator cannot see all prior messages. We achieve this by
providing model explanations towards a higher automatically-calculated
diversity score.

###  2.3. Explanations in Intelligent Systems

Explainable AI (XAI) techniques have drawn much attention as intelligent
systems become more complex, requiring transparency and accountability (Abdul
et al., 2018; Wang et al., 2019). Prior research has shown that XAI can
increase user trust and understanding of AI systems (Bansal et al., 2021; Wang
and Yin, 2021), and explored designing proper XAI to improve human-AI
collaboration, e.g., for computer-aided translation (Coppers et al., 2018),
playing Chess (Das and Chernova, 2020), and music creation (Louie et al.,
2020). Though many studies have sought to identify optimal explanation types
in specific tasks (Lim et al., 2009; Tsai et al., 2021), some found that
providing a variety of explanations can have stronger benefits (Anderson et
al., 2020) or support various usage strategies (Lim and Dey, 2011, 2013).
While prior studies have found that XAI can improve system transparency,
fairness, and acceptance, we explore how XAI can improve user creativity and
employed multiple explanations to stimulate creativity.

##  3\. Technical Approach

We aim to direct ideators towards improved ideations by providing automatic
feedback and explanations. For each prompted ideation, ideators can learn from
the feedback to iteratively improve their ideations. Figure 1 shows the
iterative process of Interpretable Directed Diversity. It involves two high-
level phases to prepare and provide prompts (Steps I, II, III), and analyze
the ideation and provide feedback iteratively (Steps 1, 2a, 2b). Phase 1
follows the same steps as in Directed Diversity (Cox et al., 2021) to I)
curate prior ideations, II) generate diverse and non-redundant prompts, and
III) send these prompts to ideators. Phase 2 is the main focus of this paper,
extends Directed Diversity (Cox et al., 2021) and involves, for each ideation,
1) analyzing the written ideation, 2) showing feedback based on a) prediction
scores and b) explanations; the analysis and feedback are repeated as the
ideator iteratively revises each ideation. The technical contribution of this
work is the predictive and explanatory feedback after the prompting and
ideation in Directed Diversity (Cox et al., 2021).

Figure 1. Overall technical pipeline to curate collected ideations, prepare
diversely selected prompts, send the prompts, and provide feedback (as scores
and three explanation types) for iterative ideation. Dots represent prompts
(green) and ideations (red for prior, blue for iterations of new ideation) as
points in a 2D vector space of ideas. A prompt will attract ideations towards
it, though influence will depend on prior iterations and explanation type.

Abstract scatter plots showing the overall technical pipeline to curate
collected ideations, prepare diversely selected prompts, send the prompts, and
provide feedback. Ideations and prompts are shown as dots and the distance
between dots represents semantic difference.

###  3.1. Feedback Score from Prediction Models

The base feedback provides assessment scores of the ideation. Currently, we
predict two scores using a machine learning model and a heuristic model. The
first goal for ideation is typically to ensure high-quality ideas, hence, we
predict a quality score $s_{q}$. However, to mitigate redundancy in crowd
ideation (Cox et al., 2021), another goal is to increase diversity, which we
predict with a diversity score $s_{d}$. Hence, we integrate both scores in our
modeling.

####  3.1.1. Quality Score Prediction

We predicted quality using a machine learning model, since it is rated by
people. We trained the prediction model based on data collected by Cox et al.
(Cox et al., 2021). The training dataset consists of 815 ideation messages
$\bm{x}\in X$ written by 250 ideators and rated by 150 validators on quality
(motivating). Ideators wrote messages to encourage physical activity and were
shown various prompts (None, Random, Directed Diversity). Each message was
rated by M=4.6 validators on a 7-point Likert scale (–3 very demotivating to
+3 very motivating). We model the prediction problem as a binary
classification instead of regression on 7 intervals to simplify it, because
subjective ratings are noisy and suffer from individual variance. Furthermore,
we binarized the rating of each message to whether it was highly motivating
(above Median = 1.17) to create a balanced dataset (equal number of positive
and negative classes) for better model training. While this approach includes
borderline ratings that may train an inaccurate decision boundary, it benefits
from training with a larger dataset. See Table 5 in Appendix A.1 for example
training data. Similar to (Cox et al., 2021), we computed the Universal
Sentence Encoder (USE) (Cer et al., 2018) embedding $\bm{z}$ for each ideation
to represent its idea as a vector. Using this embedding and normalized message
length as input features, we trained a two-layer fully-connected neural
network with sigmoid activation in the final layer to predict quality. The
model was accurate with AUC = 0.717 from a 5-fold cross-validation. We
determine the quality score $s_{q}$ as the prediction confidence (0-100%).
Note that this represents the probability of the message being highly
motivating, rather than the estimated rating of a validator in (Cox et al.,
2021).

####  3.1.2. Diversity Score Calculation

While (Cox et al., 2021) had also collected validator ratings on perceived
diversity, those measures relate to pairs or groups of messages rather than
individual ones. We require a single diversity label for each message to
provide direct feedback during ideation, hence we use a heuristic calculation.
We determined the diversity score $s_{d}$ by adding the new ideation $\bm{x}$
to the prior ideations $X$, and calculating the increase in the Ideation
Dispersion (MST Sum of Edge Weights) metric as defined in (Cox et al., 2021)
from the prior collection to the new collection with the new ideation. For
evaluation (later section), we initialized with 50 prior ideations from
messages collected by Cox et al. (Cox et al., 2021).

###  3.2. Feedback Explanations from Explainable AI

Inspired by Miller (Miller, 2019), we propose actionable explanations to
increase ideation quality and diversity — attribution, contrastive
attribution, and counterfactual suggestions. These provide multi-faceted
feedback for ideators to understand issues and opportunities to ideate better.
Our explanation techniques are agnostic to the prediction models and can
generalize to multiple scores. For brevity, we combine the notation for the
quality and diversity scores as vector $\bm{s}=(s_{q},s_{d})^{\top}$. Next, we
describe each explanation type in detail.

####  3.2.1. Attribution Explanation

Attribution explanations answer the question “Why P”. They indicate which
features are influential in a prediction. For ideated messages, we treat each
word as a feature. Each attribution may support (positive value) or undermine
(negative) the prediction. The larger the magnitude, the stronger the
influence. Since the goal is to increase the score, the explanation should
explain which features most hinder a higher score, and ideators should focus
on words with the most negative attribution. There are several methods for
calculating feature attributions, such as calculating their gradients
(Sundararajan et al., 2017), backpropagated relevance (Binder et al., 2016),
Shapley values (Lundberg and Lee, 2017), or approximating linear slopes
(Ribeiro et al., 2016). However, these methods are computationally expensive,
since they involve iterating through many parameters, features, or neighboring
instances. This is infeasible for our application, since we need fast
calculations for live feedback and cannot pre-compute explanations. Instead,
we use a simpler sensitivity analysis based on ablation (Richardson et al.,
2006; Tullio et al., 2007). Our approach to calculating attributions are as
follows:

  1. 1.

Define the ideation message as $\bm{x}$ with the $r$th word as $x_{r}$

  2. 2.

Ablate (remove) the rth feature $x_{r}$ from the dataset

  3. 3.

Calculate the score of the new simulated ideation
$\bm{s}(\bm{x}\setminus{\\{x_{r}\\}})$

  4. 4.

Calculate the feature attribution $w_{r}$ as the decrease in the predicted
score $\bm{s}$, i.e.,
$w_{r}=-(\bm{s}(\bm{x})-\bm{s}(\bm{x}\setminus{\\{x_{r}\\}}))$

  5. 5.

Shift all attributions to be negative to emphasize the most important features
to change with largest magnitude, i.e., $w_{r}\rightarrow w_{r}-\min_{\rho\in
R}(w_{\rho})$. This makes the attributions more actionable.

Figure 2. Conceptual approach for explanations. Each square represents a word,
each letter for a different word. A word can be existing (solid line),
inserted (double-line), or deleted (dotted). Colors indicate attribution: red
= word that should be changed to increase score, blue = good word that
increased or could increase the score, darker color = higher score magnitude.
a) For an ideation (top row), the attribution towards a word is based on the
score decrease (right stacked bar chart) when it is deleted from the ideation
(e.g., A, C, D). Their sum is the total Attributions. b) Contrastive
attributions compare an ideation $t+1$ (second row) with its previous
iteration $t$ (first). B and C were deleted, while $\mathcal{M}$ was inserted.
Attributions for these edits show that deleting C and adding $\mathcal{M}$
were beneficial, while deleting B was detrimental. c) Counterfactual
Suggestions involve searching for related words from a knowledge graph (lower
left) and calculating their Attribution towards increasing the scoring. Using
the suggested word M as inspiration, the ideator may replace C with
$\mathcal{M}$.

Abstract figure showing different types of explanations. Each word in an
ideation is shown as a square. Colors of squares are used to indicate the
relative values in explantions.

Figure 2a illustrates this algorithm. This approach can be applied to the
Diversity and Quality scores, since it is agnostic to how the score is
calculated (can be machine learning or heuristic). See the red highlights
(negative attributions) of the feedback user interface in Figure 6 for an
example of attributions explanation.

####  3.2.2. Contrastive Attribution Explanation

Figure 3. Conceptual effect of providing different types of explanations for
ideation feedback with the intent to increase the score for diversity (top
row) or quality (bottom row) separately. Dots represent prompts (green) and
ideations (red for prior, blue for iterations of new ideation) as points in a
2D vector space of ideas. 1) Darker red regions indicate locations in the
vector space that are dense with prior ideations that new ideations should
avoid increasing diversity. 2) Yellow areas indicate a nonlinear decision
surface with the change in color representing a sharp decision boundary. 1,2)
Purple arrows indicate the directional influence of the feedback. a)
Attribution explanations direct ideation towards a higher score. b)
Contrastive attribution explanations convey the difference between iterations
along the direction of a higher score. c) Counterfactual suggestion
explanations direct towards an alternative, similar ideation with a higher
score.

Abstract scatter plots showing the hypothetical effect of different feedback
on ideation diversity and quality. Ideations and prompts are shown as dots and
the effects of feedback are shown as arrows.

Contrastive explanations answer the question “Why P and not Q”. The
contrastive attribution explanation extends the attribution explanation to
focus on specific differences between two classification labels, and we extend
their use to contrast between two iterations, $\bm{x}^{(t_{1})}$ and
$\bm{x}^{(t_{2})}$, of an ideation. This identifies the edit differences in
word attributions between them, which are either insertions or deletions. For
simplicity, we do not distinguish word order. We generate contrastive
explanations for ideation iteration with these steps:

  * 1.

For each inserted word $x_{r\in R_{ins}}$, calculate attribution as $w_{r\in
R_{ins}}^{(t_{2})}=(\bm{s}(\bm{x}^{(t_{2})})-\bm{s}(\bm{x}^{(t_{2})}\setminus\\{x_{r\in
R_{ins}}\\}))$

  * 2.

For each deleted word $x_{r\in R_{del}}$, calculate attribution in reverse,
i.e., add the word to the later iteration $x^{(t_{2})}$ and compute the
decrease in score, as $w_{r\in
R_{del}}^{(t_{2})}=-(\bm{s}(\bm{x}^{(t_{2})})-\bm{s}(\bm{x}^{(t_{2})}\cup\\{x_{r\in
R_{del}}\\}))$

  * 3.

Calculate the change in scores, i.e., $\Delta
s=\bm{s}(\bm{x}^{(t_{2})})-\bm{s}(\bm{x}^{(t_{1})})$

  * 4.

Calibrate total attributions to match the change in scores, i.e.,

    * 1.

Min-max normalize all attributions to between 0 and 1

    * 2.

Shift the attributions such that $\sum_{r\in{\\{R_{ins}\cup
R_{des}\\}}}w_{r}^{(t_{2})}=\Delta s$

Figure 2b illustrates this algorithm. See the red (negative attributions) and
blue (positive) highlights of the feedback user interface in Figure 7 for an
example of contrastive attributions explanation.

####  3.2.3. Counterfactual Suggestion Explanation

Counterfactual explanations answer “How to change inputs to get Q instead of
P”. For ideation, this determines how an ideation could be edited to increase
its score. We propose to substitute words with alternative suggestions
searched using the ConceptNet knowledge graph v5.8 (Speer et al., 2017) based
on various semantic relationships. This will reduce the cognitive load,
mitigate the ideator’s lack of experience with recalling related terms, and
stimulate more ideas (Bae et al., 2020). In an ideation $\bm{x}$, for each
important feature word $x_{r}$ with negative attribution,

  * 1.

Search for related words $x_{\rho_{r}}$ using the knowledge graph

    * 1.

Exclude feature words $x_{r}$ with too few (¡10) related words

    * 2.

Exclude relations111Excluded: Synonym, Antonym, DerivedFrom, SymbolOf,
DefinedAs,MannerOf, EtymologicallyRelatedTo, EtymologicallyDerivedFrom,
ExternalURL.  that are less actionable

  * 2.

For each related word $x_{\rho_{r}}$,

    * 1.

Substitute feature word $x_{r}$ with its related word $x_{\rho_{r}}$ into the
ideation message $\bm{x}$

    * 2.

Filter word $x_{\rho_{r}}$ for relevance

      * 1.

Compute USE (Cer et al., 2018) embedding vector $\bm{z}_{r}$ for the word
$x_{\rho_{r}}$

      * 2.

Compute mean pairwise distance $\bar{d}_{\rho_{r}}$ to prior ideations

      * 3.

Exclude word if out-of-scope, like in (Cox et al., 2021), i.e.,
$\bar{d}_{\rho_{r}}>\delta$

    * 3.

Calculate attribution $w_{\rho_{r}}$ due to deleting $x_{r}$ and inserting
$x_{\rho_{r}}$, i.e.,
$w_{\rho_{r}}=-(\bm{s}(\bm{x})-\bm{s}(\bm{x}\cup\\{x_{r}\\}\setminus\\{x_{\rho_{r}})\\}))$

    * 4.

Include related word with large attribution, i.e., $w_{\rho_{r}}>\omega$

Figure 2c illustrates this algorithm and Figure 8 shows a demo. We limited
Counterfactual suggestions for words with negative attribution. The approach
cannot replace phrases or whole sentences.

####  3.2.4. Summary and Hypothesized Effects of Explanations

We proposed three actionable explanations for improving ideation scores.
Specifically, we a) shifted Attribution explanations to frame influence by
which words are best to improve the ideation; b) framed Contrastive
Attributions by what changes were successful or detrimental; and c)
streamlined Counterfactual Suggestions to recommend non-distant words that are
estimated to improve scores.

These explanations aim to direct ideators towards higher scores, but have
slight differences in direction (Figure 3). The Attributions explanation
(Figure 3a) points in the direction of increasing score, but may be prone to
some error due to the approximations in the ablation technique (e.g., not
calculating Shapley values (Lundberg and Lee, 2017)). The Contrastive
Attributions explanation (Figure 3b) describes how the difference between two
iterations works towards or away from increasing score; this is equivalent to
resolving the blue arrow vector along the red/yellow line vector. It is also
subject to approximation errors like Attributions. The Counterfactual
Suggestion explanation (Figure 3c) directs the ideator towards a hypothetical
ideation with the substituted word(s) , which may not be the most direct
towards increasing score. Finally, all three explanation types do not
necessarily direct the ideation straight towards the original prompt, but this
can also provide opportunities for diversification.

##  4\. Prompt and Feedback Interfaces

We describe the ideation interface variants and how to use them.

###  4.1. Ideation Task and Prompt Interface

Consider the ideator’s task to write motivational messages to encourage
exercise and physical activity. For each ideation, she is prompted with a
phrase and asked to write a message inspired by the phrase (Figure 4). She
does not need to use the words or concepts if she finds them too irrelevant or
awkward. After the first iteration, the participant is shown feedback and
asked to revise the ideation, up to two times. For each iteration, she is
reminded of the fixed original prompt and shown feedback based on her writing.

Table 1. Feedback Conditions in user studies. We investigated 6 variants of the Feedback Interface (columns), formed from combinations of four Feedback Features (rows). Feedback Interface is the main independent variable in our experiments. | Feedback Interface
---|---
Feedback Feature | N | S |  SA |  SAX |  SAC |  SAXC
Score Prediction (S) |  | ✓ |  ✓ |  ✓ |  ✓ |  ✓
Attributions (A) |  |  |  ✓ |  ✓ |  ✓ |  ✓
Contrastive Attributions (X) |  |  |  |  ✓ |  |  ✓
Counterfactual Suggestions (C) |  |  |  |  |  ✓ |  ✓

Figure 4. Prompt UI for ideation.

Screenshot of Prompt UI for ideation. The phrase ”did not regularly exercise”
is shown to inspire an ideator to write a message to motivate exercise. The
written message is ”Physical activity is good for health. Let’s go for some
exercise.”

Figure 5. User interface with Score feedback (S) showing Diversity and Quality
scores for each iteration.

Screenshot of UI with Diversity and Quality scores for each iteration.

Figure 6. Score + Attribution (SA) feedback showing the most attributed words
for the selected score (Diversity, in this case). Darker red indicates higher
importance. Hovering over a highlighted word shows the attribution sub-score.

Screenshot of UI with Attribution explanations, besides Diversity and Quality
scores for each iteration. For each ideation, three words are highlighted to
be prioritized for revision.

Figure 7. SA + Contrastive Attribution explanations (SAX) feedback comparing
Attempts 1 and 3. Underlined words were inserted in Attempt 3 compared to 1,
and struck-out words were deleted from Attempt 1 to 3. Inserting words “walk”,
“sitting” and “time”, and deleting “exercise” increased Diversity score, but
inserting “reduce” decreased the score.

Screenshot of UI with Contrastive Attribution explanations, besides Diversity
and Quality scores for each iteration. The edited words between two iterations
are highlighted with different colors to show their effect on the change of
ideation score.

Figure 8. SAX + Counterfactual Suggestion explanations (SAXC) feedback.
Hovering a highlighted word will show a pop-up with attribution importance,
and suggested replacement words. Potential score increases indicated with blue
highlights; darker blue for higher increase. This suggests changing “time” to
“musical time” to increase diversity by +1%, but to ”dreamlining” to increase
quality by +2%.

Screenshot of UI with Counterfactual Suggestion explanations, besides
Attribution explanations and scores for each iteration. Three suggested words
are shown when hovering a highlighted word.

###  4.2. Feedback Features and Interfaces

We investigated 6 variants (see Table 1) of the Feedback Interface that had
different combinations of whether to show explanations and which type
(Feedback Features). Note that since all XAI-based feedback explain the Score,
they cannot omit showing it. Furthermore, since attribution is foundational to
contrastive attribution and selecting words for counterfactual suggestions,
all XAI-based feedback interfaces include Attribution explanations.

The first baseline interface with No feedback (N) (Feedback Condition = N,
Figure 5, left two columns only) only shows the previous messages that the
ideator wrote in separate rows in a table. The ideator is only asked to write
the next iteration without any other information. The second baseline
interface, adds Scores (S) on the right columns of the feedback table (Figure
5). Ideators can see two scores for motivating-ness (quality) and diversity
increase. The quality score shows confidence % of the model when predicting
high quality. The diversity score was normalized with 100% for the maximum
possible angular distance ($\pi$ on the unit hypersphere).

We next describe the XAI-based Feedback Features used in the interpretable
feedback interfaces. Note that we describe them as revised after findings from
the formative study (described later). Attribution (A) explanations (Figure 6)
are presented as red highlights to indicate key words that ideators should
consider changing to improve the ideation scores. Darker reds indicate more
negative attributions. These exclude stop words (e.g., “the”, “to”). To limit
information overload, only three words are highlighted. Since the quality and
diversity objectives are not necessarily aligned (Figure 3), the highlights
are specific to each score only one at a time. Ideators view explanations for
each score by selecting its radio button (in table heading). When the ideator
hovers her mouse over a highlighted word, a popup will show its attribution
sub-score.

Contrastive Attribution (X) explanations (Figure 7) are similar to Attribution
explanations, but only word differences between two iterations are
highlighted. The last iteration can be compared against any earlier iteration.
Deletions are highlighted in the earlier iteration, while insertions are
highlighted in the last iteration. Red highlights have the same meaning as for
Attribution explanations, while blue highlights indicate beneficial edits
(positive attribution) for verifying successful edits. Darker blues indicate
stronger improvements. Currently, only iterations of the same ideation are
compared, though future work could compare different ideations.

Counterfactual Suggestion (C) explanations (Figure 8) suggest alternative
words to replace a problematic word (negative attribution). Hovering on a red
highlighted word shows a popup with its attribution to the score (same for A
and X explanations), and lists related words that could be used for
substitution. The potential change in both scores is calculated for each
related word. Ideators can decide whether to improve quality or diversity,
decide their relevance, and integrate that word or something else. Ideators
are reminded that they can propose their own terms instead of what was
suggested. To limit confusion and increase utility, only words which a) have
an increase in either score and b) are not too irrelevant (i.e., not distant
from “physical activity”) are included.

###  4.3. System Implementation and Initialization

The system was deployed in a survey implemented in Qualtrics, which embeded an
external webpage for the ideation user interface, hosted from a web server
with Intel Xeon CPU E5-2640 v4 @ 2.40Ghz x 40, 128GB RAM, and Tesla P100 GPU.
We used the GPU for calculating the USE embeddings for prompt phrases,
ideations, and words; this is used for diversity score predictions and
attribution calculations. Each calculation of the score prediction and
explanations took 3-4 seconds for messages with 20-40 words, which pilot
participants found as an acceptable wait time.

To prompt ideators, we chose 50 Directed phrases from the corpus of (Cox et
al., 2021), which we randomly sampled without replacement for each
participant. Each ideator will not see repeated prompts. The collection of
prior ideations was initialized with 50 ideations from (Cox et al., 2021)
randomly chosen from the None condition (no prompting). For ecological
(external) validity, we dynamically updated the collection of prior ideations
after each ideator submits an ideation. This captures the growth of diversity
in the collection as more ideations are submitted. We do this separately for
each Feedback Interface variant to keep them independent, i.e., ideations from
ideators in each UI condition are only added to the collection of prior
ideations for that condition so as not to “contaminate” other collections.

##  5\. Evaluation

We evaluated the usability, understandability, usage and usefulness of
different Feedback Interfaces in multiple studies. First, we conducted a
Formative Ideation Study to qualitatively observe how ideators use the
different feedback and elicit their opinions about the feedback. This helped
us to identify usage patterns, ensure that the interfaces were usable and
useful, and mitigate any usability issues. Next, we conducted a controlled
Summative Ideation Study with participants as ideators to determine if
providing explanations improved ideation Quality and Diversity over No
Feedback or Score-only Feedback baselines, to compare which explanations were
better, and to assess the ease of understanding and ease of use for each
explanation type. In this study, we measured ideation Quality and Diversity
from computational metrics, but these may be non-representative of how people
would perceive the ideations. Hence, we conducted Validation Studies of
Ideation Quality and Ideation Diversity to measure perceptions from third-
party validators. Two studies were required due to the different experiment
procedures for each measure — individual ideation rating for Quality and
pairwise ideation rating for Diversity, like in (Cox et al., 2021).

###  5.1. Formative Ideation User Study

We conducted a formative user study to investigate 1) how users interpreted
various explanation features, 2) how that influenced their ideation, and to
identify 3) usability or interpretability issues in our initial design. We
then refined our user interfaces for the subsequent summative user study
(described later).

####  5.1.1. Method and Procedure

We recruited 15 participants from a university mailing list. They were 6 male,
9 female, with ages 21-33 years old, and all were students. The experiment
took 40-50 minutes and participants were compensated $10 SGD ($7.43 USD). We
employed a within-subjects design with Latin square arrangement to mitigate
order effects. We conducted the study online via a Zoom audio call with screen
recording, which the participant consented to. Participants completed a
tutorial and could clarify instructions with an experimenter. Next,
participants performed one ideation session for each of all 6 variants of the
Feedback Interface listed in Table 1. For each ideation session, the
participant was prompted with a different phrase to ideate a motivational
message for physical activity. After submitting the attempt, she saw the
feedback, and revised her message; this happened for two revisions. Thus, the
experiment was within-subjects with 6 Conditions $\times$ 1 Ideation $\times$
3 Iterations. Using the think aloud protocol, participants were encouraged to
speak their thoughts as they read the prompt, feedback, and thought about what
to write. We also asked them about their experience with feedback and comments
for improvement.

####  5.1.2. Qualitative Findings

We transcribed the interviews and performed a thematic analysis of usage and
utterances. Guided by the three goals for the formative study, one co-author
coded the findings into themes with regular discussion with other co-authors.
We organized our findings by our three objectives: feedback interpretations,
ideation approach, and interpretability issues.

Feedback interpretations. Typical feedback usage involved: 1) noting the
prediction scores; 2) examining which words were highlighted in red to “know
which words to focus on”[P13]; 3) looking up related words with the
counterfactual suggestion popup, and considering which would lead to the
highest score increases for both diversity and quality. After the second
iteration, participants would also use the compare mode to check which words
were detrimental or useful. In summary, the exploration sequence was
attributions $\rightarrow$ counterfactual suggestions $\rightarrow$
contrastive attributions. However, participants had some difficulties when
making sense of the feedback. Some participants tried to self-explain the
quality score, since this concept is more commonplace, but had to depend on
the feedback regarding the diversity score, since “without the scores I
wouldn’t be able to tell whether my sentence is diverse” [P12] and “with the
related words I know whether there will be an increase” [P13].

When not receiving explanations, some participants struggled to deeply
understand why their ideation scored poorly on quality. For example, P14 found
that “the scores seem a little arbitrary”; P10 felt that it “doesn’t show why
it is motivating”; and P3 wondered why ‘exercise’ was highlighted, since
“[exercise] is the main word, not like I could really change anything about
it”. This was actually because many participants redundantly used this word.
Currently, our approach highlights culpable words, but this suggests the need
for semantic explanations. Participants were also confused when their scores
decreased despite following the explanations. P12 “tried to change the word
with a suggestion but wasn’t sure why the score decreased”. After being
suggested the word ‘desirable’ to replace ‘wanted’ with a projected +5% to
quality the score, P13 wrote ‘desired’ and was confused to get a 2% score
decrease instead. Thus, the high dimensionality of language modeling can lead
to spurious errors due to non-linear relations, harming user experience.
Nevertheless, in our later study, we found that the feedback was useful for
multiple users. Finally, while our feedback was automated from a corpus and
language model, P10 wanted suggestions to “try including [words] based on
other people’s answers” to rely on social proof (Cialdini et al., 1999).

Ideation approach. Participants ideated differently based on feedback type.
Without explanations, they mostly depended on trial-and-error. P13 felt that
writing more specifically, concretely, or with simpler words could increase
diversity scores; for example, she revised the term ‘physical activity’ to
‘pull up’ to be more diverse “because you’re taking a specific activity rather
than a general term”. Writing more specific terms is consistent with goal
setting theory (Locke and Latham, 1990, 2002) and distinct words have
different embedding placements in our vector space. Participants could be more
focused with highlighted words as they “gave me something to work off” [P14].
Next, we describe some interesting breakdowns and user mitigations.

Some participants struggled with the potentially divergent nature of the
quality and diversity criteria. There was diverse preference to focus on
either improving quality since it was more intuitive (e.g., P6, P10), or
diversity since it fluctuated more at each iteration (e.g., P1, P4). Hence, to
prioritize either criteria, each score could be rescaled to nudge users
accordingly. Due to the breadth of concepts in ConceptNet, participants found
that the suggested words sometimes seemed irrelevant, yet some could be
tangentially inspiring. On being suggested the terms ‘skate’ and ‘release
energy’ to replace ‘exercise’, P11 substituted with ‘swimming’, perhaps
because of finding another activity that is more energetic than skating and
remembering terms starting with ‘s’. Some participants were too adherent to
the suggestions to the extent of losing task relevance. On receiving the term
‘arsenic trichloride’ (with potential +1% for Diversity and +4% for
Motivating) to replace her word ‘organic’, P11 used the chemical term in her
ideation and rewrote “… by augmenting physical activities with  organic
supplements” to “… by augmenting physical activities with arsenic trichloride
vitamins”, which is nonsensical since Arsenic trichloride ($AsCl_{3}$) is
actually a highly toxic substance. This indicates that it is important to have
an additional step to filter ideations for safety. Furthermore, while the
feedback is helpful to improve scores, we found that some participants drifted
away from the prompt phrase. Starting with the phrase ‘right to take care’ and
writing ‘You are  responsible for taking  care of your own health’, P8 fixated
on improving the lower scoring word ‘responsible’, and ended up writing “You
are in  control of your own health”, which inadvertently dropped the word
‘care’; thus, she neglected her original prompt, though this did slightly
improve her calculated diversity (55% to 56%). Finally, we found that our
focus on word-based feedback could limit some ideation styles. Interestingly,
when ideating without feedback, some participants wrote with a collective
tone, e.g., “Let’s go and exercise” [P1], “We can start our exercise with some
stretching” [P4]; but this tone was absent when feedback was provided, and
ideations became neutral and formal, e.g., ”Exercising will reduce chances of
you going for surgeries and you can feel better, lose weight and be fitter”
[P1].

Interpretability issues and remedies. We discuss issues identified by
participants and remedies that we subsequently implemented for the interfaces
tested in the summative study. Our attribution explanations originally
highlighted about 6 words per ideation and users found it too tedious to track
and manage all of them. Remedy: we limited the highlights to 3 words with the
most negative attributions. Interviewees were confused with many
counterfactual words, since they had negative or low improvement scores or
were not semantically relevant (e.g., replacing ‘play’ with suggested word
‘kids’ with potential +0% for Diversity and +0% for Motivating). Remedy: we
ensured that suggested words have at least one positive score, and limited to
relevant suggestions by eliminating words that had embedding distances too far
from words ‘exercise” and ‘physical activity”. Participants had found the
compare mode (to show contrastive attributions) useful, but tended to forget
to switch over to it. Remedy: we set contrastive explanations as default for
each iteration if this explanation was available. The default can be gradually
reset after users acclimatize to remembering this feature. We implemented
these improvements in the feedback UI, and launched a larger summative
controlled study to measure the impact of explanations on ideation quality and
diversity.

Table 2. Dependent variables (DV) measured in the summative ideation user study. Measure |  Of |  Description and Justification
---|---|---
Ideation task time |  Iteration |  Duration to ideate at each iteration. Objectively measures ideation fluency (Barbot, 2018) or ease of ideation (Cox et al., 2021).
Perceived Importance |  Quality score,
Diversity score |  Ideators’ preference of perceived importance for message quality or diversity [7-point Likert scale: –3 = “Motivating much more important”, +3 = “Diverse much more important”].
Perceived
Ideation Quality |  Ideation
(Final iteration) |  Ideators’ self-assessment of how motivating and creative the ideation is
[7-point Likert scale: –3 = Strongly Disagree, +3 = Strongly Agree].
Understanding, Ease of Use, Helpfulness |  Prompt,
Feedback Feature |  Ideators’ perceptions asked separately of the Prompt and each Feedback Feature (S of Quality and Diversity, A, X, C) [5-point Likert scale: –2 = Strongly Disagree, +2 = Strongly Agree] and rationale [Open text].
Usage |  Prompt,
Feedback |  Qualitative description of how the feedback information was used [Open text].
This was only asked of the 2nd ideation for each interface section.
Computational
Diversity |  Ideation |  Metrics of diversity based on ideation embeddings (Cox et al., 2021) [Ideation Dispersion (MST Mean of Edge Weights), Ideation Disparity (Mean Pairwise Distance), and Repeller Chamfer Distance (Mean Min Pairwise Distance)].

###  5.2. Summative Ideation User Study

We conducted a mixed-design experiment with main independent variable (IV),
Feedback Condition (N, S, SA, SAX, SAC, SAXC) and secondary IV, Iteration
(t=1,2,3). Iteration was fully within-subjects, while Feedback Condition was
partially within-subjects. For each ideation, participants saw a Directed
prompt from a pre-selected set of 50 (Section 4.3) (Cox et al., 2021). All
Feedback Conditions had the same 50 prompts. We exposed each participant to 2
randomly-selected Feedback Conditions with 2 prompts per type and 3 iterations
per prompt (total 12 ideation iterations) to mitigate individual variance,
while limiting fatigue with too many trials. We limited the prompt to contain
only one phrase, since users could struggle to utilize multiple phrases (Cox
et al., 2021). While the system could show explanations for both Quality and
Diversity, we showed Diversity explanations by default to prioritize improving
diversity which users may find less intuitive (Section 5.1.2). Table 2
describes dependent variables measured. The experiment apparatus and survey
questions were implemented in Qualtrics (see Appendix A.2.1).

####  5.2.1. Experiment Task and Procedure

Participants were tasked to write motivational messages towards physical
activity using various feedback with the following procedure:

  1. 1.

Introduction to experiment objective and consent to the study.

  2. 2.

Screening quiz with a 4-item word association test (Chandler et al., 2019) to
assess English language skills. All answers must be correct to continue.

  3. 3.

UI section ($\times 2$) with different Feedback Conditions.

    1. a)

Tutorial about ideation process, and how to use the feedback.

    2. b)

Ideation session ($\times 2$) to ideate based on a prompt each.

      1. 1.

Prompted ideation to view a prompt, write an initial ideation in one to three
sentences, and submit for automatic review. This page is timed to measure
ideation task time.

      2. 2.

Iterated revision ($\times 2$) to receive feedback and revise.

        1. 1.

Prompted ideation to view the same prompt again.

        2. 2.

Ideation feedback to inform the participant where and how to improve their
previous ideation.

        3. 3.

Ideation revision and submission. With these two revisions, there are three
ideation iterations.

      3. 3.

Perception questionnaire to ask participants about their perceptions of usage
and usability (Table 2).

    3. 4.

Post-questionnaire on demographics.

To control fatigue, we limited to 4 ideation trials. Instead of 4 ideations
$\times$ 1 condition (no repeated trials, potentially noisy), or 1 ideation
$\times$ 4 conditions (fully between-subjects, high individual variance), we
balanced with 2 ideations $\times$ 2 conditions.

####  5.2.2. Experiment Data Collection

We recruited participants from Amazon Mechanical Turk (AMT) with high
qualification ($\geq$5000 completed HITs, ¿97% approval). 104 workers
attempted the survey, 97 passed the screening quiz, and 70 completed the
survey. They were 42.0% female, between 23 and 66 years old (M=39.5).
Participants were compensated US$4.00 after completing the ideation tasks and
surveys. Participants completed the survey in median time 26.0 minutes and
were compensated US$9.24/hour. We collected 4 ideations per participant (2
Feedback Conditions x 2 Ideations) with up to 3 Iterations each, from 70
participants for a total of 280 ideations across the 6 Feedback Conditions.

####  5.2.3. Statistical Analysis

We fit linear mixed effects regression (LMER) models on various dependent
variables, performed ANOVAs on the fixed main and interaction effects, and
post-hoc contrast tests for specific differences identified. Since LMER models
can accommodate missing data by imputing and estimating values, we can analyze
our experiment with partially within-subjects independent variables. Due to
the large number of comparisons in our analysis, we consider differences with
p¡.001 as significant and p¡.005 as marginally significant. This is stricter
than a Bonferroni correction for 50 comparisons (significance level = .05/50).

For perceived ratings (Usefulness, Ease of Use, Understandability), we fit a
LMER model with Feedback Feature (Prompt, Diversity Score, Quality Score,
Attribution, Contrastive, Counterfactual) as fixed effect, and Participant as
random effect (Table 6 in Appendix A.3). Note that the features were
considered separately rather than as a combination in the Feedback Interface.

Figure 9. Results of ideators’ perceived ratings for each feedback feature.
Feedback Features: Pr = Prompt, S(D) = Diversity Score, S(Q) = Quality Score,
A = Attribution explanation, C = Counterfactual suggestion, X = Contrastive
attribution. Red markers indicate significant differences from Pr. p values
indicate contrast tests relative to Pr. * indicates p¡.0001.

Three sub-figures of interval plot of user ratings for each feedback feature
regarding usefulness, ease to understand, and ease to use.

Figure 10. Results of ideator performance. a) Time effort ratio between
Iteration 1 and 3. b) Increase in Quality Score from Iteration 1 to the
iteration with best Quality Score. c) Increase in Diversity Score from
Iteration 1 to the iteration with best Diversity Score. Feedback Conditions: N
= None, S = Score Prediction, SA = Score + Attribution, SAC = SA +
Counterfactual, SAX = SA + Contrastive, SAXC = SA + X + C. p values indicate
1-sample, 1-tail t-tests relative to N (a) or 0% (b, c); * indicates p¡.0001.

Three sub-figures of interval plot for each feedback condition regarding
ideation time, the increase of ideation quality score, and the increase of
ideation diversity score.

Figure 11. Ideation Diversity calculated with different computational metrics:
a,b) diversity of new and prior ideations, and c) new ideations from prior
ones. p values indicate contrast tests relative to N. * indicates
significantly different from N at p¡.0001.

Three sub-figures of interval plot for each feedback condition regarding three
computational metrics of Ideation Diversity, including Ideation Dispersion,
Ideation Disparity, and Repeller Chamfer Distance.

For ideation performance, we examined ideation speed, and whether Diversity
and Quality Scores increased. We fit a LMER model with Feedback Condition and
Prompt (which one of the 50 prompts was shown) as fixed main effects, and
Participant as random effect (Table 7 in Appendix A.3). We evaluated whether
ideation took longer with Score and XAI feedback compared to the baseline 1st
iteration, so we calculated the ratio between the 1st and 3rd iteration with a
log transform (to improve normality) to get the Ideation Time Effort metric.
Note that the best Score may be achieved at the 2nd or 3rd iteration, i.e.,
the participant may inadvertently get a lower score on the 3rd attempt. Hence,
we calculated the difference between the best Score of the latter two
iterations with the first, and did so separately for Diversity and Quality.
There was a strong negative correlation (r = –.436, p¡.0001) between the
Diversity and Quality Scores, so we included Quality Score Increase as a fixed
main effect too for the Best Diversity Score Increase response, and vice
versa. We also tested the Score Increase estimates from the LMER model against
no increase with a 1-sample, 1-tail t-test for each Feedback Condition against
the constant 0.

To analyze objective diversity for each Feedback Condition, we resampled 50
bootstrap samples, calculated three computational diversity metrics defined in
(Cox et al., 2021) (Ideation Dispersion, Ideation Disparity, and Repeller
Chamfer Distance), and fit a LMER model with Feedback Condition as fixed main
effect. In Appendix A.3, Table 6, Table 7, and Table 8 summarize the goodness
of fit (R2) of the LMER models, and statistical significance of ANOVA tests.

####  5.2.4. Results

We describe participant priorities and perceptions of their ideations and the
feedback, and their performance as calculated with metrics. When ideating,
participants prioritized improving their Quality Score (56.5%, rating ¡ 0)
instead of Diversity Score (12.6%, rating ¿ 0), and 31% balanced between the
two. They perceived their ideations as Motivating (high quality, 81.3% with
rating ¿ 0) and Creative (73.8%), though there was no significant difference
across Feedback Conditions. Participants rated all features as useful, easy to
understand and use, except for Counterfactual suggestions which had lower
usefulness and ease of use (Figure 9).

As expected, participants took more time to ideate when receiving Score (S)
and XAI feedback, and significantly so for SAC and SAXC compared to None
(Figure 10a); suggesting that they were studying these feedback. S feedback
increases were non-significant or marginal compared to no feedback (None).
However, compared to no improvement (Increase = 0), we found that showing S or
all explanations significantly improved Quality Score (Figure 10b), and
showing SA, SAC, and SAXC significantly improved Diversity Score (Figure 10c).
S did not improve Diversity Score, perhaps since ideators were blind to what
they could change or do without explanations, and would end up randomly
editing. Explanations did not improve Quality compared to just showing Scores
(p=n.s.), and only very marginally improved Diversity (p=.0414 for SAXC). The
lack of differences could be due to the high variability in ideations and
limited number of participants; future work could recruit more than 50
participants per condition.

The three computational diversity metrics (Cox et al., 2021) — Ideation
Dispersion, Ideation Disparity, and Repeller Chamfer Distance — measure
slightly different aspects of diversity, and had different trends (Figure 11).
The first two metrics calculate the total diversity of combining new ideations
with the seed prior ideations, and the third metric measures how different the
new ideations are from the prior ones. In general, SAX had the highest
diversity. Other results were somewhat indeterminate. SAC had the lowest
Ideation Dispersion, but higher Repeller Chamfer Distance; this suggests that
Counterfactuals may help diversify ideations from those without any feedback
(N), but may not diversify ideations from other forms of feedback. The
detrimental effect of Counterfactual suggestions may also hinder diversity
when combined with Contrastive explanations (SAXC; Figure 11b,c).

We qualitatively analyzed ideator rationales to interpret their perceived
usefulness, understanding, and usage of various ideation feedback. Many
findings echo our earlier ones from the formative study. Participants were
mixed regarding whether Score feedback was helpful. P36 “found the feedback
easy to understand because it gave me a number to see how well my [message]
is.”; but P13 “found the feedback difficult to understand because I have no
idea how it’s calculated. I can’t tell what part of my idea it’s targeting so
I have no idea how to improve. It feels random.”; P46 was more positive,
finding them “somewhat helpful, but did not give guidance on how to improve my
scores”. This validates the need for deeper explanations.

However, the receptivity towards Attribution feedback was also mixed. P41
reported that “feedback was good, preventing me from overusing common
phrases”, and P43 “did like the highlights to look for different wording that
is not so generic”. The highlights were more useful when used in contrast mode
to show Contrastive attributions, e.g. “The compare mode definitely showed me
which words I should keep and which ones I should edit, to get the optimal
score.” [P68]. In contrast, P64 wanted more useful explanations and found that
attribution “colors and words are not as helpful as they really are
ambivalent.” P63 felt “it was often not clear why a certain word would be less
valuable than another or how it would affect the scores, especially
diversity.” These cases indicate highlighting salient words and rating their
attribution lacks rationalization insights (Ehsan et al., 2018).

Perceptions towards Counterfactual suggestions were more negative, though
sometimes positive. P22 found ideating “easier now that there were suggested
words”, and P07 said it “helped me to add words to increase inspiration.”
However, others found the suggested words problematic: P70 felt that
“sometimes the suggested words seemed completely unrelated to the prompt or
even fitness itself.”; P28 felt that “the suggestions make the phrase a lot
worse, non-grammatically correct”; P32 “found the feedback a little confusing
because it seemed like when I changed words to try to increase my score it
ended up keeping it close to the [previous] score.” Hence, Counterfactual
suggestions should be relevant, grammatical, and efficacious.

Providing all explanations together enabled rich usage. P22 “changed words
based on suggested words, excised words that complicated the message
needlessly, and used the comparison checker to see if there was a word that
particularly hurt between each prompt.” Therefore, it is important to provide
these explanations together.

Table 3. Dependent variables (DV) measured in the ideation quality and diversity validation user studies. Measure |  Of |  Description and Justification
---|---|---
Individual Quality
(Motivating/Informative/Helpful) |  Ideation |  Rating of how the message feels [7-point Likert scale: –3 = Very Demotivating/Uninformative/Unhelpful, +3 = Very Motivating/Informative/Helpful].
Pairwise Dissimilarity |  Ideation-Pair |  Rating of perceived difference between 2 ideations [20-interval slider: 0 = Identical, 100 = Highly different].

Figure 12. Example trial in Ideation Diversity Validation User Study for
participants to examine a pair of ideations (Left), rationalize and rate their
dissimilarity (Middle), while referring to three reference pairs with varying
dissimilarities.

Screenshots of the survey in Ideation Diversity Validation User Study.

###  5.3. Ideation Quality Validation User Study

To evaluate ideation quality, we conducted a within-subjects experiment with
Feedback Condition as independent variable. Similar to (Cox et al., 2021;
Kocielnik and Hsieh, 2017), we measured quality with three 7-point Likert
scale questions on whether an individual ideation was Motivating, Informative,
or Helpful (Table 3). The ideations evaluated included the third iteration of
all ideations for each Feedback Conditions from the Summative Ideation User
Study (N=43 to 49; 278 total).

####  5.3.1. Experiment Task and Procedure

Participants were tasked to read motivational messages towards physical
activity, and rate their quality (Appendix A.2.2). Participants went through
the same procedure as in the Ideation User Study, but with a different Step 3:

  1. 3.

Assess 25 messages regarding how well they motivate for physical activity. For
each message,

    1. a.

Read a random message from one of the 6 Feedback Conditions (N, S, SA, SAC,
SAX, SAXC).

    2. b.

Rate on a 7-point Likert scale, whether the message is motivating (effective),
informative, and helpful.

    3. c.

Write their rationale for their quality ratings. This was only asked randomly
two times to mitigate response fatigue with texts that are too short or copied
from previous responses.

####  5.3.2. Experiment Data Collection

We recruited participants from AMT with the same high qualification as the
ideation study. 71 workers attempted the survey, 60 passed the screening task
and complete the survey (84.5% pass rate). They were 41.7% female, between 25
and 61 years old (M=36.0). Participants completed the survey in median time
12.1 minutes and were compensated US$1.50. In total, 278 messages were rated
1500 times (M=5.40x/message); 2 omitted by random chance. Average aggregate-
judge correlations (Chan et al., 2017) for motivation, informativeness,
helpfulness ratings were r=.472, .467, .469, respectively, indicating
reasonable inter-rater agreement.

####  5.3.3. Statistical Analysis and Results

We binarized ratings to evaluate as continuous variables and fit LMER models
on three quality ratings like in the Ideation User Study with Feedback
Condition as fixed effect, and Participant and Ideation as random effects
(Table 9 in Appendix A.3). Ideations were rated as Motivating (M = 72.6%),
Informative (M = 70.1%), and Helpful (M = 75.5%), but there were no
significant differences across Feedback Conditions (all p=n.s.).

###  5.4. Ideation Diversity Validation User Study

The ideation study measured diversity with computational metrics, but people
may be less sensitive to perceiving diversity, so it is necessary to evaluate
with a user study. Here, we ask a new set of third-party participants to
validate ideation diversity from each Feedback Condition. We conducted a
mixed-design experiment with Feedback Condition (4 levels: N, S, SAX, SAXC) as
independent variable. We omitted SA and SAC, which did not improve
computational diversity (Figure 11) and were thus unlikely to increase
perceived diversity. Similar to (Cox et al., 2021; Dow et al., 2010;
Siangliulue et al., 2015), participants rated the perceived dissimilarity
between a pair of ideations (Table 3). From the ideation study, one collection
of up to 100 ideations was obtained for each Condition consisting of 50 prior
messages (from (Cox et al., 2021)) and up to 50 new ideations (3rd iteration)
from participants. Similar to (Cox et al., 2021), we randomly sampled 200
ideation pairs per condition; each pair could contain 2 prior, 2 new, or 1+1
mixed ideations.

####  5.4.1. Experiment Task and Procedure

Participants were tasked to review pairs of motivational messages, and rate
their dissimilarity (Appendix A.2.3). The experiment procedure is as follows:

  1. 1.

Introduction to experiment objective and consent to the study.

  2. 2.

Screening quiz which needs all correct answers with

    1. a)

4-item word association test (Chandler et al., 2019) of English language
skill,

    2. b)

Similarity judgment test on identifying the most similar ideation pair from 3
pairs (set to be easy).

  3. 3.

Practice session with three pair trials. For each trial:

    1. a)

Read two ideation messages.

    2. b)

Write key concepts for each message as attention check.

    3. c)

Rationalize the pair dissimilarity by multiple categories (identified from a
pilot study with 660 rationales from 110 participants). This is to prime
rigorous study of the messages and mitigate mindless rushing. We designed the
question as a matrix of checkboxes (Figure 12, Middle Top) instead of free
text, to reduce response fatigue.

    4. d)

Rate perceived dissimilarity on a 20-interval slider from 0 to 100 (Figure 12,
Middle Bottom). We used a high-resolution slider instead of a Likert scale for
more precise measurement.

    5. e)

View the “correct” dissimilarity rating of 3 practice trials with ratings 1,
5, 9. The rating values were averages from pilot study participants, which we
use to calibrate participants towards more consistent ratings.

  4. 4.

Main session with 28 message pairs (7 pairs per Feedback Condition, 4 Feedback
Conditions). Same procedure as the Practice session, except for omitting Step
3e, and for Step 3d:

    1. d)

Rate perceived dissimilarity with the same interface as Step 3d and reference
of the 3 practice pairs with ”correct” ratings to anchor participants (Figure
12, Right).

  5. 5.

Post-questionnaire on demographics.

####  5.4.2. Experiment Data Collection

We recruited participants from AMT with the same high qualification as the
ideation study. 162 workers attempted the survey and 125 passed screening and
completed the survey (77.2% pass rate). They were 41.6% female, between 20 and
67 years old (M=34.0). Participants completed the survey in median time 47.6
minutes and were compensated US$6.00. In total, 800 message pairs were rated
3,500 times (M=4.38x / pair). The average aggregate-judge correlation (Chan et
al., 2017) was r=.478 for perceived dissimilarity, indicating reasonable
inter-rater agreement.

Figure 13. Results of validator perceived dissimilarity between ideation pairs
from different Feedback Conditions. p values indicate difference from N. *
indicates p¡.0001

Interval plot of the validator perceived dissimilarity between ideation pairs
from different Feedback Conditions.

Table 4. Summary of results of explanation effects. Grey 0 indicates baseline or not significantly different from baseline (N or Pr.), Grey arrow indicates marginally significant from N, black arrow indicates significant at p¡.0001. |  |  |  | Feedback Interface
---|---|---|---|---
Effect | Measure | User Study | Evidence | N | S | SA | SAC | SAX | SAXC
Ideation Quality | Quality Score Increase | Summative Ideation |  Fig. 10b | 0 | $\uparrow$ | $\uparrow$ | $\uparrow$ | $\uparrow$ | $\uparrow$
| Motivating/Informative/Helpful ratings | Quality Validation | - | 0 | 0 | 0 | 0 | 0 | 0
Ideation Diversity | Diversity Score Increase | Summative Ideation |  Fig. 10c | 0 | 0 | $\uparrow$ | $\uparrow$ | 0 | $\uparrow$
| Computational Ideation Dispersion |  |  Fig. 11a | 0 | 0 | 0 | $\downarrow$ | $\uparrow$ | $\uparrow$
| Computational Ideation Disparity |  |  Fig. 11b | 0 | $\uparrow$ | $\uparrow$ | 0 | $\uparrow$ | $\uparrow$
| Computational Repeller Chamfer Distance |  |  Fig. 11c | 0 | 0 | 0 | $\uparrow$ | $\uparrow$ | $\uparrow$
| Validator pairwise dissimilarity rating | Diversity Validation |  Fig. 13 | 0 | 0 | - | $\uparrow$ | - | $\uparrow$
Ideation Effort | Log(Task Time 1 / Task Time 3) | Summative Ideation |  Fig. 10a | 0 | 0 | 0 | $\uparrow$ | 0 | $\uparrow$
|  |  |  | Feedback Feature
Effect | Measure | User Study | Evidence | Pr. | S(D) | S(Q) | A | C | X
Ideation Effort | Usefulness rating | Summative Ideation |  Fig. 9a | 0 | 0 | $\uparrow$ | $\uparrow$ | 0 | 0
| Ease of Understanding rating |  |  Fig. 9b | 0 | 0 | 0 | 0 | 0 | 0
| Ease of Use rating |  |  Fig. 9c | 0 | 0 | 0 | 0 | 0 | 0

####  5.4.3. Statistical Analysis and Results

Similar to our analysis in the summative ideation study, we fit a LMER model
on perceived dissimilarity with Feedback Condition, Trial Index (in survey), #
Rationales Considered (indicating thoughtfulness) as fixed effects, and
Participant as random effect (Appendix A.3 Table 10). Since all Conditions
contain pairs with only prior messages, we excluded these pairs since they
will have identical dissimilarity across Conditions.

Ideation message pairs from the SAX and SAXC conditions had higher perceived
pairwise dissimilarity than pairs from N (Figure 13). Pairs from the S
condition only had marginally higher ratings than N. This mostly agrees with
our results of the computational diversity metrics (Figure 11). Therefore,
Attribution and Contrastive Attribution improved diversity, while
Counterfactual Suggestion had limited effect.

###  5.5. Summary of Evaluation Results

We summarize our findings from the multiple experiments here and in Table 4.
1) From the formative ideation study, participants wanted some feedback on
their ideations, appreciated all XAI types in feedback more than just showing
Scores, had a tendency to prioritize either for Quality or Diversity, might
have fixated on prompts or suggestions too much, and sometimes struggled with
problematic Counterfactual Suggestions. 2) From the summative ideation study,
all feedback types were rated equally understandable and useful, though Score
and Attribution feedback were rated more useful than showing Prompts only. 3)
From the summative ideation and both validation studies, a) Quality Score
increased with all feedback types, but this was not perceptible with third-
party validators; b) Diversity Score, computed diversity, and validator
perceived pairwise dissimilarity were increased for feedback with combined
explanations (SAX, SAXC). Therefore, Score, Attribute and Contrastive
explanations were especially useful. Counterfactual Suggestions were somewhat
detrimental, but this was not significant.

##  6\. Discussion

We discuss the value of ideation XAI feedback, future improvements,
generalization, and implications to human-AI collaboration.

###  6.1. Effectiveness of XAI Feedback for Ideation

We have shown that XAI provides helpful feedback to improve collective
creativity in ideation, and multiple explanations should be combined to
amplify insights for ideators. Towards improving diversity, SAX feedback was
most effective, followed by SAXC which was similarly effective. Counterfactual
suggestions were promising but harmful; we discuss improvements later.
However, Score feedback alone was very marginally useful and explanations were
less effective at improving quality, and we discuss improvements later.

Despite providing helpful explanations, some ideators still struggled to apply
feedback for ideation. We had suggested the need for semantic explanations
rather than correlated attributions (Section 5.1.2). To further help, we could
leverage strategies in education: providing examples and self-explanation. We
could improve the tutorials and provide worked examples (Atkinson et al.,
2000) of past ideators deriving new ideations from each explanation feature,
particularly including their rationale. Ideators could reflect more rigorously
on the feedback by self-explaining (Vanlehn et al., 2016) what they learned
from the feedback and how they could or could not apply it to edit their
ideation.

####  6.1.1. Improving XAI Feedback for Ideation Quality

We had mixed results regarding the impact of Score and XAI feedback on
Quality. Feedback did increase the Quality Score in the ideation study, but
not the perceived quality in the validation study, perhaps due to several
reasons. 1) Ideators were confounded with a conflicting goal to increase
Diversity. Diversity and Quality Scores were highly negatively correlated, and
including either as a factor in the LMER models led to significant effects.
Nevertheless, we focused on increasing Diversity while maintaining Quality,
rather than mainly increasing Quality. For applications prioritizing Quality,
ideators could be told to only improve Quality, but at the cost of more
redundancy. 2) Ideators may have felt self-sufficient and not have relied much
on XAI to improve Quality. 3) The Quality prediction model may need to be more
accurate to represent and explain Quality, perhaps with a regression model
instead of binary classifier. 4) Although we had used the Universal Sentence
Encoder (USE) (Cer et al., 2018) that models whole sentences rather than just
words, explanations were framed with individual words. This caused ideators to
fixate on each word separately rather than longer phrases or gestalt concepts.
Presenting the explanation as a knowledge graph, highlighting multiple
associated words, or inferring and presenting associated concepts (Zhang and
Lim, 2022) may mitigate this fixation. Also, instead of using generic language
models, the modeling could be domain-specific to motivation and physical
activity and include knowledge bases (e.g., (Bosselut et al., 2020; Petroni et
al., 2020)).

####  6.1.2. Providing More Useful Counterfactual Suggestions

Counterfactual explanations need to be more usable by improving the relevance
and grammatical context of suggested words. The grammar was due to lemmatizing
attribution words to their root and finding related words in ConceptNet. The
suggested words should be converted to fit the grammatical form of the word
being replaced.

The irrelevance of suggested words could be due to ConceptNet using domain-
independent data sources (e.g., Wikipedia, Wiktionary, WordNet) (Speer et al.,
2017). Words are thus too distant from the domain of physical activity. At the
potential cost of stifling creativity by reducing too many suggestions, we had
limited the embedding distances of the words (remedy in Section 5.1.2), yet
the remaining words were still insufficiently relevant. Future work should
balance irrelevance and inspiration by carefully controlling the distance
threshold and training ideators to be more comfortable with less relevant
words. We can also leverage recent advances in synthetic data generation and
use generative adversarial networks (GAN) to synthesize counterfactual
sentences that increase quality and diversity (e.g., (Xu et al., 2018)).
However, these sentences are likely to be ungrammatical or strange, needing
crowdworkers to clean up.

Some participants had deviated from the prompt phrase, reducing the precision
of the directing. To mitigate this, we can limit the distances of suggested
words from the prompts. Reminders can also be sent if the new ideations are
measured to be too distant from the prompts by embedding distance.
Nevertheless, some deviation can accommodate inaccuracies in the embedding
distance calculations.

###  6.2. Generalization to Other Applications, Domains, and Explanations

By bridging the gap between the education-based feedback (Hattie and
Timperley, 2007) and philosophy-driven AI explanations (Miller, 2019), we
proposed and evaluated an explainable AI-based ideation feedback system to
improve quality and diversity in iterative crowd ideation. Our evaluation on
one use case of motivational messaging for physical activity may limit the
generalization of our results. We had focused on a low-skill task of everyday
writing where lay users typically do not need sophisticated reasoning. In
domains requiring expertise, ideators may further appreciate complex
explanations and may even better leverage simpler explanations (e.g., plain
attribution). For tasks that are non-text-based, such as poster design (Dow et
al., 2010), attribution may be ambiguous and counterfactuals more complex to
develop or describe. Therefore, explanations will need to be more carefully
designed for more complex ideation tasks.

Our approach can be applied to other applications, such as story writing and
graphic design. The short length of motivational messages makes computing on
and reviewing them tractable, but processing longer documents like short
stories (Clark et al., 2018) will require larger language models (Devlin et
al., 2018). The long documents could also be divided to be iterated one part
at a time (Bernstein et al., 2010), or summarized automatically (Gambhir and
Gupta, 2017) before predicting a score and providing simplified feedback. For
graphical ideation tasks like graphic design (Oppenlaender et al., 2020) or
mood boards (Koch et al., 2020), the design artifact can be parsed as an
image, scored with a prediction from a Convolutional Neural Network, and
explained with saliency maps (Selvaraju et al., 2020) to identify problematic
regions. For audio ideation tasks like music creation (Louie et al., 2020),
Recurrent Neural Networks can be trained and explained with attention
mechanisms (Vaswani et al., 2017).

Interpretable Directed Diversity supports three popular explanation types, and
future work can study others to stimulate creativity. For example, feature
visualizations (Olah et al., 2017) provide a “vocabulary” of filters to
interpret image predictions. For text, this could indicate concepts that
ideators could reflect on to generate high scoring ideations; though these
latent concepts may be uninterpretable. Concept Activation Vectors (CAVs) (Kim
et al., 2018) provide explanations with user-chosen concepts. Users could use
CAVs like in SMILEY (Cai et al., 2019) to discover ideations that are more
similar to their desired concept.

###  6.3. Human-AI Collaboration for Creativity

Among the myriad techniques to support crowd ideation with human-guided
feedback (Dow et al., 2012; Duque-Estrada, 2014; Oppenlaender et al., 2020),
and automatic feedback (Bae et al., 2020; Clark et al., 2018), we add yet
another technique to improve automation and scale. Similar to (Louie et al.,
2020), our method directs or steers crowdworkers towards more creative ideas.
Can human facilitators then leave the management to AI? No, we do not argue
that crowd and feedback management be handled fully automatically. Instead,
data management should be collaborative between the human facilitator and AI.
Particularly, there is a need for better curation of source data for prompts
and counterfactual suggestions (e.g., regularly update corpus (Cox et al.,
2021)). Human support is especially needed for the early phase, since there
would be insufficient ideations to train an AI model to predict scores. This
presents a causality dilemma that (ideation) labeling needs pre-collected,
pre-labeled data for training, yet crowdsourcing is needed to label the
initial data.

##  7\. Conclusion

We have proposed Interpretable Directed Diversity to provide feedback to
direct crowd ideators with explainable AI (XAI) feedback to iteratively
generate more collectively creative ideas. We implemented and evaluated
automatic scoring models and explanation techniques for Attributions,
Contrastive Attributions, and Counterfactual Suggestions to improve ideation
diversity and quality. Through a series of formative ideation study, summative
ideation user study, and validation user studies, with computational language
modeling embedding-based metrics and subjective user ratings, we found
significantly positive effects of the proposed XAI types on increasing
collective ideation diversity, except the Counterfactual Suggestions that
still require improvements. Our results demonstrate the use of XAI for
creativity applications. Hence, Interpretable Directed Diversity provides a
generalizable method for scalable, real-time, and contextualized feedback to
improve collective creativity.

###### Acknowledgements.

This work was carried out in part at NUS Institute for Health Innovation and
Technology (iHealthtech) and with funding support from the NUS ODPRT and
Ministry of Education, Singapore.

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##  Appendix A Appendix

###  A.1. Example training data for Quality (Motivation) machine learning
model

Table 5. Example messages from (Cox et al., 2021) with varying motivation ratings. Message | Motivation
Rating (–3 to 3) | Highly
Motivating?
---|---|---
Walking outside in nature can rejuvenate your mind while toning your body at the same time! | 2.60 | High
Push yourself, don’t wreck yourself. Set some steady goals, and work your way up! | 2.00 | High
Even small amounts of exercise can help you become healthy. You don’t need to dedicate 2 hours a day to become healthier! | 1.75 | High
Your dog would love to go on a walk with you each day! | 1.00 | Low
Switch off an air conditioner while working out. Let the sweat out, and burn some calories. | 0.00 | Low
Athletes are healthy since they train every day. You will feel much healthier if you exercise too! | –0.17 | Low

###  A.2. Questionnaire Interfaces for User Studies

####  A.2.1. Summative Ideation User Study

Screenshots for survey in the Feedback Condition SAXC, containing all
explanation types. Other conditions will have fewer instructions and
questions.

Figure 14. Introduction.

Screenshot of the Task Description in Summative Ideation User Study.

Figure 15. Screening questions on word association to assess English language
skills.

Screenshot of the screening questions on word association to assess English
language skills in Summative Ideation User Study.

Figure 16. Tutorial on Scores, Attribution and Counterfactual explanations.

Screenshot of the tutorial on Scores, Attribution and Counterfactual
explanations in Summative Ideation User Study.

Figure 17. The tutorial of introducing the Contrastive explanations (i.e.,
color highlights and attribution to score changes) between attempts and
Counterfactual explanations (i.e., suggested related words and potential
contribution scores).

Screenshot of the tutorial on Contrastive explanations between attempts and
Counterfactual explanations in Summative Ideation User Study.

Figure 18. Screening Quiz.

Screenshot (part 1) of the screening quiz after the tutorial in Summative
Ideation User Study.

Figure 18. Screening Quiz. (continued)

Screenshot (part 2) of the screening quiz after the tutorial in Summative
Ideation User Study.

Figure 18. Screening Quiz. (continued)

Screenshot (part 3) of the screening quiz after the tutorial in Summative
Ideation User Study.

Figure 19. Questions to rate priority on Motivation or Creativity.

Screenshot of questions to rate priority on Motivation or Creativity after the
ideation session in Summative Ideation User Study.

Figure 20. Questions on perceived helpfulness, ease of understanding, and ease
of use regarding Feedback Features. Ellipses (…) notation indicate the same
matrix of radio buttons, used for brevity.

Screenshot of questions to rate perceived helpfulness, ease of understanding,
and ease of use regarding Feedback Features after the ideation session in
Summative Ideation User Study.

####  A.2.2. Ideation Quality Validation User Study

Figure 21. Introduction.

Screenshot of Task Description in Ideation Quality Validation User Study.

Figure 22. Questions to rate qualities of Motivatingness, Informativeness, and
Helpfulness. Rationale was collected as attention checks.

Screenshot of questions to rate qualities of Motivatingness, Informativeness,
and Helpfulness in Ideation Quality Validation User Study.

####  A.2.3. Ideation Diversity Validation User Study

Figure 23. Introduction.

Screenshot of Task Description in Ideation Diversity Validation User Study.

Figure 24. Screening quiz questions on message pair dissimilarity perception.

Screenshot of screening quiz questions on message pair dissimilarity
perception in Ideation Quality Validation User Study.

###  A.3. Linear Mixed Models and Statistical Analysis Results

Table 6. Statistical analysis for Ideation User Study of perceived ratings due to Feedback Feature as fixed effect and Participant as random effect in linear mixed effects models. n.s. means not significant at $p>.05$. $p>F$ is the significance level of the fixed effect ANOVA. $R^{2}$ is the model’s coefficient of determination to indicate goodness of fit. Response |  Linear Effects Model (Participant as random effect) | $p>F$ | $R^{2}$
---|---|---|---
Usefulness | Feedback Feature | ¡.0001 | .487
Ease of use | Feedback Feature | n.s. | .480
Understandability | Feedback Feature | ¡.0001 | .471
Table 7. Statistical analysis for Ideation User Study of responses due to Feedback Condition, Prompt, and Score Increases as fixed effects and Participant as random effect in linear mixed effects models. $p>F$ is the significance level of the fixed effect ANOVA. $R^{2}$ is the model’s coefficient of determination to indicate goodness of fit. Response |  Linear Effects Model (Participant as random effect) | $p>F$ | $R^{2}$
---|---|---|---
Ideation Time Effort | Feedback Condition + | .0117 | .586
| Prompt | n.s. |
Quality Score Increase | Feedback Condition + | .0361 | .384
| Prompt + | n.s. |
| Diversity Score Increase | ¡.0001 |
Diversity Score Increase | Feedback Condition + | .0361 | .388
| Prompt + | n.s. |
| Quality Score Increase | ¡.0001 |
Table 8. Statistical analysis for Ideation User Study of computational diversity metrics due to Feedback Condition as fixed effect and Participant as random effect in linear mixed effects models. $p>F$ is the significance level of the fixed effect ANOVA. $R^{2}$ is the model’s coefficient of determination to indicate goodness of fit. Response |  Linear Effects Model (Participant as random effect) | $p>F$ | $R^{2}$
---|---|---|---
Ideation Dispersion (MST Mean of Edge Weights) | Feedback Condition | ¡.0001 | .405
Ideation Disparity (Mean Pairwise Distance) | Feedback Condition | ¡.0001 | .360
Table 9. Statistical analysis for Ideation Quality Validation User study of responses due to Feedback Condition as fixed effects and Participant and Ideation as random effects in linear mixed effects models. $p>F$ is the significance level of the fixed effect ANOVA. $R^{2}$ is the model’s coefficient of determination to indicate goodness of fit. Response |  Linear Effects Model (Participant, Ideation as random effects) | $p>F$ | $R^{2}$
---|---|---|---
Motivatingness Rating | Feedback Condition | n.s. | .292
Informativeness Rating | Feedback Condition | n.s. | .298
Helpfulness Rating | Feedback Condition | n.s. | .324
Table 10. Statistical analysis for Ideation Diversity Validation User study of responses due to Feedback Condition, Trial Index (sequence number of pair trial), # Rationales Considered (how thoughtful participant was) and an interaction as fixed effects, and Participant as random effect in a linear mixed effects model. $p>F$ is the significance level of the fixed effect ANOVA. $R^{2}$ is the model’s coefficient of determination to indicate goodness of fit. Response |  Linear Effects Model (Participant as random effect) | $p>F$ | $R^{2}$
---|---|---|---
Pairwise Dissimilarity Rating | Feedback Condition + | ¡.0001 | .371
| Trial Index + | .0128 |
| # Rationales Considered + | n.s. |
| # Rationales Considered × Feedback Condition | n.s. |