EUCA dataset description

Associated Paper: EUCA: the End-User-Centered Explainable AI Framework

Authors: Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Ghassan Hamarneh

Introduction:

EUCA dataset is for modelling personalized or interactive explainable AI. It contains 309 data points of 32 end-users' preferences on 12 forms of explanation (including feature-, example-, and rule-based explanations). The data were collected from a user study on 32 layperson participants in the Greater Vancouver city area in 2019-2020. In the user study, the participants (P01-P32) were presented with AI-assisted critical tasks on house price prediction, health status prediction, purchasing a self-driving car, and studying for a biological exam [1]. Within each task and for its given explanation goal [2], the participants selected and rank the explanatory forms [3] that they saw the most suitable.

1 EUCA_EndUserXAI_ExplanatoryFormRanking.csv

Column description:

• Index - Participants' number
• Case - task-explanation goal combination
• accept to use AI? trust it? - Participants response to whether they will use AI given the task and explanation goal
• require explanation? - Participants response to the question whether they request an explanation for the AI
• 1st, 2nd, 3rd, ... - Explanatory form card selection and ranking cards fulfill requirement? - After the card selection, participants were asked whether the selected card combination fulfill their explainability requirement.

2 EUCA_EndUserXAI_demography.csv

It contains the participants demographics, including their age, gender, educational background, and their knowledge and attitudes toward AI.

EUCA dataset zip file for download

More Context for EUCA Dataset

[1] Critical tasks

There are four tasks. Task label and their corresponding task titles are: house - Selling your house car - Buying an autonomous driving vehicle health - Personal health decision bird - Learning bird species

Please refer to EUCA quantatative data analysis report for the storyboard of the tasks and explanation goals presented in the user study.

[2] Explanation goal

End-users may have different goals/purposes to check an explanation from AI. The EUCA dataset includes the following 11 explanation goals, with its [label] in the dataset, full name and description

1. [trust] Calibrate trust: trust is a key to establish human-AI decision-making partnership. Since users can easily distrust or overtrust AI, it is important to calibrate the trust to reflect the capabilities of AI systems.

2. [safe] Ensure safety: users need to ensure safety of the decision consequences.

3. [bias] - Detect bias: users need to ensure the decision is impartial and unbiased.

4. [unexpect] Resolve disagreement with AI: the AI prediction is unexpected and there are disagreements between users and AI.

5. [expected] - Expected: the AI's prediction is expected and aligns with users' expectations.

6. [differentiate] Differentiate similar instances: due to the consequences of wrong decisions, users sometimes need to discern similar instances or outcomes. For example, a doctor differentiates whether the diagnosis is a benign or malignant tumor.

7. [learning] Learn: users need to gain knowledge, improve their problem-solving skills, and discover new knowledge

8. [control] Improve: users seek causal factors to control and improve the predicted outcome.

9. [communicate] Communicate with stakeholders: many critical decision-making processes involve multiple stakeholders, and users need to discuss the decision with them.

10. [report] Generate reports: users need to utilize the explanations to perform particular tasks such as report production. For example, a radiologist generates a medical report on a patient's X-ray image.

11. [multi] Trade-off multiple objectives: AI may be optimized on an incomplete objective while the users seek to fulfill multiple objectives in real-world applications. For example, a doctor needs to ensure a treatment plan is effective as well as has acceptable patient adherence. Ethical and legal requirements may also be included as objectives.

[3] Explanatory form

The following 12 explanatory forms are end-user-friendly, i.e.: no technical knowledge is required for the end-user to interpret the explanation.

• Feature-Based Explanation
• Feature Attribution - fa
  • Note: for tasks that has image as input data, the feature attribution is denoted by the following two cards:
  • ir: important regions (a.k.a. heat map or saliency map)
  • irc: important regions with their feature contribution percentage
• Feature Shape - fs

• Feature Interaction - fi

• Example-Based Explanation

• Similar Example - se
• Typical Example - te

• Counterfactual Example - ce

  • Note: for contractual example, there were two visual variations used in the user study:
  • cet: counterfactual example with transition from one example to the counterfactual one
  • ceh: counterfactual example with the contrastive feature highlighted

• Rule-Based Explanation

• Rule - rt
• Decision Tree - dt

• Decision Flow - df

• Supplementary Information

• Input
• Output
• Performance
• Dataset - prior (output prediction with prior distribution of each class in the training set)

Note: occasionally there is a wild card, which means the participant draw the card by themselves. It is indicated as 'wc'.

For visual examples of each explanatory form card, please refer to the Explanatory_form_labels.pdf document.

Link to the details on users' requirements on different explanatory forms

Code and report for EUCA data quantatitve analysis

• EUCA data analysis code
• EUCA quantatative data analysis report

EUCA data citation

@article{jin2021euca,
   title={EUCA: the End-User-Centered Explainable AI Framework},
      author={Weina Jin and Jianyu Fan and Diane Gromala and Philippe Pasquier and Ghassan Hamarneh},
      year={2021},
      eprint={2102.02437},
      archivePrefix={arXiv},
      primaryClass={cs.HC}
}