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End-User-Centered Explainable AI Framework

A practical prototyping tool to design explainable AI for non-technical end-users.

End-User-Friendly Explanatory Forms
• Feature-based explanation
• Example-based explanation
• Rule-based explanation
• Supplementary information
Explanation Needs
EUCA Dataset

EUCA dataset description

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

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


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


[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.

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 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}
}