While a vast collection of explainable AI (XAI) algorithms has been developed in recent years, they have been criticized for significant gaps with how humans produce and consume explanations. As a result, current XAI techniques are often found to be hard to use and lack effectiveness. In this work, we attempt to close these gaps by making AI explanations selective ---a fundamental property of human explanations---by selectively presenting a subset of model reasoning based on what aligns with the recipient's preferences. We propose a general framework for generating selective explanations by leveraging human input on a small dataset. This framework opens up a rich design space that accounts for different selectivity goals, types of input, and more. As a showcase, we use a decision-support task to explore selective explanations based on what the decision-maker would consider relevant to the decision task. We conducted two experimental studies to examine three paradigms based on our proposed framework: in Study 1, we ask the participants to provide critique-based or open-ended input to generate selective explanations (self-input). In Study 2, we show the participants selective explanations based on input from a panel of similar users (annotator input). Our experiments demonstrate the promise of selective explanations in reducing over-reliance on AI and improving collaborative decision making and subjective perceptions of the AI system, but also paint a nuanced picture that attributes some of these positive effects to the opportunity to provide one's own input to augment AI explanations. Overall, our work proposes a novel XAI framework inspired by human communication behaviors and demonstrates its potential to encourage future work to make AI explanations more human-compatible.
- Award ID(s):
- 1900767
- NSF-PAR ID:
- 10344287
- Date Published:
- Journal Name:
- ACM Transactions on Interactive Intelligent Systems
- ISSN:
- 2160-6455
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Practitioner notes What is already known about this topic
Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.
While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.
There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.
What this paper adds
Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.
Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.
Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.
Implications for practice and/or policy
It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.
Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.
To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3531066
