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Reframing Explanation as an Interactive Medium: The EQUAS (Explainable QUestion Answering System) Project
  • +13
  • Dhruv Batra ,
  • William Ferguson,
  • Raymond Mooney,
  • Devi Parikh,
  • Antonio Torralba ,
  • David Bau,
  • David Diller,
  • Joshua Fasching,
  • Jaden Fiotto-Kaufman,
  • Yash Goyal ,
  • Jeff Miller,
  • Kerry Moffitt,
  • Alex Montes De Oca,
  • Ramprasaath R. Selvaraju ,
  • Ayush Shrivastava ,
  • Jialin Wu
Dhruv Batra
Georgia Tech
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William Ferguson
Raytheon BBN Technologies
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Raymond Mooney
The University of Texas at Austin
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Devi Parikh
Georgia Tech
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Antonio Torralba
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
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David Bau
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
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David Diller
Raytheon BBN Technologies
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Joshua Fasching
Raytheon BBN Technologies
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Jaden Fiotto-Kaufman
Raytheon BBN Technologies
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Yash Goyal
Georgia Tech
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Jeff Miller
Raytheon BBN Technologies
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Kerry Moffitt
Raytheon BBN Technologies
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Alex Montes De Oca
Raytheon BBN Technologies
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Ramprasaath R. Selvaraju
Georgia Tech
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Ayush Shrivastava
Georgia Tech
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Jialin Wu
The University of Texas at Austin
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Abstract

This letter provides a retrospective analysis of our team’s research performed under the DARPA Explainable Artificial Intelligence (XAI) project. We began by exploring salience maps, English sentences, and lists of feature names for explaining the behavior of deep-learning-based discriminative systems, especially visual question answering systems. We demonstrated limited positive effects from statically presenting explanations along with system answers – for example when teaching people to identify bird species. Many XAI performers were getting better results when users interacted with explanations. This motivated us to evolve the notion of explanation as an interactive medium – usually, between humans and AI systems but sometimes within the software system. We realized that interacting via explanations could enable people to task and adapt ML agents. We added affordances for editing explanations and modified the ML system to act in accordance with the edits to produce an interpretable interface to the agent. Through this interface, editing an explanation can adapt a system’s performance to new, modified purposes. This deep tasking, wherein the agent knows its objective and the explanation for that objective will be critical to enable higher levels of autonomy.

Peer review status:IN REVISION

11 Jun 2021Submitted to Applied AI Letters
18 Jun 2021Assigned to Editor
18 Jun 2021Submission Checks Completed
22 Jun 2021Reviewer(s) Assigned
17 Jul 2021Review(s) Completed, Editorial Evaluation Pending
26 Jul 2021Editorial Decision: Revise Minor