While there have been many proposals on how to make AI algorithms more transparent , few have attempted to evaluate the impact of AI explanations on human performance on a task using AI. We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG)  as a method of evaluating the efficacy of explanations  in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images. We study the effect of VQA agent explanations on the game performance as a function of explanation type and quality. We observe that "helpful" explanations are conducive to game performance (by almost 22\% for "excellent" rated explanation games), and having at least one "correct " explanation is significantly helpful when VQA system answers are mostly noisy (by almost 30\% compared to no explanation games). We also see that players develop a preference for explanations even when penalized and that the explanations are mostly rated as "helpful" .
While there have been many proposals on making AI algorithms transparent and explainable , few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG) , as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA) . In our proposed ExAG, a human user needs to guess a secretly picked image by the VQA agent by asking natural language questions . We show that when AI explains its answers, users succeed more often in guessing the secret image correctly. Furthermore, we show that while good explanations improve human performance, incorrect explanations can degrade game performance as compared to no-explanation games. Notably, a few correct explanations can readily improve human performance in game rounds where the AI system's answers are mostly incorrect as compared to no-explanation games. Our experiments, therefore, show that ExAG is an effective means to evaluate the efficacy of AI-generated explanation on human-AI collaborative tasks .
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how to make AI algorithms more transparent
making AI algorithms transparent and explainable
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AI
AI-generated
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on a task using AI. We
in conducting human-AI collaborative tasks. To bridge the gap, we
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guessing
retrieval
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,
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(visual evidence or textual justification)
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- the task of answering
. In our proposed ExAG, a human user needs to guess a secretly picked image by the VQA agent by asking
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on images. We study the effect of VQA agent explanations on the game performance as a function of explanation type and quality. We observe that "helpful" explanations are conducive to game performance (by almost 22\% for "excellent" rated explanation games), and having at least one "correct " explanation is significantly helpful when VQA system
. We show that when AI explains its answers, users succeed more often in guessing the secret image correctly. Furthermore, we show that while good explanations improve human performance, incorrect explanations can degrade game performance as compared to no-explanation games. Notably, a few correct explanations can readily improve human performance in game rounds where the AI system's
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noisy (by almost 30\% compared to no explanation games). We also see that players develop a preference for explanations even when penalized and that the explanations are mostly rated as "helpful"
incorrect as compared to no-explanation games. Our experiments, therefore, show that ExAG is an effective means to evaluate the efficacy of AI-generated explanation on human-AI collaborative tasks