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 .
While there have been many proposals on making AI algorithms 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 secret image picked by the VQA agent by asking natural language questions to it . We show that overall, when AI explains its answers, users succeed more often in guessing the secret image correctly. Notably, a few correct explanations can readily improve human performance when VQA answers are mostly incorrect as compared to no-explanation games. Furthermore, we also show that while explanations rated as "helpful" significantly improve human performance , "incorrect" and "unhelpful" explanations can degrade performance as compared to no-explanation games. Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task .
D
transparent and
R
secretly picked image
secret image picked
A
to it
A
overall,
R
Furthermore, we show that while good explanations
Notably, a few correct explanations can readily
R
, incorrect explanations can degrade game performance
when VQA answers are mostly incorrect
R
Notably, a few correct explanations can readily
Furthermore, we also show that while explanations rated as "helpful" significantly
R
in game rounds where the AI system's answers are mostly incorrect
, "incorrect" and "unhelpful" explanations can degrade performance
R
show
demonstrate
R
explanation on
explanations on a
R
tasks
task