117 research outputs found
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
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.Comment: 2019 AAAI Conference on Human Computation and Crowdsourcin
Broadening AI Ethics Narratives: An Indic Art View
Incorporating interdisciplinary perspectives is seen as an essential step
towards enhancing artificial intelligence (AI) ethics. In this regard, the
field of arts is perceived to play a key role in elucidating diverse historical
and cultural narratives, serving as a bridge across research communities. Most
of the works that examine the interplay between the field of arts and AI ethics
concern digital artworks, largely exploring the potential of computational
tools in being able to surface biases in AI systems. In this paper, we
investigate a complementary direction--that of uncovering the unique
socio-cultural perspectives embedded in human-made art, which in turn, can be
valuable in expanding the horizon of AI ethics. Through qualitative interviews
of sixteen artists, art scholars, and researchers of diverse Indian art forms
like music, sculpture, painting, floor drawings, dance, etc., we explore how
{\it non-Western} ethical abstractions, methods of learning, and participatory
practices observed in Indian arts, one of the most ancient yet perpetual and
influential art traditions, can inform the FAccT community. Insights from our
study suggest (1) the need for incorporating holistic perspectives (that are
informed both by data-driven observations and prior beliefs encapsulating the
structural models of the world) in designing ethical AI algorithms, (2) the
need for integrating multimodal data formats for design, development, and
evaluation of ethical AI systems, (3) the need for viewing AI ethics as a
dynamic, cumulative, shared process rather than as a self contained framework
to facilitate adaptability without annihilation of values, (4) the need for
consistent life-long learning to enhance AI accountability, and (5) the need
for identifying ethical commonalities across cultures and infusing the same
into AI system design, so as to enhance applicability across geographies
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