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