Interdisciplinary Approaches to Understanding Artificial Intelligence's Impact on Society

TL;DR

This paper emphasizes the importance of interdisciplinary approaches to understanding AI's societal impact, highlighting the need to address ethical, social, and technical challenges collectively.

Contribution

It advocates for integrating social sciences with computer science to better address AI's societal harms and promote responsible development.

Findings

AI causes biases and inequalities in society

Technical focus overlooks societal harms

Interdisciplinary collaboration can mitigate risks

Abstract

Innovations in AI have focused primarily on the questions of "what" and "how"-algorithms for finding patterns in web searches, for instance-without adequate attention to the possible harms (such as privacy, bias, or manipulation) and without adequate consideration of the societal context in which these systems operate. In part, this is driven by incentives and forces in the tech industry, where a more product-driven focus tends to drown out broader reflective concerns about potential harms and misframings. But this focus on what and how is largely a reflection of the engineering and mathematics-focused training in computer science, which emphasizes the building of tools and development of computational concepts. As a result of this tight technical focus, and the rapid, worldwide explosion in its use, AI has come with a storm of unanticipated socio-technical problems, ranging from algorithms that act in racially or gender-biased ways, get caught in feedback loops that perpetuate inequalities, or enable unprecedented behavioral monitoring surveillance that challenges the fundamental values of free, democratic societies. Given that AI is no longer solely the domain of technologists but rather of society as a whole, we need tighter coupling of computer science and those disciplines that study society and societal values.