AUTHOR=Moore Jared TITLE=AI for Not Bad JOURNAL=Frontiers in Big Data VOLUME=2 YEAR=2019 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2019.00032 DOI=10.3389/fdata.2019.00032 ISSN=2624-909X ABSTRACT=

Hype surrounds the promotions, aspirations, and notions of “artificial intelligence (AI) for social good” and its related permutations. These terms, as used in data science and particularly in public discourse, are vague. Far from being irrelevant to data scientists or practitioners of AI, the terms create the public notion of the systems built. Through a critical reflection, I explore how notions of AI for social good are vague, offer insufficient criteria for judgement, and elide the externalities and structural interdependence of AI systems. Instead, the field known as “AI for social good” is best understood and referred to as “AI for not bad.”