The Technology of Outrage: Bias in Artificial Intelligence

Artificial intelligence and machine learning are increasingly used to offload decision making from people. In the past, one of the rationales for this replacement was that machines, unlike people, can be fair and unbiased. Evidence suggests otherwise. We begin by entertaining the ideas that algorithms can replace people and that algorithms cannot be biased. Taken as axioms, these statements quickly lead to absurdity. Spurred on by this result, we investigate the slogans more closely and identify equivocation surrounding the word 'bias.' We diagnose three forms of outrage-intellectual, moral, and political-that are at play when people react emotionally to algorithmic bias. Then we suggest three practical approaches to addressing bias that the AI community could take, which include clarifying the language around bias, developing new auditing methods for intelligent systems, and building certain capabilities into these systems. We conclude by offering a moral regarding the conversations about algorithmic bias that may transfer to other areas of artificial intelligence.

Taking the Intentional Stance Seriously: A Guide to Progress in Artificial Intelligence

Finding claims that researchers have made considerable progress in artificial intelligence over the last several decades is easy. However, our everyday interactions with cognitive systems quickly move from intriguing to frustrating. The root of those frustrations rests in a mismatch between the expectations we have due to our inherent, folk-psychological theories and the real limitations we see in existing computer programs. To address the discordance, we find ourselves building mental models of how each unique tool works: how we address Apple's Siri may differ from how we address Amazon's Alexa, the prompts that create striking images in Midjourney may produce unsatisfactory renderings in OpenAI's DALL-E. Emphasizing intentionality in research on cognitive systems provides a way to reduce these discrepancies, bringing system behavior closer to folk psychology. This paper scrutinizes the propositional attitude of intention to clarify this claim. That analysis is joined with broad methodological suggestions informed by recent practices within large-scale research programs. The overall goal is to identify a novel approach for measuring and making progress in artificial intelligence.