The Technology of Outrage: Bias in Artificial Intelligence

TL;DR

This paper critically examines the concept of bias in AI, revealing misconceptions, analyzing emotional reactions, and proposing practical strategies for addressing bias in machine learning systems.

Contribution

It clarifies the ambiguous use of 'bias,' identifies forms of outrage, and suggests concrete approaches for bias mitigation in AI.

Findings

Algorithms can be biased, contradicting claims of fairness.

Emotional outrage stems from intellectual, moral, and political sources.

Proposes clarifying bias language and developing new auditing methods.

Abstract

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.