Epistemic Therapy for Bias in Automated Decision-Making

Abstract

Despite recent interest in both the critical and machine learning literature on "bias" in artificial intelligence (AI) systems, the nature of specific biases stemming from the interaction of machines, humans, and data remains ambiguous. Influenced by Gendler's work on human cognitive biases, we introduce the concept of alief-discordant belief, the tension between the intuitive moral dispositions of designers and the explicit representations generated by algorithms. Our discussion of alief-discordant belief diagnoses the ethical concerns that arise when designing AI systems atop human biases. We furthermore codify the relationship between data, algorithms, and engineers as components of this cognitive discordance, comprising a novel epistemic framework for ethics in AI.