Patterns and Antipatterns, Principles, and Pitfalls: Accountability and Transparency in Artificial Intelligence

Jeanna Neefe Matthews

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Ethics and Social Impacts of AI Digitalization, Law, and Regulation Explainable Artificial Intelligence (XAI)

Abstract

This article discusses a set of principles for accountability and transparency in AI as well as a set of antipatterns or harmful trends too often seen in deployed systems. It provides concrete suggestions for what can be done to shift the balance away from these antipatterns and toward more positive ones.

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References

2018Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor 2017Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings 2022Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints 2017Meaningful information and the right to explanation 2016Class Advantage, Commitment Penalty 2019Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction

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2022Decoding Human Behavior with Big Data? Critical, Constructive Input from the Decision Sciences 2026Process safety in the era of AI: Assurance, deployment, and measurable risk reduction

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