mlsanityData to 13 Oct 2026

Hard Choices and Hard Limits for Artificial Intelligence

arXiv 2105.07852 ↗First posted 4 May 2021cs.CY cs.AI cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI) is supposed to help us make better choices. Some of these choices are small, like what route to take to work, or what music to listen to. Others are big, like what treatment to administer for a disease or how long to sentence someone for a crime. If AI can assist with these big decisions, we might think it can also help with hard choices, cases where alternatives are neither better, worse nor equal but on a par. The aim of this paper, however, is to show that this view is mistaken: the fact of parity shows that there are hard limits on AI in decision making and choices that AI cannot, and should not, resolve.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 16 of its 87 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

16 of 87 references.

References · 16

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,005
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,915
  3. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 585
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  7. 2019On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inferencecited by 49
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4 more
  1. 2019Equal Opportunity in Online Classification with Partial Feedbackcited by 34
  2. 2018Legible Normativity for AI Alignment: The Value of Silly Rulescited by 15
  3. 2020The Impossibility Theorem of Machine Fairness -- A Causal Perspectivecited by 13
  4. 2019Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitmentscited by 8

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023Auditing large language models: a three-layered approachcited by 136
  2. 2025AI Agents and Hard Choicescited by 0