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The Illusion of Opting in AI-Mediated Consequential Decisions

arXiv 2605.28210 ↗First posted 27 May 2026cs.AI cs.CY cs.HCcited by 0

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Abstract (arXiv)

Drawing on Ullmann-Margalit's concept of opting (transformative, irrevocable, and shadowed by foreclosed alternatives), we show that current AI systems raise a profound ethical problem that existing AI ethics has not fully captured: the illusion of opting, in which persons and groups encounter the deceptive appearance of meaningful consequential choice while the agency needed to become genuinely capable of choosing is weakened. Against approaches that treat AI primarily as an optimizer of already given ends, we argue that AI systems should be evaluated by whether they protect and cultivate meta-capacity against the illusion of opting: the socially and institutionally scaffolded agentive capacity through which means and ends can be formed, contested, revised, and owned. This reframing is especially urgent for disadvantaged populations, who are least able to absorb the costs of the illusion of opting when AI-mediated pathways misdirect behavior and action. We propose three normative imperatives for AI-mediated consequential decisions: existential honesty, which acknowledges the limits of prediction; ecological rationality, which situates guidance within heterogeneous lived ecologies; and counterfactual reparation, which acknowledges and repairs foreclosed alternatives when AI-mediated decision-making pathways fail.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 50 references that are in the map. First posted on arXiv 27 May 2026.

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13 of 50 references.

References · 13

  1. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  2. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  3. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  4. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 232
  5. 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 137
  6. 2018Explaining Explanations in AIcited by 136
  7. 2020Participation is not a Design Fix for Machine Learningcited by 110
  8. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
  9. 2019Problem Formulation and Fairnesscited by 85
  10. 2020Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contextscited by 22
  11. 2022Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophycited by 15
  12. 2021The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studiescited by 13
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  1. 2023Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systemscited by 9

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