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Evidence record 14866 · automatically gathered

When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution

arXiv:2607.28041v1 Announce Type: new Abstract: As AI systems become capable of producing the essays, code, reports, summaries, plans, and decisions through which institutions usually recognize competence, a familiar question becomes harder to answer: what is learning for? Existing AI ethics rightly emphasizes present failures--bias, opacity, hallucination, labor extraction, privacy risk, and weak accountability. But if the case for learning rests only on those failures, then each technical impr

Record details

Published: 31 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Privacy · Jobs & economy · Transparency
Retrieved: 31 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution

arXiv · 30 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

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Shared topic: Bias & fairness · Shared topic: Privacy

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification

arXiv cs.CR (AI security) · 23 July 2026

Same publisher byline · Shared topic: Transparency

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arXiv · 22 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

arXiv · 21 July 2026

How to cite this record

ethics.ai (31 July 2026), “When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution,” evidence record 14866, https://ethics.ai/record/14866 (originally published by arXiv cs.CY).

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