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Evidence record 14949 · automatically gathered

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

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 improvement appears to weaken it. This article devel

Record details

Published: 30 July 2026
Source: arXiv
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 cs.CY · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Ethical use of artificial intelligence in education: proposed ethical competency framework for teachers

Frontiers in Artificial Intelligence · 3 August 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

arXiv · 3 August 2026

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

What Does the Credential Still Certify? Cognitive Stewardship for AI-Mediated Education

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 (30 July 2026), “When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution,” evidence record 14949, https://ethics.ai/record/14949 (originally published by arXiv).

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