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Evidence record 3033 · automatically gathered

Normative Alignment of Recommender Systems via Internal Label Shift

We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions over item-level attributes, such as categories. Recommender systems optimized solely for user engagement often fail to satisfy broader normative objectives, including fairness, diversity, and editorial values. NAILS modifies the user-conditional item distribution to induce a specified marginal distribution over attrib

Record details

Published: 12 July 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 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.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (12 July 2026), “Normative Alignment of Recommender Systems via Internal Label Shift,” evidence record 3033, https://ethics.ai/record/3033 (originally published by arXiv fairness query).

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