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Evidence record 1208 · automatically gathered

Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$δ$}{delta} Alignment

Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in multi-task learning (MTL) can be confounded by method-induced representation scales. This paper identifies threshold confounding: when the auditing tolerance is derived from each model's own representation distances, different algorithms are compared under different semantic thresholds. A threshold-drift analysis further shows how Bias rankings can

Record details

Published: 9 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Transparency
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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Shared topic: Bias & fairness · Shared topic: Safety & alignment

Prompt Programming for Cultural Bias and Alignment of Large Language Models

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

Explaining Explanations: An Overview of Interpretability of Machine Learning

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How to cite this record

ethics.ai (9 June 2026), “Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$δ$}{delta} Alignment,” evidence record 1208, https://ethics.ai/record/1208 (originally published by arXiv).

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