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Evidence record 15830 · automatically gathered

Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation

arXiv:2608.02551v1 Announce Type: new Abstract: Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates "a CEO in the United States," the prompt leaves demographic realization to the model. Existing group fairness definitions assume that sensitive attributes are given on the input side. Generative audits instead examine output-side demographic composition, yet the targets they compare it against are typically sup

Record details

Published: 4 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Transparency
Retrieved: 4 August 2026

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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

Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation

arXiv · 3 August 2026

Shared topic: Bias & fairness · Shared topic: Transparency

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Shared topic: Bias & fairness · Shared topic: Transparency

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

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Shared topic: Bias & fairness · Shared topic: Transparency

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

arXiv · 3 August 2026

How to cite this record

ethics.ai (4 August 2026), “Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation,” evidence record 15830, https://ethics.ai/record/15830 (originally published by arXiv cs.CY).

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