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Evidence record 4551 · automatically gathered

Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems

Designing fair algorithmic decision systems requires balancing model performance with fairness toward affected individuals: More fairness might require sacrificing some performance and vice versa, yet the space of possible trade-offs is still poorly understood. We investigate fairness in binary prediction-based decision problems by conceptualizing decision making as a multi-objective optimization problem that simultaneously considers decision-maker utility and group fairness. We investigate the

Record details

Published: 11 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Finance, VC & PE
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: Finance, VC & PE

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Shared topic: Bias & fairness · Shared topic: Finance, VC & PE

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Shared topic: Bias & fairness · Shared topic: Finance, VC & PE

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Shared topic: Bias & fairness · Shared topic: Finance, VC & PE

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How to cite this record

ethics.ai (11 May 2026), “Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems,” evidence record 4551, https://ethics.ai/record/4551 (originally published by arXiv).

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