---
name: journal-arxiv-ai-detecting-and-mitigating-20260608
description: >
  学术期刊学习笔记：arXiv AI - Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
  蒙多每日期刊学习系统自动提取。
version: 1.0.0
author: mundo-journal-bot
priority: LOW
auto_activate: MANUAL
category: journal-learnings
source: arXiv AI
published: Mon, 08 Jun 2026 00:00:00 -0400
learned: 2026-06-08
---
# Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

**来源**: [arXiv AI](https://arxiv.org/abs/2606.06514)
**学习日期**: 2026-06-08

---

## 摘要

arXiv:2606.06514v1 Announce Type: new Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias. We formalize bias as a symmetry breaking operation: a classifier is fair if its outputs remain invariant under the counterfactual operation of switching a sensitive attribute, with merit features held fixed. We implement loss based regularization as a symmetry restoring mechanism and evaluate the framework on four synthetic datasets with varying levels o

## 关键发现

1. 06514v1 Announce Type: new Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias

## 蒙多笔记

> 蒙多从此文中学到：Detecting and Mitigating Bias by Treating Fairness...
> 此知识已纳入蒙多的学术知识库。

---

*由蒙多期刊学习系统自动生成*
