首页/论文/1909.05167

arXiv 1909.051672019-09cs.LGcs.AIcs.CYstat.ML

FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

University of Bristol（布里斯托尔大学）；RMIT University（皇家墨尔本理工大学）

Kacper Sokol, Raul Santos-Rodriguez, Peter Flach

备注：: Homepage: https://fat-forensics.org/ Source Code: https://github.com/fat-forensics/fat-forensics/
DOI：: 10.1016/j.simpa.2022.100406

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that such algorithms can cause we developed an open source toolbox that can analyse selected fairness, accountability and transparency aspects of the machine learning process: data (and their features), models and predictions, allowing to automatically and objectively report them to relevant stakeholders. In this paper we describe the design, scope, usage and impact of this Python package, which is published under the 3-Clause BSD open source licence.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

University of Bristol（布里斯托大学）

Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life. In most cases, however, these systems and decisions are neither regulated nor certified. Given the potential harm that these algorithms can cause, their qualities such as fairness, accountability and transparency (FAT) are of paramount importance. To ensure high-quality, fair, transparent and reliable predictive systems, we developed an open source Python package called FAT Forensics. It can inspect important fairness, accountability and transpare…

2022-09-08cs.AIcs.CYcs.LG

人工智能中的FATE：迈向算法包容性与可及性

FATE in AI: Towards Algorithmic Inclusivity and Accessibility

University of Huddersfield（哈德斯菲尔德大学）

本研究通过用户研究和参与式会议，探讨全球南方地区AI的FATE需求，提出社区主导的数据策略以促进包容性，确保AI符合社会价值观。

2023-11-13cs.AIcs.CYcs.HC

算法灭绝在加沙：人工智能、反制责任与公共话语的腐败

Genocide by Algorithm in Gaza: Artificial Intelligence, Countervailing Responsibility, and the Corruption of Public Discourse

Aula Fellowship for AI（奥拉人工智能奖学金项目）

本文探讨人工智能在加沙战争中的角色，分析其作为认知基础设施对暴力的分类与合法化，并呼吁通过民主化AI伦理来对抗算法暴力。

2026-02-09cs.AIcs.CYcs.HC

The Algorithmic Imprint

Georgia Institute of Technology（佐治亚理工学院）；Data & Society Research Institute（数据与社会研究所）

When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE). However, just because an algorithm is removed does not imply its FATE-related issues cease to exist. In this paper, we introduce the notion of the "algorithmic imprint" to illustrate how merely removing an algorithm does not necessarily undo or mitigate its consequences. We operationalize this concept and its implications through the 2020 events surrounding the algorithmic grading of the General Certificate of Education (G…

2022-06-03cs.AIcs.CYcs.HC

《人工智能法案》对非歧视法律与算法公平性的影响

Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness

University of Bayreuth（拜罗伊特大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）

本文探讨欧盟《人工智能法案》如何通过将非歧视责任前置至AI设计阶段，弥合算法公平性与法律框架的鸿沟，并提出偏见检测与纠正的实践建议。

2024-06-26cs.AI

"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning

Duke Institute for Health Data & Society Research（杜克健康数据与社会研究所）；Duke Institute for Health Innovation（杜克健康创新研究所）；Harvard University（哈佛大学）；Duke School of Medicine（杜克医学院）

Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less focus on real world implementation and the associated challenges to accuracy, fairness, accountability, and transparency that come from actual, situated use. Serious questions remain under examined regarding how to ethically build models, interpret and explain model output, recognize and account for biases, and minimize disruptions to professional expertise and work cultures. We address this gap in the literature and pr…

2019-12-07cs.AIcs.CYcs.HC

更多相关论文收起更多论文

The Right Tool for the Job: Open-Source Auditing Tools in Machine Learning

Complex Adaptive Systems Research（复杂自适应系统研究机构）

In recent years, discussions about fairness in machine learning, AI ethics and algorithm audits have increased. Many entities have developed framework guidance to establish a baseline rubric for fairness and accountability. However, in spite of increased discussions and multiple frameworks, algorithm and data auditing still remain difficult to execute in practice. Many open-source auditing tools are available, but users aren't always aware of the tools, what they are useful for, or how to access them. Model auditing and evaluation are not frequently emphasized skills in machine learning. There…

2022-06-20cs.AIcs.CYcs.LG

Addressing multiple metrics of group fairness in data-driven decision making

Joint Research Centre, European Commission（欧盟联合研究中心）；Universitat Pompeu Fabra（庞培法布拉大学）

The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) literature proposes a varied set of group fairness metrics to measure discrimination against socio-demographic groups that are characterized by a protected feature, such as gender or race.Such a system can be deemed as either fair or unfair depending on the choice of the metric. Several metrics have been proposed, some of them incompatible with each other.We do so empirically, by observing that several of these metrics cluster together in two or three main clusters for the same groups and machine learning methods. In a…

2020-03-10cs.CYcs.LGstat.ML