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Evidence record 17932 · automatically gathered

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning

Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation. Existing fairness-aware aggregation methods remain formally vulnerable to fairness poisoning: a malicious client maximizing group disparity while preserving accuracy evades accuracy-based Byzantine defenses, and in our threat model FairFed's gap-based weighting can be gamed by an adversary who observes the global fairness score. We present Fairis, a server-s

Record details

Published: 6 August 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Bias & fairness
Retrieved: 10 August 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

Challenges in Evaluating Explanation Methods for Static and Evolving Data

arXiv cs.AI · 6 August 2026

Shared topic: Bias & fairness

Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

arXiv · 6 August 2026

Shared topic: Bias & fairness

Depth-Guided Video Object Counting in Crowded Scenes

arXiv cs.AI · 6 August 2026

Shared topic: Bias & fairness

CogVis: Must Open-Vocabulary Change Detection Perceive the Scene Anew for Every Query?

arXiv cs.AI · 6 August 2026

Shared topic: Bias & fairness

Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts

arXiv cs.AI · 6 August 2026

Shared topic: Bias & fairness

Conditional Cognitive Biases in LLMs: How Biased User Turns Modulate In-Context Reasoning

arXiv cs.CY · 7 August 2026

How to cite this record

ethics.ai (6 August 2026), “Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning,” evidence record 17932, https://ethics.ai/record/17932 (originally published by arXiv cs.CR (AI security)).

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