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Fairness as a Service (FaaS): verifiable and privacy-preserving fairness auditing of machine learning systems
Providing trust in machine learning (ML) systems and their fairness is a socio-technical challenge, and while the use of ML continues to rise, there...
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Ensuring Fairness of Human- and AI-Generated Test Items
Large language models (LLMs) have been a catalyst for the increased use of AI for automatic item generation on high-stakes assessments. Standard...
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Understanding and improving fairness in cognitive diagnosis
Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive...
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Fairness
Once you’ve laid out the foundations for a responsible AI framework by defining AI principles and ensuring data ethics are applied to the training...
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Fairness-aware machine learning engineering: how far are we?
Machine learning is part of the daily life of people and companies worldwide. Unfortunately, bias in machine learning algorithms risks unfairly...
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Rawlsian AI fairness loopholes
Researchers and industry developers in artificial intelligence (AI) and natural language processing (NLP) have uniformly adopted a Rawlsian...
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Fairness with censorship and group constraints
Fairness in machine learning (ML) has gained attention within the ML community and the broader society beyond with many fairness definitions and...
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Search-based Automatic Repair for Fairness and Accuracy in Decision-making Software
Decision-making software mainly based on Machine Learning (ML) may contain fairness issues (e.g., providing favourable treatment to certain people...
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FairCF: fairness-aware collaborative filtering
Collaborative filtering (CF) techniques learn user and item embeddings from user-item interaction behaviors, and are commonly used in recommendation...
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Verifiable Fairness: Privacy–preserving Computation of Fairness for Machine Learning Systems
Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and...
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Towards Transnational Fairness in Machine Learning: A Case Study in Disaster Response Systems
Research on fairness in machine learning (ML) has been largely focusing on individual and group fairness. With the adoption of ML-based technologies...
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Investigating fairness in machine learning-based audio sentiment analysis
Audio sentiment analysis is a growing area of research, however little attention has been paid to the fairness of machine learning models in this...
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Federated Dynamic Client Selection for Fairness Guarantee in Heterogeneous Edge Computing
Federated learning has emerged as a distributed learning paradigm by training at each client and aggregating at a parameter server. System...
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Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare
As machine learning (ML) usage becomes more popular in the healthcare sector, there are also increasing concerns about potential biases and risks...
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Algorithmic Fairness
An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) and machine learning...
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The Man Behind the Curtain: Appropriating Fairness in AI
Our goal in this paper is to establish a set of criteria for understanding the meaning and sources of attributing (un)fairness to AI algorithms. To...
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Fairness, Simulations, and Inhibitor Arcs
An execution of a parallel system is fair if no activity that is capable of taking place is denied this capability for a very long time, or even...
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To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods
The right to be forgotten (RTBF) allows individuals to request the removal of personal information from online platforms. Researchers have proposed...
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On the impact of multi-dimensional local differential privacy on fairness
Automated decision systems are increasingly used to make consequential decisions in people’s lives. Due to the sensitivity of the manipulated data...
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Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems
Decisions made by various artificial intelligence (AI) systems greatly influence our day-to-day lives. With the increasing use of AI systems, it...
