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Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation
In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to data volume. Recommender systems...
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MBFair: a model-based verification methodology for detecting violations of individual fairness
Decision-making systems are prone to discrimination against individuals with regard to protected characteristics such as gender and ethnicity....
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FedEem: a fairness-based asynchronous federated learning mechanism
Federated learning is a mechanism for model training in distributed systems, aiming to protect data privacy while achieving collective intelligence....
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Multi-sourced Integrated Ranking with Exposure Fairness
Integrated ranking system is one of the critical components of industrial recommendation platforms. An integrated ranking system is expected to...
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A model of the relationship between the variations of effectiveness and fairness in information retrieval
The requirement that, for fair document retrieval, the documents should be ranked in the order to equally expose authors and organizations has been...
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Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning
Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. This posits...
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PreCoF: counterfactual explanations for fairness
This paper studies how counterfactual explanations can be used to assess the fairness of a model. Using machine learning for high-stakes decisions is...
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Enforcing fairness using ensemble of diverse Pareto-optimal models
One of the main challenges of machine learning is to ensure that its applications do not generate or propagate unfair discrimination based on...
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Policy advice and best practices on bias and fairness in AI
The literature addressing bias and fairness in AI models ( fair-AI ) is growing at a fast pace, making it difficult for novel researchers and...
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DFGR: Diversity and Fairness Awareness of Group Recommendation in an Event-based Social Network
An event-based social network is a new type of social network that combines online and offline networks, and one of its important problems is...
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Fairness–accuracy tradeoff: activation function choice in a neural network
Models can have different outcomes based on the different types of inputs; the training data used to build the model will change its output (a...
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Dbias: detecting biases and ensuring fairness in news articles
Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in...
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Bias and Fairness
In the artificial intelligence (AI) landscape, bias’s impact on decisions is of vital concern. From individual choices to complex models, bias...
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A Federated Framework for Edge Computing Devices with Collaborative Fairness and Adversarial Robustness
Federated learning is a distributed machine learning framework for edge computing devices that provides several benefits, such as eliminating...
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Adversarial learning for counterfactual fairness
In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at...
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Fairness in graph-based semi-supervised learning
Machine learning is widely deployed in society, unleashing its power in a wide range of applications owing to the advent of big data. One emerging...
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Social norm bias: residual harms of fairness-aware algorithms
Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive...
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Algorithmic fairness datasets: the story so far
Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a...
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A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle
Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on...
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Achieving User-Side Fairness in Contextual Bandits
Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt...
