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A review on fairness in machine learning
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
Fairness in recommender systems: research landscape and future directions
Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …
Algorithmic fairness
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence (AI) and machine learning (ML) algorithms in spheres …
controlled by artificial intelligence (AI) and machine learning (ML) algorithms in spheres …
[HTML][HTML] A survey on fairness-aware recommender systems
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …
life by providing personalized suggestions and facilitating people in decision-making, which …
Disentangling fairness perceptions in algorithmic decision-making: the effects of explanations, human oversight, and contestability
Recent research claims that information cues and system attributes of algorithmic decision-
making processes affect decision subjects' fairness perceptions. However, little is still known …
making processes affect decision subjects' fairness perceptions. However, little is still known …
Fair top-k ranking with multiple protected groups
Ranking items or people is a fundamental operation at the basis of several processes and
services, not all of them happening online. Ranking is required for different tasks, including …
services, not all of them happening online. Ranking is required for different tasks, including …
Towards understanding and mitigating unintended biases in language model-driven conversational recommendation
Abstract Conversational Recommendation Systems (CRSs) have recently started to
leverage pretrained language models (LM) such as BERT for their ability to semantically …
leverage pretrained language models (LM) such as BERT for their ability to semantically …
Understanding the contribution of recommendation algorithms on misinformation recommendation and misinformation dissemination on social networks
Social networks are a platform for individuals and organizations to connect with each other
and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have …
and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have …
[HTML][HTML] FairLens: Auditing black-box clinical decision support systems
The pervasive application of algorithmic decision-making is raising concerns on the risk of
unintended bias in AI systems deployed in critical settings such as healthcare. The detection …
unintended bias in AI systems deployed in critical settings such as healthcare. The detection …
Fairness metrics and bias mitigation strategies for rating predictions
A Ashokan, C Haas - Information Processing & Management, 2021 - Elsevier
Algorithm fairness is an established line of research in the machine learning domain with
substantial work while the equivalent in the recommender system domain is relatively new …
substantial work while the equivalent in the recommender system domain is relatively new …