Machine learning models are increasingly adopted for facilitating clinical decision-making.
However, recent research has shown that machine learning techniques may result in …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Federated learning (FL) has gain growing interests for its capability of learning from
distributed data sources collectively without the need of accessing the raw data samples …

Federated learning (FL) refers to the paradigm of learning models over a collaborative
research network involving multiple clients without sacrificing privacy. Recently, there have …

Recently, with the applications of algorithms in various risky scenarios, algorithmic fairness
has been a serious concern and received lots of interest in machine learning community. In …

People frequently interact with information retrieval (IR) systems, however, IR models exhibit
biases and discrimination towards various demographics. The in-processing fair ranking …

Social science research has shown that candidates with names indicative of certain races or
genders often face discrimination in employment practices. Similarly, Large Language …

Machine learning systems produce biased results towards certain demographic groups,
known as the fairness problem. Recent approaches to tackle this problem learn a latent …

The rapid accumulation of large-scale Electronic Health Records (EHR) presents
considerable opportunities to generate real-world evidence to inform clinical decision …

Liver transplantation often faces fairness challenges across subgroups defined by sensitive
attributes such as age group, gender, and race/ethnicity. Machine learning models for …