Artificial intelligence (AI) and algorithmic decision making are having a profound impact on
our daily lives. These systems are vastly used in different high-stakes applications like …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

Graph diffusion problems such as the propagation of rumors, computer viruses, or smart grid
failures are ubiquitous and societal. Hence it is usually crucial to identify diffusion sources …

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 …

In this paper, we study the bias in named entity recognition (NER) models---specifically, the
difference in the ability to recognize male and female names as PERSON entity types. We …

In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

Social media posts have been used to predict different user behaviors and attitudes,
including mental health condition, political affiliation, and vaccine hesitancy. Unfortunately …

Machine Learning models have been deployed across many different aspects of society,
often in situations that affect social welfare. Although these models offer streamlined …

The rapid use and success of deep learning models in various application domains have
raised significant challenges about the fairness of these models when used in the real world …

1)(暨南大学信息科学技术学院, 广州510632) 2)(桂林电子科技大学广西可信软件重点实验室,
广西桂林541004) 摘要随着人工智能的发展, 机器学习技术越来越多地应用于社会各个领域 …