As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

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

Human-AI coevolution, defined as a process in which humans and AI algorithms
continuously influence each other, increasingly characterises our society, but is …

The widespread collection and use of data has been the subject of much public and
scholarly interest in recent years. Data justice has emerged as a significant framework for …

We initiate a principled study of algorithmic collective action on digital platforms that deploy
machine learning algorithms. We propose a simple theoretical model of a collective …

Machine learning systems are deployed in critical settings, but they might fail in unexpected
ways, impacting the accuracy of their predictions. Poisoning attacks against machine …

Motivated by the extensive documented disparate harms of artificial intelligence (AI), many
recent practitioner-facing reflective tools have been created to promote responsible AI …

Algorithmic discrimination has become one of the critical points in the discussion about the
consequences of an intensively datafied world. While many scholars address this problem …

Research in adversarial machine learning has shown how the performance of machine
learning models can be seriously compromised by injecting even a small fraction of …

There have been increasing calls for centering impacted communities–both online and
offline–in the design of the AI systems that will be deployed in their communities. However …