Algorithmic decision-making increasingly shapes people's daily lives. Given that such
autonomous systems can cause severe harm to individuals and social groups, fairness …

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

Many researchers motivate explainable AI with studies showing that human-AI team
performance on decision-making tasks improves when the AI explains its recommendations …

The increased use of algorithmic predictions in sensitive domains has been accompanied
by both enthusiasm and concern. To understand the opportunities and risks of these …

Recent work in HCI suggests that users can be powerful in surfacing harmful algorithmic
behaviors that formal auditing approaches fail to detect. However, it is not well understood …

Research exploring how to support decision-making has often used machine learning to
automate or assist human decisions. We take an alternative approach for improving decision …

The uptake of artificial intelligence-based applications raises concerns about the fairness
and transparency of AI behaviour. Consequently, the Computer Science community calls for …

The social computing community has demonstrated interest in the ethical issues sometimes
produced by machine learning (ML) models, like violations of privacy, fairness, and …

A key insight from research on organizational justice is that fairness is in the eye of the
beholder. With increasing discussions–especially among computer scientists and …

The AI Ethics community faces an imperative to empower stakeholders and impacted
community members so that they can scrutinize and influence the design, development, and …