Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

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 …

The topic of algorithmic fairness is of increasing importance to the Human–Computer
Interaction research community following accumulating concerns regarding the use and …

Fairness, accountability, and transparency are the three conceptual foundations of the
FAccT conference. Transparency, however, has yet to be scrutinized to the same degree as …

In this paper, we argue that we cannot expect that AI systems—even given more data or
better computational resources—will be more ethical than the humans who develop, deploy …

Plural counterfactual examples have been proposed to explain the prediction of a classifier
by offering a user several instances of minimal modifications that may be performed to …

Ethical principles for algorithms are gaining importance as more and more stakeholders are
affected by" high-risk" algorithmic decision-making (ADM) systems. Understanding how …

Artificial intelligence (AI) approaches, such as deep learning models, are increasingly used
to determine risks in construction. However, the black-box nature of AI models makes their …

Social media feeds are deeply personal spaces that reflect individual values and
preferences. However, top-down, platform-wide content algorithms can reduce users' sense …

Recently substantial improvements in neural retrieval methods also bring to light the
inherent blackbox nature of these methods, especially when viewed from an explainability …