We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and
reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is …

Understanding the landscape of potential harms from algorithmic systems enables
practitioners to better anticipate consequences of the systems they build. It also supports the …

One of the most concrete measures towards meaningful AI accountability is to
consequentially assess and report the systems' performance and impact. However, the …

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

Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1)
competence, 2) transparency, and 3) fairness. However, end-users may fail to recognize …

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 …

As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important
to consider AI's ethical and societal implications. In this paper, we present a bottom-up …

Algorithmic audits are increasingly used to hold people accountable for the algorithms they
implement. However, much work remains to integrate ethical and legal evaluations of how …

Organisations generate vast amounts of information, which has resulted in a long-term
research effort into knowledge access systems for enterprise settings. Recent developments …

A large body of research has attempted to ensure that algorithmic systems adhere to notions
of fairness and transparency. Increasingly, researchers have highlighted that mitigating …