NeurIPS 2020 requested that research paper submissions include impact statements on"
potential nefarious uses and the consequences of failure." However, as researchers …

While demands for change and accountability for harmful AI consequences mount,
foreseeing the downstream effects of deploying AI systems remains a challenging task. We …

As algorithms have become ubiquitous in consequential domains, societal concerns about
the potential for discriminatory outcomes have prompted urgent calls to address algorithmic …

The computer science research community and the broader public have become
increasingly aware of negative consequences of algorithmic systems. In response, the top …

As AI systems proliferate in society, the AI community is increasingly preoccupied with the
concept of AI Safety, namely the prevention of failures due to accidents that arise from an …

Generative AI systems produce a range of ethical and social risks. Evaluation of these risks
is a critical step on the path to ensuring the safety of these systems. However, evaluation …

Generative AI systems produce a range of risks. To ensure the safety of generative AI
systems, these risks must be evaluated. In this paper, we make two main contributions …

Deployed AI systems often do not work. They can be constructed haphazardly, deployed
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …

As AI systems are integrated into high stakes social domains, researchers now examine how
to design and operate them in a safe and ethical manner. However, the criteria for identifying …

This paper reviews algorithmic or artificial intelligence (AI) bias in education technology,
especially through the lenses of speculative fiction, speculative and liberatory design. It …