A recent wave of research has attempted to define fairness quantitatively. In particular, this
work has explored what fairness might mean in the context of decisions based on the …

Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the
world are engaged in building a governance regime around it. What exactly the “it” is that is …

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

As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

Academic and policy proposals on algorithmic accountability often seek to understand
algorithmic systems in their socio-technical context, recognising that they are produced by …

There has been rapidly growing interest in the use of algorithms in hiring, especially as a
means to address or mitigate bias. Yet, to date, little is known about how these methods are …

Artificial intelligence (AI) presents new challenges for the user experience (UX) of products
and services. Recently, practitioner-facing resources and design guidelines have become …

Health systems rely on commercial prediction algorithms to identify and help patients with
complex health needs. We show that a widely used algorithm, typical of this industry-wide …