Recent years have seen a rapid increase in the use of behavioural science to address the
priorities of public and private sector actors. There is now a vibrant ecosystem of …

It has been the historic responsibility of the social sciences to investigate human societies.
Fulfilling this responsibility requires social theories, measurement models and social data …

Machine learning currently exerts an outsized influence on the world, increasingly affecting
institutional practices and impacted communities. It is therefore critical that we question …

Natural language computer applications are becoming increasingly sophisticated and, with
the recent release of Generative Pre-trained Transformer 3, they could be deployed in …

Advances in algorithmic fairness have largely omitted sexual orientation and gender identity.
We explore queer concerns in privacy, censorship, language, online safety, health, and …

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

Previous work has largely considered the fairness of image captioning systems through the
underspecified lens of “bias.” In contrast, we present a set of techniques for measuring five …

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …

What does it mean for a clustering to be fair? One popular approach seeks to ensure that
each cluster contains groups in (roughly) the same proportion in which they exist in the …

Arguments that machine learning (ML) is facing a reproducibility and replication crisis
suggest that some published claims in research cannot be taken at face value. Concerns …