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 is growing concern that decision-making informed by machine learning (ML)
algorithms may unfairly discriminate based on personal demographic attributes, such as …

In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes
mostly focus on detecting harmful algorithmic biases. While these strategies have proven to …

Many high-stakes policies can be modeled as a sequence of decisions along a pipeline. We
are interested in auditing such pipelines for both Our empirical focus is on policy decisions …

As machine learning (ML) algorithms are increasingly used to inform decisions across
domains, there has been a proliferation of literature seeking to define “fairness” narrowly as …

Racial discrimination in housing has long fueled disparities in homeownership and wealth in
the United States. Now, automated algorithms play a dominant role in rental and lending …