Fairness in algorithmic decision-making

We were tasked with evaluating fairness in its myriad forms, and mapping the various expressions of fairness to the data science workflow. Accenture challenged us to aggregate and organise the elements of the fairness literature into a manageable structure, and to provide meaningful visualisations that facilitate productive discussions around fairness in an analytical project. In this study, we focus on financial services and, in particular, on credit allowance in retail banking, where there is a prevalence of algorithms impacting customers and the services they are able to receive.

Citation information

Data Study Group team. (2019, February 5). Data Study Group Final Report: Accenture. Zenodo. http://doi.org/10.5281/zenodo.2557795

Additional information

Paul-Marie Carfantan, LSE
Omar Costilla-Reyes, MIT
Delia Fuhrmann, University of Cambridge
Jonas Glesaaen, Swansea University
Qi He, UCL
Andreas Kirsch, University of Oxford
Julie Lee, UCL
Mohammad Malekzadeh, Queen Mary University of London
Esben Sørig, Goldsmiths, University of London.
Caryn Tan, Accenture
Emily Turner, University of Manchester
Quang Vinh, Inria Nancy Grand-Est