I recently spoke on a panel at Strata called ‘When good algorithms go bad’, organised by the good people at DataKind and sponsored by Pivotal Labs and O’Reilly Media. My co-panellists were Madeleine Greenhalgh (Cabinet Office), Shahzia Holtom (Pivotal Labs), and Hetan Shah (Royal Statistical Society), with Duncan Ross (DataKind) chairing what was a very stimulating discussion about the ethics of data science.
I won’t attempt to summarise the excellent contributions of the other speakers (and the audience), but here are some relevant (I hope!) references for those interested in delving further into some of the topics raised:
- “Big Data’s Disparate Impact,” by Solon Barocas and Andrew Selbst: This paper from 2014 provides a clear technical introduction to the problem of disparate impact arising from machine learning
- The Fairness and Accountability in Machine Learning (FAT-ML) workshop is held annually and is a great resource for cutting-edge research in this area.
- Suresh Venkatasubramanian’s reflections on the 2nd FAT-ML
- If you happen to be going to KDD2016, there will be a tutorial on Algorithmic bias: from discrimination discovery to fairness-aware data mining, which will cover many of the techniques that can be used to both detect and prevent discrimination in machine learning.
Many thanks to DataKind UK for organising and inviting me!