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Sara Haijan, Eurecat-Technology center of Catalonia, speaks on Discovery and Prevention of Algorithmic Discrimination and Fairness

The GESIS Computational Social Science (CSS) Seminar is an English monthly event for expert exchange on data science and social analytics organized by the Gesis Leibniz-Institut for Social Sciences. The August edition of the seminar was delivered by Sara Haijan, EURECAT. On 24 August Ms. Haijan gave a talk on the Discovery and Prevention of Algorithmic Discrimination and Fairness.

At the beginning of 2014, as an answer to the growing concerns about the role played by data mining/machine learning algorithms in decision-making, USA President Obama called for a 90-day review of big data collecting and analysing practices. The resulting report concluded that “big data technologies can cause societal harms beyond damages to privacy”. In particular, it expressed concerns about the possibility that decisions informed by big data could have discriminatory effects, even in the absence of discriminatory intent, further imposing less favorable treatment to already disadvantaged groups. In its recommendations to the President, the report called for additional “technical expertise to stop discrimination”, and for further research into the dangers of “encoding discrimination in automated decisions”.

As a result of continuous and sustained efforts to fight discrimination on all fronts, anti-discrimination techniques in data mining have been developed, which come to support and augment those advances in anti-discrimination legislation. While some avenues of action focus on the discovery and measurement of discrimination, others deal with preventing data mining from becoming itself a source of discrimination, due to automated decision making based on discriminatory models extracted from inherently biased datasets. Ms. Haijan provided an overview of the recent techniques for discrimination prevention, simultaneous discrimination and privacy protection, and discrimination discovery and showed some recent results from the field.

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