Guest lecture: Vassilis Christophides

Data Ethics in Algorithmic Decision Making

27.02.2020 - 27.02.2020

Vassilis Christophides, professor of Computer Science at the University of Crete invited by laboraory ETIS.

Machine Learning (ML) algorithms typically operate by learning patterns in available data and generalizing them to unseen data. There is growing recognition that even ML models developed with the best of intentions may exhibit discriminatory biases, perpetuate inequality, or perform less well for historically disadvantaged groups. Harms to particular individuals or groups are essentially caused by “biased data” a notion that encompass many forms of “bugs” specific to data-driven decision systems. In this talk, we are presenting statistics and causal analytics approaches to unveil discrimination practices in high-stake applications like criminal justice and predictive policing, credit-worthiness and loans, etc. We survey current research efforts on ethical ML balancing between fairness and accuracy of predictions. We acknowledge the fact that “biased data” are often due to various imperfections arising during data collection or data processing.  Then, we are advocating the need to detect, report and prevent data ethics issues at the earliest possible stage of the pipelines used to build ML models. This call for tools to diagnose whether a given fairness issue might be addressed by collecting more training data from a particular subpopulation or by better cleaning existing training data and to predict how much more data are need to gather or to repair. We conclude with few preliminary ideas on how we can actively guide upstream data cleaning to jointly optimize fairness and accuracy of downstream ML models.

Download his CV

Date: Thursday 27th February from 12:30 to 14:00

Venue: Maison internationale de la recherche, Neuville-sur-Oise. Access to the MIR

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