Edwige Cyffers

Phd Student in Privacy Preserving Machine Learning

French Delegate at Y20

Make hear the voice of Young people on Innovation, Digitalization and Future of Work

I was selected as French delegate in the track Innovation, Digitalization and Future of Work. The Y20 is the official youth engagement group for the G20. The twenty delegates of each track work during several months to establish common recommendations to the G20. The communiqué is announced publicly at the Y20 Summit and presented to world leaders as part of the official G20 summit.

Final communique

Artificial Intelligence and Agnostic Science

Differential Privacy: a new notion of the privacy for a new paradigm of data?

The recent advances of technology, that make the storage and processing of large amount of data affordable, have led to increasing collect of sensitive data. A consequence of the Big Data phenomenon is that the traditional definition of what is personal information, used to define boundary of privacy, seems inaccurate. Indeed, “anonymized” dataset can be massively re-identify, and increases further desanonymization in a vicious circle. Differential Privacy has been proposed as a more relevant way ta measure to what extent a contribution to a database threatens the individual, and it has become the gold standard in Machine Learning research. I will briefly present the key points of the definition, highlight its strengths and the issues that are imposed by this framework.

Official page of the conference: SCAI


Philosophical work on COMPAS

What place for a software that predicts recidivism in the US justice ?

Compas is a popular risk assessment tool used by several states in the United States of America as a piece of information for criminal sentencing. We discuss its legitimacy in the judicial framework. As it relies on machine learning, which use big data to optimize a predictive model, specific questions of fairness arise. It has a cost in terms of transparency and bias might come from the racial bias of the past and current society that generates the training data. We study these questions and show that awareness is welcome to avoid the amplification of norms based on statistics, as it might not be compatible with human notion of justice.

Ask for full text (French).

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