I attended CAp for the first time, a good opportunity to discover several French Machine Learning teams. I did an oral presentation of the paper Privacy amplification by decentralization.

I was the only woman with oral presentation of paper in this conference.

Two years after my work on the predictive justice software Compas during my Philosophy Degree, I wanted to summarize my takeaways from this use case, using also my new experience coming from my PhD team that works on fairness as well. Alain Desrosières was a statistician who succeeds in highlighting how the processus of getting numbers used for the statistics and how they are performed embed political decisions. Numbers are built, and thus come from choices which relevance can be discussing by opening the black lox of their creation and analysis.

Even if I believe that the content of his book La politique des grands nombres is integrated to the culture of every good engineer, this book is a seminal work when one wants to deepen the relationship between social impact and data-based learning. This workshop was the opportunity to see how sociologists and some of his former colleague approach his work.

My contribution was on the controversy on racial bias in Compas, and how it has been quantified by the company selling the software, by the newspaper Propublica and by machine learning community on fairness, especially pointing shortcomings on the use of biased dataset for checking bias.

I was invited to give a talk my paper Privacy Amplification by Decentralization at the seminar of the Argo team at Inria Paris.

I was invited to give a talk on a short introduction to Differential Privacy and my paper Privacy Amplification by Decentralization at the seminar of the MLO team.

In line with last year event, I gave a talk to this workshop. The goal is to widen our understanding of algorithms to face the challenges raised by the diversity of algorithms currently deployed. The discussion between lawyers, logicians, machine learning users and philosophers allows to question our practices and bias in how we define the concept of algorithm.

Abstract The recent advances of technology, that make the storage and the processing of large amount of data affordable, drove up the collection of sensitive data. For instance, a smartphone can track its owner position, her sport activity, her messages, her photos, her queries and browsing. Data leakage, malicious or not, is thus a burning issue of the digital era. How can we guarantee privacy, this slippery concept on the fringe of obfuscation, unlinkability, anonymity, confidentiality and data minimization ? Differential privacy is currently the gold standard both in research and industry for machine learning applications. It quantifies the privacy loss occurring during the use of a record, by synthesizing its impact in a scalar. This presentation addresses how the definition was introduced and its implicit assumptions. We see how the context of digitization induces a shift in the privacy protection and test the limit of differential privacy through its variants and real-world implications, connecting it with regulations and other notions of protection.