Augustin Chaintreau

Mathematics, and scientifically reproducible datasets, are critical not only for algorithms to improve for all, but also to guide society on the ethical and economic consequences of differentiated treatments led by automatic decisions. Chaintreau joined Columbia after five years of research in industry; since then he works only with public and non-profit funds. Scientists and engineers in industry labs are however frequent collaborators in the joint release of data, tools, or publication of results, as well as investigative journalists.

Today’s big data is flawed, and the threats it poses are not theoretical: Chaintreau has proved with reproducible experiments that personalization algorithms in services used by millions pose moral hazards. His measurements have revealed that metrics of social endorsement misrepresent online attention. As our lives become more digital, Chaintreau has showed that the network dynamics facilitated by online interactions and sharing economies sometimes organically exacerbates various inequalities. Personal information collection and usage, however, ultimately bring benefits that we cannot forego, in various domains such as health, energy efficiency, and public policies. While at first glance, those problems appear embedded in the fabric of Big Data, Chaintreau's work has shown, on the contrary, that these trends can be reversed by addressing some of their common root causes.