Mathematizing fairness? On statistical metrics of algorithmic fairness
Both the lecture and the discussion will be in English. It will start at 5 PM (Paris time), on Monday, November 15, 2021.
Please register (for this and for the future meetings of the seminar) here:
One of the great topic of the AI ethics literature has been the discussion of possible metrics of algorithmic fairness. Those are statistical metrics designed to determine whether the input-output behavior of a given model exhibits biases towards a given population. The topic has grown in relevance as several early mathematical results, called “incompatibility results”, demonstrated the impossibility of a simultaneous satisfaction of several current metrics, even when those seem both natural and desirable. In this talk, we will tackle two philosophical issues. The first issue is the exact status of those metrics, and hence of incompatibility results: are we dealing with definitions or simple indicators? Should we consider that we face several competing definitions, or should we defend a form of pluralism? The second issue, structurally tied to the first one, bears on the risk of bureaucratization of fairness issues through the use of those metrics: what are the risks of abusive reduction of the difficult issues raised by (algorithmic) discrimination to the simple satisfaction of a metric?