Restricting algorithms to limit their powers of discrimination

From music suggestions to help with medical diagnoses, population surveillance, university selection and professional recruitment, algorithms are everywhere, and transform our everyday lives. Sometimes, they lead us astray.

At fault are the statistical, economic and cognitive biases inherent to the very nature of the current algorithms, which are supplied with massive data that may be incomplete or incorrect. However, there are solutions for reducing and correcting these biases. Stéphan Clémençon and David Bounie, Télécom ParisTech researchers in machine learning and economics, respectively, recently published a report on the current approaches and those which are under exploration.

Read on