Post-doc in Fairness at Data and Society

As part of the research we’re doing in algorithmic fairness we’re looking to hire a post-doctoral researcher who can help us bridge the gap between the more technical aspects of algorithmic fairness and the ways in which this discussion informs and is informed by the larger context in the social sciences. Specifically,

  • Candidates for this position should have a strong grasp of technical systems (including machine learning), as well as a rich understanding of socio-technical discussions. For example, candidates might have an undergraduate degree in computer science and a PhD in a social science field. Or they may have a more hybrid degree in an information school or CS program. They may be a data scientist or study data scientists.
  • Candidates should be able to translate between engineers and critics, feel comfortable at ACM/AAAI/IEEE conferences and want to publish in law reviews or social science journals as well as CS proceedings.
  • Candidates should be excited by the idea of working with researchers invested in fairness, accountability, and transparency in machine learning (e.g.,
  • Preference given for researchers who have qualitative empirical skills.

If you might be such a person, please do send in an application (Role #1).

Data & Society is a wonderful place to be if you’re at all interested in this area. danah boyd has assembled a group of thinkers that represent the best kind of holistic thinking on a topic that intersects CS, sociology, political science and the law.

Algorithmic Fairness at the LSE

In April, I attended (virtually) a workshop organized by the Media Policy Project of the London School of Economics on “Automation, Prediction and Digital Inequalities”. 

As part of the workshop, I was asked to write a “provocation” that I read at the workshop. This was subsequently converted into a blog post for the MPP’s blog, and here it is.

The case I make here (that I will expand on in the next post) is for trying to develop a mathematical framework for thinking about fairness in algorithms. As a computer scientist, this idea seems like second nature to me, but I recognize that to the larger community of people thinking about fairness in society, this case needs to be argued.