I am an independent machine learning researcher, with a special interest in algorithmic fairness and the author of Mitigating Bias in Machine Learning.

Machine Learning Research

  • I believe that conceptually, individual fairness [1], [2] provides a blueprint for how we should be thinking about fairness of predictive algorithms. I’ve reconciled conflicting research on its meaning and behaviour.
  • An important point of contention among researchers is on the existence of an inherent trade-off between individual and group fairness [3], [4]. I clarify the notion of individual fairness as an extension of group fairness, rather than acting in opposition to it. I have proven that existing empirical evidence of a trade-off between individual and group fairness [5] in fact demonstrates the well understood trade-off between utility and fairness.
  • I’m interested in generalised entropy indices as utility / objective functions and how different choices of parameters might provide better measures of utility across both groups and individuals.
  • Mapping philosophical notions of fairness to mathematical criteria.
  • Mathematically rigorous approaches to tackling questions about fairness.

Data Science

Prior to becoming a full time researcher, I was a Principal Data Scientist at Doma (fka States Title)  working on title risk products.

Before this, I was an Artificial Intelligence Fellow at Insight Data Science. While there, I developed DebiasML, a practical yet effective methodology for reducing bias in Machine Learning algorithms. 

Derivatives Quant (Model Risk)

Before transitioning to Data Science, I spent over a decade in the banking sector. I was a Derivatives Quant, managing pricing model risk at the Royal Bank of Scotland (RBS), as part of their independent model validation team. My work involved developing and reviewing pricing models, quantifying product specific valuation risk, devising mitigation strategies, designing model governance processes and standards and more. My role was cross functional, working with Trading, Market Risk, Finance, Auditors and Regulators.

Applied Mathematics

I have a Doctorate (Ph.D.) in Fluid Dynamics (specifically looking at supersonic vortex breakdown), a Masters (M.Sc.) in Theoretical and Applied Fluid Dynamics and a Bachelors in Mathematics (B.Sc. Hons). I studied at the School of Mathematics at The University of Manchester – where Osborne Reynolds (of Reynold’s number fame) spent his career and Alan Turing formulated the Turing test and helped develop the Manchester Computers (the world’s first stored-program and transistorised computer).


  1. Aristotle, Nicomachean Ethics, V.3. 1131a10–b15; Politics, III.9.1280 a8–15, III. 12. 1282b18–23
  2. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel. Fairness through awareness, 2011.
  3. Arvind Narayanan. “Tutorial: 21 fairness definitions and their politics”. 2018.
  4. Reuben Binns. “On the Apparent Conflict Between Individual and Group Fairness.” 2019.
  5. Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar. A unified approach to quantifying algorithmic unfairness. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, July 2018.