Jake Robertson

I'm a Master's student, machine learner, and early-career researcher, fascinated about the legal, ethical, and philosophical intersections of AI and society. Currently I'm working on socio-technical problems in machine learning such as interpretability and fairness. Check out my work below!

Fairness and Interpretability

AIES'22

Aug 2022, Oxford, UK

"A Bio-Inspired Framework for Machine Bias Interpretation," ACM Conference on AI, Ethics, and Society (AIES'22)

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Interpretability and Explainability

GECCO'21

Jul 2021, Lille, France

"An Evolutionary Approach to Interpretable Learning" AAAI/ACM Genetic and Evolutionary Computation Conference (GECCO'21)

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Publications

Jake Robertson, Catherine Stinson, and Ting Hu. 2022. A Bio-Inspired Framework for Machine Bias Interpretation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22), August 1–3, 2022, Oxford, United Kingdom. ACM, New York, NY, USA, 11 pages

Jake Robertson and Ting Hu. 2021. An Evolutionary Approach to Interpretable Learning. In Proceedings of the Genetic and Evolutionary Computation Conference 2021 (GECCO’21), July 10-14, 2021, Lille, France. ACM, New York, NY, USA, 2 pages

Google Scholar

About Me

I finished my Bachelor's of Computing at Queen's University in 2021 and am currently working on my Master's in Computer Science (Artificial Intelligence) at the University of Freiburg, where I work as a research assistant (HiWi) in the Freiburg Machine Learning Lab. The final year of my Master's degree is fully funded by the Konrad Zuse School of Excellence in Learning and Intelligent systems (ELIZA).

Contact Info

  1. University of Freiburg
  2. Faculty of Engineering
  3. Baden-Württemberg, Germany
  4. Email | GitHub | LinkedIn