Our Work
The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender.
Read MoreOn December 12 2020, Atoosa Kasirzadeh and Andrew Smart will present their paper ‘A critique of the use of counterfactuals in ethical machine learning’ at the Virtual NeurIPS 2020 Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability.
Read MoreIn this QuantumBlack Australia virtual Meetup, the ethics of artificial intelligence was discussed with the Gradient Institute and HMI. Click through for more information.
Read MoreAtoosa presented her paper ‘A philosophical theory of AI explanations’ to an academic audience at UC Berkeley. Click through for more information.
Read MoreAtoosa Kasirzadeh gave a talk on ‘The Use and Misuse of Counterfactuals in Fair Machine Learning’ at the Virtual Workshop on the Philosophy of Medical AI, hosted by University of Tübingen in October 2020.
Read MoreAnthony Asher, Adam Druissi, Seth Lazar, and Tiberio Caetano presented the online seminar ‘Data Ethics — A Virtual Session’ on the 13th of October 2020. Click through for more information.
Read MoreA paper workshop that was co-hosted by Dr Damian Clifford RF of the HMI project at ANU, and Prof Jeannie Paterson, co-Director, CAIDE at University of Melbourne. Click through for more information or view a draft agenda here.
Read MorePresent some of our work in developing practical solvers for the Partially Observable Markov Decision Process (POMDP) with applications in robotics. I enjoy the discussion in the seminar.
Read MoreMichael gave a talk to a Canberra Meetup on responsible AI, setting machine learning in context and asking how it might help us to improve how we think about ethics.
