Will Bateman presented ‘COVID, Artificial Intelligence and litigation against the government’ at the ACT Law Society on the 30th of September 2020. Click through for more information.
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At HawaiiCon 2020, Claire Benn discussed her chapter ‘Playtest and the Power of Virtual Reality: Are Our Fears Real?’ which is part of the recently released Black Mirror and Philosophy: Dark Reflections book. Click through for more information.
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Atoosa 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.
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Dr Will Bateman presented ‘Mechanical Cognition, Determinism and Individual Justice: New Challenges for Administrative Law from AI’ at a lunchtime webinar on the 13th of October 2020. The event was chaired by Paul Pfitzner. Click through for more information.
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A 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.
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One of the open questions in Bayesian epistemology is how to rationally learn from indicative conditionals (Douven, 2016). Eva et al. (2019) propose a strategy to resolve this question. They claim that their strategy provides a "uniquely rational response to any given learning scenario". We show that their updating strategy is neither very general nor always rational. Even worse, we generalize their strategy and show that it still fails. Bad news for the Bayesians.
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Present 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.
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Michael 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.
I discussed the pitfalls of over- and underestimating the applicability of modelling results to understanding real world epistemic communities, and how to have an appropriate epistemic attitude towards them.
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I use category theory, the mathematical theory of structure, to illuminate the ways in which mathematical formalisms are used to represent real-world systems.
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In this talk, Atoosa offered a multi-faceted unifying theory for the varieties of explanations as to why a prediction-based algorithmic decision is obtained. This framework lays the groundwork for establishing the relevant connection between technical, moral, and legal aspects of artificially-intelligent decision-making.
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