Our Work
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.
Read MoreIn this talk, I argue for the practical problems of a counterfactual theory of mathematical explanations in sciences.
Read MoreIn this talk, I discuss for the bridging role of mathematics in empirical sciences as a reliable connecting scheme in our explanatory reasoning from lower-level to higher-level phenomena. I support this discussion by analyzing two explanations in biology and physics.
Read MoreIn this talk, I introduce a philosophically-informed framework for the varieties of explanations used for building transparent AI decisions. This paper has been presented at Halıcıoğlu Data Science Institute and Department of Philosophy (University of California San Diego), Department of Philosophy (Stanford University and University of Washington), Department of Logic and Philosophy of Science (University of California, Irvine)
Read MoreIn a counterexample based approach to conformant planning, choosing the right counterexample can improve performance. We formalise this observation by introducing the notion of “superiority” of a counterexample over another one,that holds whenever the superior counterexample exhibits more tags than the latter. We provide a theoretical explanation that supports the strategy of searching for maximally superior counterexamples, and we show how this strategy can be implemented. The empirical experiments validate our approach.
Read MoreI offer a multi-faceted conceptual framework for the explanation and the interpretations of algorithmic decisions, and I claim that this framework can lay the groundwork for a focused discussion among multiple stakeholders about the social implications of algorithmic decision-making, as well as AI governance and ethics more generally.
Read MoreIn October 2019 Seth Lazar visited MIT and Carnegie Mellon University, to present a talk on the value of explanations to philosophers and computer scientists.
Read MoreThe Morality and Machine Intelligence conference brought together academic leaders from institutes across the US, UK and Australia, from philosophy, social science and computer science to openly discuss their latest research on the ethics of machine intelligence.
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