My work focuses on philosophical issues that arise when reflecting on natural sciences and technology. Thus far, I've conducted that work under two main headings: Philosophy of Biology; and Philosophy of AI.
In Philosophy of Biology, I've worked on the influence of philosophical commitments on Darwin interpretations (HOPOS, 2018); the interplay and tension between the “natural” and “artificial” (Biology & Philosophy, 2021); biologists' and philosophers' debates about the human “place” in nature (Palgrave, 2016); the legitimacy of typological thinking in biology (Studies C, 2015); the promises and dangers of seeking to ground ethics in biology (IPQ, 2011); and the views of 20th century bio-philosophers such as Helmuth Plessner and Marjorie Grene on organic life and human distinctiveness (IJPS, 2015; Canguilhem & Continental Philosophy 2023).
In Philosophy of AI, my current projects focus on quantitative ethics metrics in AI and machine learning, such as metrics for fairness, privacy, or transparency. Such metrics are the focus of a robust interdisciplinary community of computer scientists, mathematicians, philosophers, and others. I'm interested in evaluating the benefits and costs of such metrics -- both individual metrics in comparison with competitors, and the "metrological" ethics framework as a whole.
In Philosophy of Biology, I've worked on the influence of philosophical commitments on Darwin interpretations (HOPOS, 2018); the interplay and tension between the “natural” and “artificial” (Biology & Philosophy, 2021); biologists' and philosophers' debates about the human “place” in nature (Palgrave, 2016); the legitimacy of typological thinking in biology (Studies C, 2015); the promises and dangers of seeking to ground ethics in biology (IPQ, 2011); and the views of 20th century bio-philosophers such as Helmuth Plessner and Marjorie Grene on organic life and human distinctiveness (IJPS, 2015; Canguilhem & Continental Philosophy 2023).
In Philosophy of AI, my current projects focus on quantitative ethics metrics in AI and machine learning, such as metrics for fairness, privacy, or transparency. Such metrics are the focus of a robust interdisciplinary community of computer scientists, mathematicians, philosophers, and others. I'm interested in evaluating the benefits and costs of such metrics -- both individual metrics in comparison with competitors, and the "metrological" ethics framework as a whole.