Description
Book SynopsisPart I: Epistemic opacity.- 1 In Which Ways is Machine Learning Opaque? (Claus Beisbart).- 2 How I Stopped Worrying and Learned to Love Opacity (Nico Formanek).- 3 Epistemic opacity and scientific realism and anti-realism (Jack Casey).- Part II: Justification.- 4 Beyond transparency: computational reliabilism as an externalist epistemology for algorithms (Juan M. Durán).- 5 Challenges for Computational Reliabilism: Epistemic Warrants, Endogeneity and Error-Based Opacity in Machine Learning (Ramón Alvarado).- 6 Can XAI Justify? (Carlos Zednik, Philippe Verreault-Julien).- Part III: Scientific Explanation (XAI) .- 7 Axe the X in XAI: A Plea for Understandable AI (Andrés Páez).- 8 Machine Learning models as Mathematics (Stefan Buijsman).- 9 From Explanations to Interpretability and Back (Tim Räz).- Part IV: Scientific Understanding and Interpretability.- 10 Explanation hacking: The Perils of Algorithmic Recourse (Emily Sullivan, Atoosa Kasirzadeh).- 11 Stakes and Understanding the Decisions of Artificial Intelligent Systems (Eva Schmidt).- Part V: Scientific Models and Representation.- 12 Representation Learning Without Representationalism. A Non-Representationalist Account of Deep Learning Models in Scientific Practice (Phillip Hintikka Kieval).- 13 Artificial Neural Nets and the Representation of Human Concepts (Timo Freisleben).- 14 Defining Formal Validity Criteria for Machine Learning Models (Chiara Manganini, Giuseppe Primiero).- Part VI: Scientific practice and scientific values in ML.- 15 Why are Human Epistemic Agents not Displaced in Machine Learning Scientific Inquiries? (Sahra A. Styger, Marianne de Heer Kloots, Oskar van der Wal, and Federica Russo).- 16 Values, Inductive Risk, and Societal-Epistemic Coupledness in Machine Learning Models (Milou Jansen, Koray Karaca).- 17 Machine Learning and the Ethics of Induction (Emanuele Ratti).- Part VII: ML in the Particular Sciences.- 18 Beyond Classification and Prediction: The Promise of Physics-Informed Machine Learning in Astronomy and Cosmology (Helen Meskhidze).- 19 Machine Learning Discoveries and Scientific Understanding in Particle Physics: Problems and Prospects (Florian J. Boge and Henk W. de Regt).- 20 Don’t Fear the Bogeyman: On Why There is no Prediction-Understanding Trade-Off for Deep-Learning in Neuroscience (Barnaby Crook, Lena Kästner).- 21 Artificial Intelligence in Climate Science: From Machine Learning to Neural Networks (Greg Lusk).- 22 Machine Learning in Public Health and the Prediction-Intervention Gap (Thomas Grote, Oliver Buchholz).
