Klein, ColinKasirzadeh, Atoosa2023-03-302023-03-30May 19 - 2978-1-4503-8473-5http://hdl.handle.net/1885/287889Computers are used to make decisions in an increasing number of domains. There is widespread agreement that some of these uses are ethically problematic. Far less clear is where ethical problems arise, and what might be done about them. This paper expands and defends the Ethical Gravity Thesis: ethical problems that arise at higher levels of analysis of an automated decision-making system are inherited by lower levels of analysis. Particular instantiations of systems can add new problems, but not ameliorate more general ones. We defend this thesis by adapting Marr's famous 1982 framework for understanding information-processing systems. We show how this framework allows one to situate ethical problems at the appropriate level of abstraction, which in turn can be used to target appropriate interventions.This project was supported by the Humanising Machine Intelligence Grand Challenge at the Australian National Universityapplication/pdfen-AU© 2021 copyright held by the owners/author(s)https://creativecommons.org/licenses/by-nc-nd/4.0/Ethics of Artificial IntelligencePolitics of Artificial IntelligenceEthical Artificial IntelligenceEthical Machine LearningAlgorithmic BiasAlgorithmic FairnessJusticePhilosophy of Artificial Intelligence202110.1145/3461702.34626062022-01-16