Tractable Probabilistic Models for Ethical AI

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Among the many ethical dimensions that arise in the use of ML technology, three stand out as immediate and profound: enabling the interpretability of the underlying decision boundary, addressing the potential for learned algorithms to become biased against certain groups, and capturing blame and responsibility for a system's outcomes. In this talk, we advocate for a research program that seeks to bridge tractable (probabilistic) models for knowledge acquisition with rich models of autonomous agency that draw on philosophical notions of beliefs, intentions, causes and effects.
Original languageEnglish
Title of host publicationGraph-Based Representation and Reasoning
EditorsTanya Braun, Diana Cristea, Robert Jäschke
PublisherSpringer International Publishing
Number of pages6
ISBN (Electronic)978-3-031-16663-1
ISBN (Print)978-3-031-16662-4
Publication statusPublished - 11 Sept 2022
Event27th International Conference on Conceptual Structures - Münster, Germany
Duration: 12 Sept 202215 Sept 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Conference on Conceptual Structures
Abbreviated titleICCS 2022
Internet address


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