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Thesis

Interpretable causal systems: interpretability and causality in machine learning for human and nonhuman decision-making

Abstract:

In order to integrate machine learning into human decision-making in a useful way, we must trust machine learning systems enough in our reasoning processes. To evaluate a system’s trustworthiness, humans naturally seek interpretable causal systems to understand outcomes, make decisions, and integrate feedback. This thesis presents four explorations into interpretable causal systems, progressing from associational interpretability up the “Ladder of Causation” to counterfactual representatio...

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Research group:
Machine Learning Research Group
Oxford college:
Balliol College
Role:
Author

Contributors

Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Exeter College
Role:
Supervisor
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Sub department:
Statistics
Oxford college:
Jesus College
Role:
Examiner
Institution:
University of Cambridge
Role:
Examiner
More from this funder
Funding agency for:
Graham, L
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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