Interpretable causal systems: interpretability and causality in machine learning for human and nonhuman decision-making
<p>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...
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| Format: | Thesis |
| Language: | English |
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2020
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