Background materials
Datasets & Methods
- CHALLENGE DESCRIPTION
- An integrated library for explanation methods: XAI-Lib
- U.S. Amazon purchases histories with user demographics
- A survey on machine learning methods for churn prediction
- Causal Inference and Uplift Modelling: A Review of the Literature
Videos & Slides
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In this talk given at “the Digital Futures: Research Hub for Digitalization”, Fosca Giannotti gives an overview on how explainable AI works.
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In this talk given at “MALOTEC Seminar”, Riccardo Guidotti presents an approach that allows to measure to which extent the explanations returned by local explanation methods are correct with respect to a synthetic ground truth explanation. Indeed, the proposed methodology enables the generation of synthetic transparent classifiers for which the reason for the decision taken, i.e., a synthetic ground truth explanation, is available by design.
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In this talk given at “Stanford Seminar”, Vera Liao highlights the central role that human-centered approaches should play in shaping XAI technologies, including driving technical choices by understanding users’ explainability needs, uncovering pitfalls of existing XAI methods, and providing conceptual frameworks for human-compatible XAI.
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In this talk given at “Dis22”, Virginia Dignum spoke about Ethical and Responsible Artificial Intelligence.
