Publications
- Tsakalakis, N., Stalla-Bourdillon, S., Carmichael, L., Huynh, D., Moreau, L. & Helal, A. (2021). The Dual Function of Explanations: Why Computing Explanations is of Value. In Data Protection and Privacy: Enforcing Rights in a Changing World. Hallinan, D., Leenes, R. & De Hert, P. (eds.). Bloomsbury Publishing, Vol. 14. p. 127-156.
- Tsakalakis, N., Stalla-Bourdillon, S., Carmichael, L., Huynh, T. D., Moreau, L. & Helal, A. (2021). The dual function of explanations: why it is useful to compute explanations. In Computer Law & Security Review 41. doi:10.1016/j.clsr.2020.105527.
- Huynh, T. D., Tsakalakis, N., Helal, A., Stalla-Bourdillon, S., & Moreau, L. (2021). Addressing Regulatory Requirements on Explanations for Automated Decisions with Provenance: A Case Study. In Digital Government: Research and Practice 2, 2. doi:10.1145/3436897.
- Kohan Marzagão, D., Huynh, T. D., Helal, A., Moreau, L. (2020). Provenance Graph Kernel.
- Tsakalakis, N., Stalla-Bourdillon, S., Carmichael, L., Huynh, T. D., Moreau, L. & Helal, A. (2020). The dual function of explanations: Why it is useful to compute explanations. In Computer Law and Security Review.
- Canal, G., Borgo, R., Coles, A., Drake, A., Huynh, T. D., Keller, P., Krivic, S., Luff, P., Mahesar, Q-A., Moreau, L., Parsons, S., Patel, M. & Sklar, E. (2020). Building Trust in Human-Machine Partnerships. In Computer Law & Security Review 39. doi:10.1016/j.clsr.2020.105489.
- Moreau, L., & Huynh, T. D. (Accepted/In press). The PROV-JSONLD Serialization: A JSON-LD Representation for the PROV Data Model. In 8th International Provenance and Annotation Workshop, IPAW 2020.
- Huynh, T. D., Stalla-Bourdillon, S., & Moreau, L. (2019). Provenance-based Explanations for Automated Decisions: Final IAA Project Report.
Talks & Events
- Huynh, T. D., 8 July 2021, PLEAD: Provenance-based Approach to Constructing Explanations for Automated Decisions. At explAIn: Exploring the links between Explainable AI, Causality and Persuasion, Technical Workshop, Imperial College London, United Kingdom.
- Tsakalakis, N., Stalla-Bourdillon, S., Carmichael, L., Huynh, T. D., Moreau, L. & Helal, A., 3 Dec 2020. The dual function of explanations: why it is useful to compute explanations. At Computer Privacy & Data Protection (CPDP) 2021: Enforcing Rights in a Changing World.
- Moreau, L., 23 Nov 2020. Explanations on the Web: a Provenance-based Approach at Expert Seminar Series, University of Warwick, United Kingdom.
- Moreau, L., 5 Nov 2020. Explanations on the Web: A Provenance-based Approach, Keynote lecture at 16th International Conference on Web Information Systems and Technologies (WEBIST 2020). (slides)
- Explanations for AI workshop at the 12th ACM Web Science Conference, 2020.
- Moreau, L., 17 Sep 2020. Provenance: a fundamental data governance tool. A case study for data science pipelines and their explanations at Open Data Science Conference (ODSC Europe 2020), London, United Kingdom.
- Moreau, L., 22 Jun 2020. The PROV-JSONLD Serialization at the 8th International Provenance and Annotation Workshop, IPAW 2020.
- Tsakalakis, N., & Moreau, L., 28 May 2020. PLEAD project presentation at Datum Future‘s Workshop on Supporting Fair & Explainable AI.
- Moreau, L. & Huynh, T. D., 2019. Provenance Tutorial at Open Data Science Conference (ODSC Europe 2019), London, United Kingdom.
- Moreau, L., 30 Oct 2019. Provenance-based Explanations for Automated Decisions at the Mirian Andrés seminars on Data Provenance, University of La Rioja, Spain.
- Moreau, L., 25 Sep 2019. Provenance-based Explanations for Automated Decisions at the Regulators and AI Working Group, Competition and Markets Authority, London, United Kingdom.
Software
- Loan Demonstrator: Provenance-based explanation demonstrator.
- ProvToolbox: A Java library to work with the PROV Data Model.
- PROV Python package: A library to work with the PROV Data Model in Python.
More provenance resources can be found at openprovenance.org.