Explanations are an integral part of human behavior: people provide explanations to justify choices and actions, and seek explanations to understand the world around them. The need for explanations extends to technology, as crucial activities and important societal functions increasingly rely on automation. Yet, today’s data is vast and often unreliable and the systems that process data are increasingly complex. As a result, data and the algorithms that process data are often poorly understood, potentially leading to spurious analyses and insights. Many users even shy away from powerful analysis tools whose processes are too complex for a human to comprehend and digest, instead opting for less sophisticated yet interpretable alternatives. The goal of our research is to promote users’ trust in data and systems through the development of data analysis toolsets where explainability and interpretability are explicit goals and priorities of the systems’ function.
- Xiaolan Wang, Laura Haas, and Alexandra Meliou, Explaining Data Integration, IEEE Data Engineering Bulletin, vol. 41, no. 2, June 2018, pp. 47–58.
- Haopeng Zhang, Yanlei Diao, and Alexandra Meliou, EXStream: Explaining Anomalies in Event Stream Monitoring, in 20th International Conference on Extending Database Technology (EDBT), 2017, pp. 156–167.
- Xiaolan Wang, Alexandra Meliou, and Eugene Wu, QFix: Diagnosing errors through query histories, in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2017, pp. 1369–1384.
- Xiaolan Wang, Xin Luna Dong, and Alexandra Meliou, Data X-Ray: A Diagnostic Tool for Data Errors, in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2015, pp. 1231–1245.
- Cibele Freire, Wolfgang Gatterbauer, Neil Immerman, and Alexandra Meliou, A Characterization of the Complexity of Resilience and Responsibility for Self-Join-Free Conjunctive Queries, PVLDB, vol. 9, no. 3, 2015, pp. 180–191.
- Alexandra Meliou, Sudeepa Roy, and Dan Suciu, Causality and Explanations in Databases, PVLDB, vol. 7, no. 13, 2014, pp. 1715–1716 (Tutorial).