Sandra Wachter
Professor of Technology and Regulation at the University of Oxford
Last updated 13 Nov 2026 · Named in 11 answers across 4 engines
Bing AI Mode starts a separate baseline. Copilot remains a separate public engine and keeps its history; changes across the switch are not like-for-like growth.
How AI engines see them
All four engines recognize Sandra Wachter as a University of Oxford scholar working on technology law, data ethics and AI regulation, and all tie her research to RegTech compliance through explainability and algorithmic accountability. Copilot adds that she co-developed the Counterfactual Explanations Framework and the Conditional Demographic Disparity bias test, adopted by companies such as Google, IBM and Amazon, and that she advises governments and the World Bank. Gemini notes her work is widely cited in scholarly literature. No engine failed to name her. The pages cited most often come from oii.ox.ac.uk, followed by law.ox.ac.uk and wikipedia.org.
- ChatGPTNot named
Sandra Wachter is Professor of Technology and Regulation at the University of Oxford and a scholar of the law, ethics and governance of artificial intelligence.
Known for- law, ethics and governance of AI
- legally compliant algorithmic systems
- explainable, fair, accountable algorithms
- GeminiNot named
Sandra Wachter is a leading authority on technology law, data ethics and AI regulation whose research underpins regulatory technology and compliance frameworks.
Known for- counterfactual explanations
- explainable AI for black box compliance
- AI regulation and data ethics
- Google AI ModeNot named
Sandra Wachter is a leading scholar in technology and regulation, data ethics and AI governance at the Oxford Internet Institute.
Known for- explainable AI
- right to reasonable inferences
- algorithmic bias and fairness
- PerplexityNot named
No description yet.
- Bing AI ModeNot named
No description yet.
- CopilotNot named
Sandra Wachter is a leading authority on technology, law and compliance whose work gives compliance teams frameworks to audit, explain and mitigate algorithmic bias.
Known for- counterfactual explanations framework
- Conditional Demographic Disparity bias test
- policy advisory on AI regulation
Top Segments
- #44AI Regulationof 81Named in 9 answers
- #21Data Privacyof 54Named in 3 answers
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