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Gillian Hadfield

Gillian Hadfield is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Gillian Hadfield rather than just read about it. In short: Gillian Kereldena Hadfield (born July 14, 1961) is a Canadian economist, legal scholar and artificial intelligence researcher who is the Bloomberg Distinguished Professor of AI Alignment and Governance. She is also Professor of Law and of Strategic Management at Toronto, Canada CIFAR AI Chair at the Vector Institute, and an AI2050 Senior Fellow.

Gillian Hadfield — main illustration
Gillian Hadfield — illustration

Key takeaways

  • Gillian Hadfield belongs to astronomy; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Gillian Hadfield to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Gillian Hadfield from memory before moving on to harder problems.

Reference excerpt

Gillian Kereldena Hadfield (born July 14, 1961) is a Canadian economist, legal scholar and artificial intelligence researcher who is the Bloomberg Distinguished Professor of AI Alignment and Governance. She is also Professor of Law and of Strategic Management at Toronto, Canada CIFAR AI Chair at the Vector Institute, and an AI2050 Senior Fellow. From 2018 to 2023, Hadfield served as Senior Policy Adviser to the artificial intelligence company OpenAI. She was previously the director and eponymous chair of the Schwartz Reisman Institute for Technology and Society at the University of Toronto Faculty of Law and the Richard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics at the University of Southern California. At USC, Hadfield directed the Southern California Innovation Project and the USC Center in Law, Economics, and Organization. She is a former member of the board of directors for the American Law and Economics Association and the International Society for New Institutional Economics.

Education and early career Hadfield received her BA with honours in economics from Queen's University in 1983. She earned a JD with distinction from Stanford Law School in 1988 and a PhD in economics from Stanford University in 1990. Following law school, Hadfield clerked for Judge Patricia M. Wald of the U.S. Court of Appeals for the District of Columbia Circuit.

Academic career Hadfield joined the faculty of the UC Berkeley School of Law as an assistant law professor in 1990. From 1994 to 1999, Hadfield was an associate law professor at the University of Toronto Law School, and then a professor of law from 1999 to 2001. Hadfield also served as a professor with NYU School of Law's Global Law Faculty from 1999 to 2001. Hadfield moved to the USC Gould School of Law in 2001, where she was appointed the Richard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics at the University of Southern California, serving in the role to 2018. In 2016, she was the Daniel R. Fischel and Sylvia M. Neil Distinguished visiting professor of Law at the University of Chicago Law School. In 2010, Hadfield was the Sidley Austin Visiting Professor at Harvard Law School, and in 2008, was the Justin W. D'Atri Visiting Professor of Law, Business, and Society at Columbia Law School. In 2006–2007 and 2010–2011, Hadfield served as a fellow of the Center for Advanced Study in the Behavioral Sciences at Stanford University, and in 1993, served as a National Fellow at the Hoover Institution. In 2018, Hadfield rejoined the University of Toronto and in 2019 was appointed the Schwartz Reisman Chair in Technology and Society, as well as the director of the Schwartz Reisman Institute for Technology and Society. Hadfield served as Senior Policy Adviser to OpenAI from 2018 to 2023. While at OpenAI, Hadfield proposed "regulatory markets, in which governments require the targets of regulation to purchase regulatory services from a private regulator" as a new form of regulation for the AI industry. In 2024, Hadfield joined Johns Hopkins University as Bloomberg Distinguished Professor of AI Alignment and Governance holding joint appointments in the School of Government and Policy and the Department of Computer Science in the Whiting School of Engineering. She is currently the Principal Investigator of the Normativity Lab.

Publications Hadfield's work is widely published in law journals, including the Stanford Law Review, and in peer-reviewed journals, including the Annals of Internal Medicine, the Journal of Comparative Economics, the Journal of Economic Behavior and Organization, and the Annual Review of Law and Social Science.

Hadfield, Gillian K. (1990). "Problematic Relations: Franchising and the Law of Incomplete Contracts". Stanford Law Review. 42 (4): 927–992. doi:10.2307/1228908. ISSN 0038-9765. JSTOR 1228908. The second wave of law and economics. Megan Richardson, Gillian K. Hadfield. Leichhardt, N.S.W.: Federation Press. 1999. ISBN 1-86287-316-X. OCLC 59512709.{{cite book}}: CS1 maint: others (link) Hadfield, Gillian K. (2008). "Legal Barriers to Innovation: The Growing Economic Cost of Professional Control over Corporate Legal Markets". Stanford Law Review. 60 (6): 1689–1732. ISSN 0038-9765. JSTOR 40040424. Hadfield, Gillian K.; Weingast, Barry R. (May 11, 2014). "Microfoundations of the Rule of Law". Annual Review of Political Science. 17 (1): 21–42. doi:10.1146/annurev-polisci-100711-135226. ISSN 1094-2939. Hadfield, Gillian K. (2017). Rules for a flat world: why humans invented law and how to reinvent it for a complex global economy. New York, NY. ISBN 978-0-19-991653-5. OCLC 950084369.{{cite book}}: CS1 maint: location missing publisher (link) Hadfield-Menell, Dylan; Hadfield, Gillian K. (January 27, 2019). "Incomplete Contracting and AI Alignment". Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. ACM. pp. 417–422. arXiv:1804.04268. doi:10.1145/3306618.3314250. ISBN 978-1-4503-6324-2. Dafoe, Allan; Bachrach, Yoram; Hadfield, Gillian; Horvitz, Eric; Larson, Kate; Graepel, Thore (2021). "Cooperative AI: machines must learn to find common ground". Nature. 593 (7857): 33–36. Bibcode:2021Natur.593...33D. doi:10.1038/d41586-021-01170-0. PMID 33947992. S2CID 233740521. Köster, Raphael; Hadfield-Menell, Dylan; Everett, Richard; Weidinger, Laura; Hadfield, Gillian K.; Leibo, Joel Z. (January 18, 2022). "Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents". Proceedings of the National Academy of Sciences. 119 (3) e2106028118. Bibcode:2022PNAS..11906028K. doi:10.1073/pnas.2106028118. ISSN 0027-8424. PMC 8784148. PMID 35022231.

References

External links Profile at the University of Toronto Faculty of Law Gillian Hadfield publications indexed by Google Scholar

Illustrations

Gillian Hadfield illustration

Worked examples

Example 1 — a first encounter with Gillian Hadfield

Start with the simplest possible case. Write down what Gillian Hadfield claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Gillian Hadfield before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Gillian Hadfield ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Gillian Hadfield

In research
Gillian Hadfield appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Gillian Hadfield in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Gillian Hadfield is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1961 births, AI legal scholars, Academic staff of the University of Toronto Faculty of Law, so understanding it makes those chapters shorter.
In everyday life
Look for Gillian Hadfield outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Gillian Hadfield in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Gillian Hadfield means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Gillian Hadfield out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Gillian Hadfield in simple terms?

Gillian Kereldena Hadfield (born July 14, 1961) is a Canadian economist, legal scholar and artificial intelligence researcher who is the Bloomberg Distinguished Professor of AI Alignment and Governance. She is also Professor of Law and of Strategic Management at Toronto, Canada CIFAR AI Chair at th…

Why does Gillian Hadfield matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Gillian Hadfield?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Gillian Hadfield.

Tags

  • 1961 births
  • AI legal scholars
  • Academic staff of the University of Toronto Faculty of Law
  • Canadian economists
  • Canadian lawyers
  • Center for Advanced Study in the Behavioral Sciences fellows
  • Hoover Institution people
  • Law and economics scholars
  • Lawyers in Ontario
  • Living people
  • New York University School of Law faculty
  • Queen's University at Kingston alumni

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