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Who really wrote Martin v. Hunter's Lessee?: Applying a machine learning ensemble to uncover a Chief Justice's unethical behavior

Published: 07 September 2023 Publication History

Abstract

This article applies multiple machine learning methods to strongly suggest that Chief Justice John Marshall behaved unethically in an important 19th Century case of American constitutional law: Martin v. Hunter's Lessee. He wrote large swathes of the opinion even though he had a large financial stake in the outcome of the case and even though he had formally recused himself. We establish this through multiple machine learning methods, including a character-sequence-to-image method known as the frequency chaos game representation (FCGR), sentence embeddings using BERT, and the reknowned Delta method involving bags of words. All these methods of stylometry reach the same conclusions.

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  1. Who really wrote Martin v. Hunter's Lessee?: Applying a machine learning ensemble to uncover a Chief Justice's unethical behavior

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      ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law
      June 2023
      499 pages
      ISBN:9798400701979
      DOI:10.1145/3594536
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 07 September 2023

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      Author Tags

      1. BERT
      2. Burrow's Delta
      3. FCGR
      4. United States Supreme Court
      5. law
      6. neural networks
      7. sentence embedding
      8. singular value decomposition
      9. spurious clues
      10. stylometry

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