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Articles 1 - 3 of 3
Full-Text Articles in Judges
(At Least) Thirteen Ways Of Looking At Election Lies, Helen Norton
(At Least) Thirteen Ways Of Looking At Election Lies, Helen Norton
Publications
Lies take many forms. Because lies vary so greatly in their motivations and consequences (among many other qualities), philosophers have long sought to catalog them to help make sense of their diversity and complexity. Legal scholars too have classified lies in various ways to explain why we punish some and protect others. This symposium essay offers yet another taxonomy of lies, focusing specifically on election lies — that is, lies told during or about elections. We can divide and describe election lies in a wide variety of ways: by speaker, by motive, by subject matter, by audience, by means of …
The Disruptive Neuroscience Of Judicial Choice, Anna Spain Bradley
The Disruptive Neuroscience Of Judicial Choice, Anna Spain Bradley
Publications
Scholars of judicial behavior overwhelmingly substantiate the historical presumption that most judges act impartially and independent most of the time. The reality of human behavior, however, says otherwise. Drawing upon untapped evidence from neuroscience, this Article provides a comprehensive evaluation of how bias, emotion, and empathy—all central to human decision-making—are inevitable in judicial choice. The Article offers three novel neuroscientific insights that explain why this inevitability is so. First, because human cognition associated with decision-making involves multiple, and often intersecting, neural regions and circuits, logic and reason are not separate from bias and emotion in the brain. Second, bias, emotion, …
Machine Learning And Law, Harry Surden
Machine Learning And Law, Harry Surden
Publications
This Article explores the application of machine learning techniques within the practice of law. Broadly speaking “machine learning” refers to computer algorithms that have the ability to “learn” or improve in performance over time on some task. In general, machine learning algorithms are designed to detect patterns in data and then apply these patterns going forward to new data in order to automate particular tasks. Outside of law, machine learning techniques have been successfully applied to automate tasks that were once thought to necessitate human intelligence — for example language translation, fraud-detection, driving automobiles, facial recognition, and data-mining. If performing …