AALL/LexisNexis Call for Papers 2019-2020 Now Open!
The winners in the Open, New Member, and Student Divisions will receive $650, and the Short Form Division winner will receive $300, all generously donated by LexisNexis. Co-authors of winning papers share awards.
Recipients are recognized during award ceremonies at the AALL Annual Meeting and will be given the opportunity to present their papers in a program.
Earlier this week, the folks at The Algorithm asked "what is AI, exactly?" The answer is reproduced below.
The question may seem basic, but the answer is kind of complicated. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can. As it currently stands, the vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning. These algorithms use statistics to find patterns in massive amounts of data. They then use those patterns to make predictions on things like what shows you might like on Netflix, what you’re saying when you speak to Alexa, or whether you have cancer based on your MRI. Machine learning, and its subset deep learning (basically mac…
The Chronicle of Higher Education recently ran an article articulating the concerns with following a particular citation style. The problem with the rules-heavy approach to teaching [citation] isn’t just the rigidity with which students are taught those rules or follow them. It’s that too often students are taught rules without any context or justification. That’s just "the way things are." Students are left following rules just because a [law review editor] told them to, none the wiser about their function or history. It’s a recipe for seeing writing as foreign or external — something a student is supposed to do but not necessarily understand. Just follow the rules, kid, and there won’t be any trouble.
Instead of taking this approach to citation, the author leads a discussion not about citation st…
As we are increasingly aware, the ethical Duty of Technology Competence requires lawyers to keep abreast of “changes in the law and its practice, including the benefits and risks associated with relevant technology.” To date, 35 states have adopted the duty.
In a previous post, I highlighted the risks of blindly relying on algorithmic results (relevant technology) as a potential violation of the Duty of Technology Competence. We now have case law from Canada focusing on the benefits of using algorithmic results to perform legal research. In fact, this case law may be interpreted as requiring the use of algorithmic results when ethically performing legal research.