Your Lawyer May Soon Be an Algorithm

As we discussed back in February, this is a coming reality:

We’re not at that point yet, and the lawyers at that kind of high-level decision-making level are the ones with least to worry about in terms of job security. They’re the ones that have the (so-far) uniquely human talent of keeping clients happy and drawing in new business.

But it seems more feasible that the career path of lawyers trying to reach those positions could change, if more of the lower steps on the ladder—the more mundane tasks that conventionally act as a training ground for future big dog lawyers—are taken over by the bots.

Your Lawyer May Soon Be an Algorithm

Why Learn to Code, Redux

Cory Doctorow gives a great justification for teaching lawyers (or doctors, or even cab drivers) to code.

Historically, a “domain expert” who wants to automate a system will approach an engineer, who will go through a formal process of requirements: gathering, technical design, implementation, testing and refinement. That’s fine as far as it goes, but there are huge dividends to be earned by giving people the power to solve their own problems without having to suffer through the inevitable signal degradation from being interpreted by others who’ve never had to do the job you’re trying to improve.

Like David Zvenyach, he further argues that students should begin with regular expressions. That’s a great suggestion and that’s where our class will begin in the spring.

Machine Learning and Law

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 well, machine learning algorithms can produce automated results that approximate those that would have been made by a similarly situated person. Harry Surden - University of Colorado Law School

HT @MillyBancroft