1. Term
Definition
Examples
Data Science
Statistics with large data sets
Analytics
Often predictive
Lex Machina
Artificial Intelligence (AI)
“the science and engineering of making intelligent machines”
—John McCarthy
Self-driving cars
Machine Learning (ML)
Algorithms that infer features from data, and improve with more data
Unsupervised Learning
ML that finds groups of similar things and outliers by itself
Clustering Fraud detection
Supervised Learning
ML you train to identify similar things
Spam filter Predictive coding Facebook feed Amazon recommendations
Natural Language Processing (NLP)
Software to understand text and speech
Siri’s speech understanding

2. Legal AI is Especially Hard
Legal data is hard to acquire in large volumes
Legal data is highly unstructured (opinions, contracts)
Legalese is a hard dialect (unnatural language processing)
It’s hard to produce good training sets
Lawyer time is expensive
That old lawyer joke about putting 3 lawyers in a room and getting at least 4 opinions

3. Emerging Issues Algorithmic transparency Ethics for technologists
GDPR Article 22
Goodman and Flaxman, European Union regulations on algorithmic decision- making and a “right to explanation” (2016)
Angwin, et al., Machine Bias, Pro Publica (2016) criminal-sentencing
Ethics for technologists
ACM Code of Ethics and Professional Conduct 2018 (now at draft 3)