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

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

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) https://arxiv.org/abs/1606.08813 Angwin, et al., Machine Bias, Pro Publica (2016) https://www.propublica.org/article/machine-bias-risk-assessments-in- criminal-sentencing Ethics for technologists ACM Code of Ethics and Professional Conduct 2018 (now at draft 3) https://ethics.acm.org/