Dan and I have written and spoken on legal informatics many times. Inevitably these conversations come to the same list of informatics examples from legal search/retrieval and decision making. These examples fall into two categories. The first sits firmly in the 20th century, while the second belongs in the 22nd century. I'll support my argument below and conclude with a lead into what I'd like to call 21st century law.
20th Century Legal Informatics — Computers as Libraries. Ask a typical lawyer how informatics affects their practice, and they might mention that salary infographic their friend emailed them. Data, modeling, statistics, and visualization might be seen as cute toys, but not real tools. Ask a typical lawyer how search affects them, however, and they'll readily list five-figure-per-seat services like Lexis, West, CCH, or RIA.
My opinion is that search is the only informatics tool that fits into the current legal paradigm — the library model. Law is a field of humans interpreting words, words live on documents, and documents live in libraries. Legal training focuses on reading and interpreting words and documents. For a new tool to be accepted by lawyers, it must complement this library model. From this standpoint, it's easy to see why search has succeeded — it facilitates the traditional library model.
22nd Century Legal Informatics — Computers as Lawyers. This category is best understood through IBM Watson and the International Association for Artificial Intelligence and Law (IAAIL). Watson embodies the hopes of 22nd century legal informatics, in which computers build and interpret models to make legal decisions. The IAAIL and its members have been presenting data models, search methods, expert systems, and judicial reasoning for more than 30 years. However, as a participant and former member, I will readily admit that the IAAIL has mostly failed to introduce these ideas into the mainstream of legal practice.
21st Century Legal Informatics — Computers and Lawyers. What can we do while our robotic overlords are still incubating? The way forward rests on four principles: Balance (neither humans nor computers alone provide optimal outcomes), Measure but not too much (measure wherever possible but avoid promoting measurement when it isn't the solution), Change but not too much (focus on cases where accurate models can be built, such as finance), and Aim high (don't let expectations based on the library model set your bar for success).