Pitfalls and Promises - AI in Law
· 2 min read
Arvind Narayanan is the AI critic you may want to follow for two reasons:
- He is not trying to be sensational by taking extreme positions
- He is a first rate academic
His work tries to define the boundaries of what can be useful and where you have to be careful in a pragmatic way.
He has published a solid paper outlining these boundaries in legal space:
- Information processing tasks have high clarity and high observability and are best use cases to start (categorizing requests for legal help, e-discovery)
- Creativity, reasoning tasks are a range (spotting errors in legal filings are easier, preparing legal filings harder)
- Predictive tasks are fraught with challenges (legal judgment predictions)
Lots of legal departments are evaluating Generative AI - and this is a good paper for them. In fact, we are doing some solid value accretive work in contract extraction space - that helps turn unstructured data into structured information for downstream applications. And our experience has been consistent with these observations.
Current State of AI in Legal Industry
- Document Review and Analysis
- Legal Research
- Contract Management
- Predictive Analytics
Challenges and Opportunities
- Accuracy and Reliability
- Ethics and Bias
- Data Privacy
- Integration with Existing Systems
