It has intrigued me that automation is not more prevalent in the legal profession than it appears as an outsider. Natural language processing, statistical analysis of case law and machine learning for building models to predict judicial decisions would seem an effective way of improving access to justice.
Training data is needed for machine learning. Explainable AI and ethics require careful attention. Moreover, it would be cynical of me to work on the assumption that lawyers are too preoccupied with legal professional privilege and profit maximization to be distracted by disruptive innovation.
Before I dig a deeper hole, I will instead focus on in-house legal departments. In particular, effective collaboration between general counsel, legal service providers and legal automation.
Which legal areas are suited to automation? Rob Booth, a co-founder of The Bionic Lawyer Project, has created a model in which legal problem-solving can be defined by two categories:
- Silver box problems are characterised by being:
- Rules based
- Stable and predictable
- Gold box problems are characterised by being:
- Complex, multifaceted and ambiguous
- Unpredictable and uncertain
- Rapidly changing or chaotically decaying
- Impacted by irrationality, emotion, dishonesty and bias
The two categories seem to map to the Cynefin domains. The silver box represents the clear and complicated domains, whereas the gold box represents complex and chaotic. In terms of aptitudes, there is some alignment with Wardley PST. What became clear as I began to map this space is that lawyers of the future will be more proficient in data analysis.
Uncertainty and rapid technological change means that, as enablers, legal departments should avoid creating isolated silos but integrate into the existing business tech stack instead, with access to corporate and operational data as required. The latter is already becoming industrialised through cloud monitoring and observability.
We should therefore be able to anticipate consolidation of legal service providers operating in the silver box category. Outsourcing silver box problems should become the norm as it will reduce costs and increase efficiency. Providers who can acquire the most data and extract the most value are likely to be those that invest most in innovation. So why not look at consolidating some services in the gold box too? Law firms pooling resources and sharing data to build more effective tools and better performing prediction models could become formidable players. AWS Robot Lawyer as a Service, perhaps?
I would welcome thoughts or comments.