AI, Machine Learning, Cloud and the Legal Sector

Phil Brown 15-Apr-2021 16:30:23

I saw this blog post released last month, discussing how AI and Machine Learning technology is being viewed by the legal industry.

My main point of interest in the above list is the subsequent ramifications it has on data and the usage of cloud technologies, particularly around AI and Machine Learning. GCP, Azure, Oracle and AWS have a wealth of AI and ML services, and you would typically expect to start developing these ideas within public cloud.

 

Why the Legal Sector?

Law is a heavily regulated industry and obviously has key concerns when it comes to data as it can be highly sensitive. Many of the technologies which are referred to in the article would be easily consumable in cloud platforms, but that means the data needs to be in the cloud, which in turn makes people nervous. Click here for more on how DSP-Explorer's services can specifically benefit the legal sector.

Interestingly enough, that concern isn’t called out in the article, yet from speaking to heavily regulated industries fears around data security are always cited as a reason not to explore cloud prospects. I wrote a piece last year which looked at how you can run Azure cognitive services anywhere; this means developing something in the cloud and running it on-premises.

These blogs are a great example of how innovation in cloud platforms is addressing concerns around data locality, and something to bear in mind for data-conscious industries.

Part One: Portable Sentiment Analysis with Azure Cognitive Services

Part Two: Portable Sentiment Analysis with Azure Cognitive Services

 

Finding a Specific Use-Case

The article calls out several concerns highlighted by lawyers and legal tech firms, one of which I’m interpreting as hyperbole. Like a lot of industries, they are experiencing ML and AI being pitched to them in a way to solve problems and improve efficiencies, yet they see little evidence of substance behind those claims.

From our own research it is clear that organisations need to see very specific use cases for the application of ML and AI before it can be taken seriously. We worked on a great project last year which demonstrates the specific nature of a ML guided project: our Cochlear Implants project.


Funding Solutions

The other interesting point raised is lack of funding as being a significant challenge. It is unclear if this lack of funding is due to the lack of a use case, or based on the perceived investment required. However, one of the great enablers of cloud technology is actually its ability to start small with minimum commitment and access to a whole range of ML and AI technologies. Here use cases can be proven without having to invest millions.

 

If you would like to find out more about developing a use case for AI and Machine Learning within your business, check out our dedicated webpage or get in touch with the DSP-Explorer innovation team today.