Being the most important communication channel, email is the most common form of information collected during discovery in litigation.
If traditional ediscovery solutions were of some help, they demonstrate significant limitations in terms of deployment efforts and relevance.
Forget them! Recent developments in artificial intelligence open brand-new perspectives for exploring/reviewing mailboxes and for overcoming limitations of the traditional solutions.
Learn how to implement them as soon as possible.
Self-Supervised learning allows quicker deployment – leaving away the bias and constraints fixed by labelling and training
In a recent LinkedIn post, we shared a very interesting article from Forbes “The Next Generation Of Artificial Intelligence”, in which different emerging areas in artificial intelligence, expected to redefine the field, are commented.
The first one being the “unsupervised learning” – or in more precise terms used by Yann LeCun the “self-supervised learning” – announced to overcome the limitations of the widely spread supervised learning approach which implies an expensive and cumbersome labelling processes and a potential bias associated to human supervision.
Amongst sectors that will significantly be impacted by this AI area, the legal sector is holding a particular place: self-supervised learning indeed brings a very efficient support in the investigation of the exponentially increasing information to be reviewed, including emails, and opens new perspectives. Already now!
Why?
Simply because it allows quicker deployment – leaving away the bias and constraints fixed by labelling and training – and faster, deeper, more flexible and more accurate researches – integrating the contextual dimension and the ability to use natural language (words used in requests still remain key but more important is the context they describe by interacting with each other).
Priceless when you need to explore mailboxes: less efforts for more relevant results and open-ended exploration. This explains why solutions built on self-supervised learning are taking the lead in e-discovery (and supervised solutions are becoming legacy).
All of this explains why, at EisphorIA from the very beginning, we have developed in-house, our own self-supervised algorithms which provided significant gains in accuracy vs. supervised models, and we will certainly not deviate from this cap!
We use our solution to explore our own emails internally!
At EisphorIA, we use our solution to explore our own emails internally given the significant advantages it provides compared to Gmail, a.o.:
Those are massive benefits... that can be obtained today by deploying our solution on any kind of mailboxes environment.
Last but not least…
The other good news is that the deployment itself is very simple, not requiring any kind of effort for both the set-up and its use. As a child’s play!
Indeed, a closed and highly secured environment (on-premise or in the cloud) allows at any time to upload (or connect) without any difficulty the selected mailboxes: a few minutes of processing and the mailboxes are ready to be reviewed with all our functionalities.
Curious to know more about this? We would be very happy to connect with you.