Salient Logo
Salient Logo
Practical AI For eDiscovery: Today, Tomorrow And In Future

Practical AI for eDiscovery: today, tomorrow and in future

When it comes to AI, there’s a lot of conjecture about the future. Elon Musk, for example, believes a human-work-free future is imminent.

Realistically, we’re still a long way away from discovering where artificial intelligence will lead us. But preparing for that mysterious future shouldn’t stop us from making the most of what we have here and now.

AI for eDiscovery today

The term ‘AI’ itself, encompasses a very broad church, and one could be forgiven for adopting the cynical position that any and every technology gets labelled as such, to ensure a place on the AI juggernaut.

In the eDiscovery space, however, AI does already play an integral role, most frequently in the review stage of the EDRM, in the form of machine learning (ML). From the first iteration as technology assisted review (TAR) to the second generation’s continuous active learning (CAL), it’s proven instrumental in speeding up the review process for today’s ever-expanding datasets. Even courts now accept the use of CAL (validated by humans) to reduce the disproportionate burden and cost of review.

Equally exciting (if less commonplace, for now) is the use of well-established AI techniques earlier in the EDRM. Bringing technology into play during the preliminary investigative phases has shown extraordinary potential to expedite processing, separate the signal from the noise and, as a result, cut overall eDiscovery costs.

We explore some of these use cases in the first two articles of our AI series, where we look at how we can use AI for intelligent culling (filtering out the junk earlier and more efficiently) and, conversely, using AI to improve inclusion (dealing with the unknown unknowns more systematically).

Of course, having access to extraordinary AI capabilities and being able to use them to best advantage aren’t exactly the same thing. Getting to grips with advanced AI capabilities is important, but it’s only part of the answer.

Truly turbocharging your investigative capabilities means building a team able to see the big picture and push the technology beyond tried and tested limits to pursue every investigative avenue. This is what we do for our clients, week after week, day after day. We are looking to integrate our AI experts to better understand our clients’ specific matter-by-matter needs, building new models where necessary, working out better ways to leverage emerging technology, and helping our clients extract every ounce of value from their data, both reactively and proactively.

But what comes next?

AI for eDiscovery tomorrow

At Salient, we live at the bleeding edge of technology, and there’s nothing our teams love more than keeping tabs on what’s coming down the pipeline.

At the moment, the focus is on large language models and generative AI, with a number of new pilots and prototypes hitting the market ready to exploit this capability on largescale datasets.

Our final two articles in our AI series explores generative AI and other emerging technology, with food for thought on how best to prepare for the brave new world that’s rapidly approaching.

Read more of our series on practical AI in eDiscovery

1. Intelligent culling - a critical component of cost-effective eDiscovery

The most effective way to reduce data volumes for review and hosting is by using AI to intelligently cull irrelevant, duplicate and otherwise non-responsive data.


Read the article >>

2. Using AI to improve inclusion

How do you find what you need when you don’t necessarily know what you’re looking for? With the help of AI and an investigative mindset, it’s possible to automatically expose leading indicators from within a much larger dataset.

Read the article >>

3. Expectation vs reality: what Generative AI really offers eDiscovery

Is generative AI really the next frontier in eDiscovery? How much of the hype is grounded in reality? We explore practical applications for GenAI in eDiscovery.

Read the article >>

4. Finetuning Generative AI for eDiscovery

AI may be powerful, but it still requires human input to deliver high quality results. We share our GenAI learnings around how context and prompt influence output and how GenAI output can be finetuned.

Read the article >>