Early data assessment is changing. Legal consultant Rachel Mills explains why.
The logic of early data assessment has always been undeniable, even more so now given the technological advances we’ve seen in recent years. Solutions like Reveal-Brainspace have added powerful emotional and sentiment analysis capabilities to traditional text search tools, giving us even richer insights in those critical early days. What’s not to like? Well, that would be the habitual fear of incurring early costs obviously, so we often get a ‘no’ to EDA, even though just as often this initial refusal turns into later regret.
What if AI can be leveraged to highlight ‘risky’ data that may be indicative that settlement is prudent or an award of damages is likely? This would have to be tailor-made to particular clients or types of claims to be most effective but is this where data-driven insight helps logic battle habit and triumph in the fight for early data assessment?
Let’s first review the ‘Why didn’t we settle on day one?’ scenario. It should be familiar and goes something like this:
- In all forms of dispute resolution, clients often look back with the benefit of hindsight and say, had we settled earlier, we would have saved costs. This is particularly so in class actions or group litigation where multiple claims of a low value can result in a significant payout and excessive costs.
- If litigators knew what data relating to a case might be available earlier, it could significantly impact litigation strategy and the ultimate decision of when or if to settle. This would ultimately reduce the costs for both parties.
- Clients often say at the beginning of litigation that there is no chance of a settlement because they consider there has been no negligence. At that stage, disclosure has not taken place and the parties are unaware of the information available to support a claim or a defence. They are also concerned about their reputation and spurious claims.
- There is a reluctance to be seen to settle claims as a commercial decision in order to reduce the final bill for damages and costs. Consequently, there is a settlement at a later stage when the cost of litigation and the risk of paying claimants’ cost becomes too high. The parties have also entrenched their positions by this stage which makes settlement more difficult and often more expensive. Claims generally settle after disclosure when the parties become aware of the potential data available that will impact the claim.
- Class actions often have a huge amount of data that needs to be gathered and interpreted before an accurate cost-benefit analysis can take place. If data analysis can happen at an earlier stage, and more effectively, this can determine whether class actions should be settled or defended at an early stage.
- The issue with early data assessment is that clients need to “front-end” the cost of litigation so that they spend money on data analysis early to avoid costs escalating later. Some are reluctant to do this but must understand that it could ultimately save unnecessary costs and assist with future claims analysis.
Solutions like Reveal-Brainspace have added powerful emotional and sentiment analysis capabilities to traditional text search tools, giving us even richer insights in those critical early days
With the skilled use of AI, you can overcome that reluctance by offering much more certainty. Our own legal technologists, for example, have the ability to create AI libraries that can learn patterns in data over and over again; this sort of data sampling and the inferences taken from it make early evaluation of a case a very much more exact science.
When you pair that technology with the right human intelligence – the investigators and case reviewers – you can also be confident of having quality judgements based on quality data sets. That sort of empowerment early on can put you in a very strong position for early settlement negotiations or an advantageous strategy at the Case Management Conference.
Old habits die hard. But AI is breathing life into new ones when it comes to early data assessment.