ROSS AI Plus Wexis Outperforms Either Westlaw or LexisNexis Alone, Study Finds

ROSS-report-figure1

ROSS Intelligence, the artificial intelligence legal research platform, outperforms Westlaw and LexisNexis in finding relevant authorities, in user satisfaction and confidence, and in research efficiency, and is virtually certain to deliver a positive return on investment.

These are among the findings of benchmark report being released today by the technology research and advisory company Blue Hill Research pitting ROSS against the two dominant legal research services. The full report will be available for download at the ROSS Intelligence website.

The study, which ROSS commissioned, assigned a panel of 16 experienced legal research professionals to research seven questions modeling real-world issues in federal bankruptcy law. Researchers were divided into four groups, with each group constrained to perform the research using a particular method:

  • Boolean search, in which researchers used only Boolean keyword search capabilities of major legal research platforms.
  • Natural language search, in which researchers used only the natural language search capabilities of major legal research platforms.
  • ROSS and Boolean Search, in which researchers were directed to use the ROSS platform and Boolean keyword search capabilities of major legal research platforms as they saw fit.
  • ROSS and natural language search, in which researchers were directed to use the ROSS platform and natural language search capabilities of major legal research platforms as they saw fit.

Of the Boolean and natural language search tools, researchers were assigned a mixture of Westlaw and LexisNexis tools. In addition, members of each research group were limited to individuals with no prior experience with the assigned tool and relatively minimal experience with bankruptcy law.

Research results were benchmarked against three assessment factors:

  • Information retrieval quality, including thoroughness, accuracy and ranking effectiveness.
  • User satisfaction, including ease of use and confidence in answer obtained.
  • Research efficiency, based on time to obtain a satisfactory answer.

The study concluded that ROSS generated better search results across all three benchmarks. “These findings indicate clear advantages resulting from the addition of the ROSS tool to electronic legal research involving traditional tools,” the report concludes.

Information Retrieval Quality

To benchmark the quality of information retrieval using the different research methods, Blue Hill compared their performance in three categories:

  • Thoroughness, or the number of relevant authorities identified.
  • Accuracy, or the amount of retrieved results that represented relevant authorities.
  • Normalized Discounted Cumulative Gain (NDCG), a standardized measure of the ranking of search results compared to an idealized ranking according to the relative value each result has to a user.

The chart at the top of this post shows the results. “On every measure, ROSS outperformed the traditional tools evaluated,” the report says.

Perhaps most striking about the findings was what they showed about Boolean search. Boolean tools retrieved less than a third of relevant authorities within their fist 20 results and only 25.8 percent of their results were relevant authorities.

By comparison, ROSS retrieved 55.8 percent of the relevant authorities within its top 20 results and 37.9 percent of results were relevant authorities.

ROSS most significantly outperformed Boolean and natural language tools in its ranking of results, the report found. Using the NDCG standard described above, ROSS achieved a score of .61, which was 46.1 percent higher than natural language and 127.8 percent higher than Boolean.

User Satisfaction and Confidence

When they completed their assignments, the researchers took a user satisfaction survey. This chart shows their responses.

ROSS-report-table2

The report summarizes it this way:

[T]he survey results summarized in Table 2 reveal strong indicators of user satisfaction of the participants using a ROSS-supported toolset with respect to the usability, presentation of search results, and inclusion of relevant authorities within the search results. Participants within these groups also indicated high levels of confidence in the ability of the tool to identify all authorities relevant to the matter. In nearly all cases, the responses indicated by participants using ROSS and another tool often exceeded those of organizations using only Boolean search or only Natural Language search by at least a full point.

The report further finds that there is a correlation between the effectiveness of the research tool and user perceptions. “Anecdotal responses … suggest that the ROSS tool’s higher concentration of relevant authorities among initial search result positions played a role with the higher satisfaction with the ease of use and confidence,” the report says.

Research Efficiency

In terms of research efficiency, the study found that the ROSS users reduced their research time by 30.3 percent over users of Boolean search and 22.3 percent over users of natural language search.

The Boolean researchers completed their tasks in 52.3 minutes, on average. Natural language researchers were a bit faster, completing their tasks in 47.2 minutes. By comparison, the group using ROSS and Boolean completed their tasks in 36.5 minutes, on average, and the ROSS and natural language group finished in 36.7 minutes.

The study also looked at the time it took researchers to write their answers to the research question. Here, they found no correlation between the research tools used and the time to write the answer.

Analyzing the Business Case

Based on these findings, Blue Hill went on to quantify how the research efficiency it found using ROSS would impact the bottom line for law firms and legal organizations. To do this, it compared the net gain that ROSS provides against the cost of acquiring it.

Blue Hill’s analysis looked at how the reduction in research time from using ROSS could be converted to productive, revenue-generating work. It estimated that for an associate billing $320 an hour, the conversion could generate revenue of from $5,306 if 10 percent of the saved time is converted to $61,868 if 100 percent of the saved time is converted.

That translates to a return on investment of anywhere from a low of 10.5 percent to a high of nearly 2,500 percent, again depending on the extent of the conversion. From the report:

Notably, using Blue Hill’s estimated billable rate of $320, a positive ROI is obtained at a 10% conversion rate, meaning that the investment drives a net gain to the firm with a minimal recovery of written-off hours. Similarly, a 176.4% to 544.5% ROI becomes possible with at least 25% conversion. Where exactly an organization will fall within these ranges will depend on a variety of factors related to its business, investment costs, and the scope of use adopted. In all cases, however, Blue Hill’s model strongly suggests positive business gain is available from the investment in ROSS.

Takeaways from Blue Hill and Me

The benchmarking report provides this conclusion:

Blue Hill finds that the ROSS tool provides significant, additive contributions to the effectiveness of legal researchers. … These results have the potential to unlock new gains in the efficient and profitable operation of legal organizations, as well as create opportunities for new revenue gain.

It also provides this caveat:

It should be noted that none of these findings indicate that AI-assisted legal research constitutes a dramatic transformation in the use of technology by legal organizations. Rather, the use cases and impact reviewed indicate that tools like ROSS Intelligence more closely represent a significant iteration in the continuing evolution of legal research tools that began with the launch of digital databases of authorities and have continued through developments in search technologies.

I would add some caveats of my own about this study:

  • First, as noted at the outset, ROSS commissioned it. However, Blue Hill is a well-regarded independent research firm and the fact that ROSS commissioned it should not detract from its findings.
  • Second, it is important to emphasize that the researchers did not use ROSS alone. They used it in conjunction with other tools to assist the overall effectiveness of their research. For the researchers using ROSS and either Boolean or natural language search, we do not know how they balanced the two tools.
  • Third, the research was limited to bankruptcy law, which is ROSS’s home turf, so to speak. Bankruptcy was the first area of law for which ROSS was designed to be used.
  • Fourth, this is not a “robots replacing lawyers” study. ROSS was used here as a tool to assist lawyers in research, not to perform research in their place.

All of that said, the significance of this report is major. Even as AI tools continue to make inroads in various segments of the legal profession, there remain many who question their real effectiveness and value and there has been a lack of evidence to assuage those questioners.

No doubt there will be other studies and other benchmarking reports of AI in the legal profession. But this study provides tangible evidence that AI not only works but, when used in conjunction with established research tools, can deliver measurably better results and measurable ROI.

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  • Vik

    I specialize in professional liability litigation (defense) and it would be interesting to see a court addressing the issue of whether it is within the standard of care for an attorney to rely exclusively on AI tools such as Ross, CARA (casetext), etc. without conducting additional “human” research to confirm the accuracy/completeness of the AI results (which, in a way, defeats the very purpose of said AI tools).

    • precisement

      That’s a typical problem created by the “black box” nature of a proprietary software.

      But even in the case of an open source software, I wonder.

  • Kevin Gerson

    I have other doubts about the purported conclusions of this study. First, both Lexis and Westlaw allow Boolean search results to then be ordered according to relevance. Was that one of the features that Blue Hill permitted its researchers to use (the study doesn’t say)? If not, then it would be absurd to expect a Boolean search to achieve high precision (what Blue Hill calls “accuracy”), especially if the search results were forced to return in reverse chronological order (which is unrelated to relevance). Second, were the researchers told that their results would be measured according to recall (what Blue Hill calls “thoroughness”). If not, then, again, it would be absurd to expect their searches to generate a high recall unless they were constructing their searches in such a way as to achieve that result. Constructing a Boolean search for high recall is very different from constructing one for high precision. Third, both Lexis and Westlaw allow Natural Language and Boolean searching. So, why then does Blue Hill not set up a test group in which the participating researchers could use both Boolean and Natural Language search features and to move between them as most experienced researchers naturally would? That would be the most relevant test group to compare to Ross/Boolean and Ross/Natural Language, no?

    • Bob Ambrogi

      These are good points. The report leaves unanswered a lot of questions about how the research was conducted.

    • David Houlihan

      Hi Kevin.

      I conducted the study and authored the report. Happy to answer your questions:

      (1) The participants were educated on the functionality of the tools and advised to use them in any manner that they felt could be most effective.

      (2) Participants were aware of all factors that we measured. Again, we advised them to guide their use based on what they believed would be most efficient and most effective as researchers. The goal was to model real-world use cases. The results are not intended to offer a test of the limits of pure technical capability, but an analysis of organic search experiences of a small sample of users. I have no doubt that any of the tools we used could provide higher performance in any measure using queries and settings intentionally constructed to optimize for that metric. We took the approach that we did because we felt it would offer a better approximation of use.

      (3) Great point. We considered a combined Boolean / Natural Language group, and I do think the report is less for its absence. Unfortunately, it was cut to keep within a manageable scope and timeline. This is a much bigger topic than our modest study could address. I am in favor of more research in this area. We need of studies involving: more use cases, additional tests (such as what you describe in your second point), and larger participant samples. If you are interested in helping us work on additional research (or have other questions), feel free to get in touch: dhoulihan@bluehillsresearch.com.

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  • precisement

    I have two questions/remarks for David Houlihan :

    1. Why no purely ROSS group ?

    2. Why were the professional researchers hired for this study proficient neither in the tools used nor the legal domain (US bankruptcy law) ?

    I’m asking because in real law firms research is done by attorneys, paralegals, trainees or law librarian researchers (I was one, in three different French and one US law firms and I trained trainees in online research). And those people are at the very least somewhat knowledgeable in the tools and the subject matter.

    On boolean research : I don’t know in US law, but in French case law, my search results are better with boolean than with natural language. But it may be the French legal databases search engines still have a long way to go in terms of NLP performances.

  • pragmatist3

    It appears that the Westlaw and Lexis results are combined in the “natural language” category. That causes me to wonder if one or the other exceeded ROSS in its results and they were combined to obscure that. I am not suggesting that. It just is raised by the way the data is presented.

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