Robophobia: Great New Law Review Article – Part 2

May 26, 2022
Professor Andrew Woods

This article is Part Two of my review of Robophobia by Professor Andrew Woods. See here for Part 1.

I want to start off Part 2 with a quote from Andrew Woods in the Introduction to his article, Robophobia, 93 U. Colo. L. Rev. 51  (Winter, 2022). Footnotes omitted.

Deciding where to deploy machine decision-makers is one of the most important policy questions of our time. The crucial question is not whether an algorithm has any flaws, but whether it outperforms current methods used to accomplish a task. Yet this view runs counter to the prevailing reactions to the introduction of algorithms in public life and in legal scholarship. Rather than engage in a rational calculation of who performs a task better, we place unreasonably high demands on robots. This is   robophobia – a bias against robots, algorithms, and other nonhuman deciders. 

Robophobia is pervasive. In healthcare, patients prefer human diagnoses to computerized diagnoses, even when they are told that the computer is more effective.  In litigation, lawyers are reluctant to rely on – and juries seem suspicious of – [*56] computer-generated discovery results, even when they have been proven to be more accurate than human discovery results. . . .

In many different domains, algorithms are simply better at performing a given task than people. Algorithms outperform humans at discrete tasks in clinical health, psychology, hiring and admissions, and much more. Yet in setting after setting, we regularly prefer worse-performing humans to a robot alternative, often at an extreme cost. 

Woods, Id. at pgs. 55-56

Bias Against AI in Electronic Discovery

Electronic discovery is a good example of the regular preference of worse-performing humans to a robot alternative, often at an extreme cost. There can be no question now that any decent computer assisted method will significantly outperform human review. We have made great progress in the law through the outstanding leadership of many lawyers and scientists in the field of ediscovery, but there is still a long way to go to convince non-specialists. Professor Woods understands this well and cites many of the leading legal experts on this topic at footnotes 137 to 148. Even though I am not included in his footnotes of authorities (what do you expect, the article was written by a mere human, not an AI), I reproduce them below in the order cited as a grateful shout-out to my esteemed colleagues.

  • Maura R. Grossman & Gordon V. Cormack, Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient than Exhaustive Manual Review, 17 Rich. J.L. & Tech. 1 (2011).
  • Sam Skolnik, Lawyers Aren’t Taking Full Advantage of AI Tools, Survey Shows, Bloomberg L. (May 14, 2019) (reporting results of a survey of 487 lawyers finding that lawyers have not well utilized useful new tools).
  • Moore v. Publicis Groupe, 287 F.R.D. 182, 191 (S.D.N.Y. 2012) (“Computer-assisted review appears to be better than the available alternatives, and thus should be used in appropriate cases.”) Judge Andrew Peck.
  • Bob Ambrogi, Latest ABA Technology Survey Provides Insights on E-Discovery Trends, Catalyst: E-Discovery Search Blog (Nov. 10, 2016) (noting that “firms are failing to use advanced e-discovery technologies or even any e-discovery technology”).
  • Doug Austin, Announcing the State of the Industry Report 2021, eDiscovery Today (Jan. 5, 2021),
  • David C. Blair & M. E. Maron, An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System, 28 Commc’ns ACM 289 (1985).
  • Thomas E. Stevens & Wayne C. Matus, Gaining a Comparative Advantage in the Process, Nat’l L.J. (Aug. 25, 2008) (describing a “general reluctance by counsel to rely on anything but what they perceive to be the most defensible positions in electronic discovery, even if those solutions do not hold up any sort of honest analysis of cost or quality”).
  • Rio Tinto PLC v. Vale S.A., 306 F.R.D. 125, 127 (S.D.N.Y. 2015). Judge Andrew Peck.
  • See The Sedona Conference, The Sedona Conference Best Practices Commentary on the Use of Search & Information Retrieval Methods in E-Discovery, 15 Sedona Conf. J. 217, 235-26 (2014) (“Some litigators continue to primarily rely upon manual review of information as part of their review process. Principal rationales [include] . . . the perception that there is a lack of scientific validity of search technologies necessary to defend against a court challenge . . . .”).
  • Doug Austin, Learning to Trust TAR as Much as Keyword Search: eDiscovery Best Practices, eDiscovery Today (June 28, 2021).
  • Robert Ambrogi, Fear Not, Lawyers, AI Is Not Your Enemy, Above Law (Oct. 30, 2017).

Robophobia Article Is A First

Robophobia is the first piece of legal scholarship to address our misjudgment of algorithms head-on. Professor Woods makes this assertion up front and I believe it. The Article catalogs different ways that we now misjudge poor algorithms. The evidence of our robophobia is overwhelming, but, before Professor Woods work, it had all been in silos and was not seriously considered. He is the first to bring it all together and consider the legal implications.

His article goes on to suggests several reforms, also a first. But before I get to that, a more detailed overview is in order. The Article is in six parts. Part I provides several examples of robophobia. Although a long list, he says it is far from exhaustive. Part II distinguishes different types of robophobia. Part III considers potential explanations for robophobia. Part IV makes a strong, balanced case for being wary of machine decision-makers, including our inclination to, in some situations, over rely on machines. Part V outlines the components of his case against robophobia. The concluding Part VI offers “tentative policy prescriptions for encouraging rational thinking – and policy making – when it comes to nonhuman deciders.

Part II of the Article – Types of Robophobia

Professor Woods identifies five different types of robophobia.

  • Elevated Performance Standards: we expect algorithms to greatly outperform the human alternatives and often demand perfection.
  • Elevated Process Standards: we demand algorithms explain their decision-making processes clearly and fully; the reasoning must be plain and understandable to human reviewers.
  • Harsher Judgments: algorithmic mistakes are routinely judges more severely than human errors. A corollary of elevated performance standards.
  • Distrust: our confidence in automated decisions is week and fragile. Would you rather get into an empty AI Uber, or one driven by a scruffy looking human?
  • Prioritizing Human Decisions: We must keep “humans in the loop” and give more weight to human input than algorithmic.

Part III – Explaining Robophobia

Professor Woods considers seven different explanations for robophobia.

  • Fear of the Unknown
  • Transparency Concerns
  • Loss of Control
  • Job Anxiety
  • Disgust
  • Gambling for Perfect Decisions
  • Overconfidence in Human Decisions

I’m limiting my review here, since the explanations for most of these should be obvious by now and I want to limit the length of my blog. But the disgust explanation was not one I expected and a short quote by Andrew Woods might be helpful, along with the robot photo I added.

Uncannily Creepy Robot

[T]he more that robots become humanlike, the more they can trigger feelings of disgust. In the 1970s, roboticist Masahiro Mori hypothesized that people would be more willing to accept robots as the machines became more humanlike, but only up to a point, and then human acceptance of nearly-human robots would decline.[227] This decline has been called the “uncanny valley,” and it has turned out to be a profound insight about how humans react to nonhuman agents. This means that as robots take the place of humans with increasing frequency—companion robots for the elderly, sex robots for the lonely, doctor robots for the sick—reports of robots’ uncanny features will likely increase.

For interesting background on the uncanny valley, see these You Tube videos and experience robot disgust for yourself. Uncanny Valley by Popular Science 2008 (old, but pretty disgusting). Here’s a more recent and detailed one, pretty good, by a popular twenty-something with pink hair. Why is this image creepy? by TUV 2022.

Parts IV and V – The Cases For and Against Robophobia

Part IV lays out all the good reasons to be suspect of delegating decision to algorithms. Part V is the new counter-argument, one we have not heard before, why robophobia is bad for us. This is probably the heart of the article and suggest you read this part for sure.

Here is a good quote at the end of Part IV to put the pro versus anti-robot positions into perspective:

Pro-robot bias is no better than antirobot bias. If we are inclined both to over- and underrely on robots, then we need to correct both problems—the human fear of robots is one piece of the larger puzzle of how robots and humans should coexist. The regulatory challenge vis-à-vis human-robot interactions then is not merely minimizing one problem or the other but rather making a rational assessment of the risks and rewards offered by nonhuman decision-makers. This requires a clear sense of the key variables along which to evaluate decision-makers.

In the first two paragraphs of Part V of his article Professor Woods deftly summarizes the case against robophobia.

We are irrational in our embrace of technology, which is driven more by intuition than reasoned debate. Sensible policy will only come from a thoughtful and deliberate—and perhaps counterintuitive—approach to integrating robots into our society. This is a point about the policymaking process as much as it is about the policies themselves. And at the moment, we are getting it wrong—most especially with the important policy choice of where to transfer control from a human decider to a robot decider.

Specifically, in most domains, we should accept much more risk from algorithms than we currently do. We should assess their performance comparatively—usually by comparing robots to the human decider they would replace—and we should care about rates of improvement. This means we should embrace robot decision-makers whenever they are better than human decision-makers. We should even embrace robot decision-makers when they are less effective than humans, as long as we have a high level of confidence that they will soon become better than humans. Implicit in this framing is a rejection of deontological claims—some would say a “right”—to having humans do certain tasks instead of robots.[255] But, this is not to say that we should prefer robots to humans in general. Indeed, we must be just as vigilant about the risks of irrationally preferring robots over humans, which can be just as harmful.[256]


The concluding Part Three of my review of Robophobia is coming soon. In the meantime, take a break and think about Professor Woods policy-based perspective. That is something practicing lawyers like me do not do often enough. Also, it is of value to consider Andrew’s reference to “deontology“, not a word previously in my vocabulary. It is a good ethics term to pick up. Thank you Immanuel Kant.





Elusion Random Sample Test Ordered Under Rule 26(g) in a Keyword Search Based Discovery Plan

August 26, 2018

There is a new case out of Chicago that advances the jurisprudence of my sub-specialty, Legal Search. City of Rockford v. Mallinckrodt ARD Inc., 2018 WL 3766673, Case 3:17-cv-50107 (N.D. Ill., Aug. 7, 2018). This discovery order was written by U.S. Magistrate Judge Iain Johnston who entitled it: “Order Establishing Production Protocol for Electronically Stored Information.” The opinion is both advanced and humorous, destined to be an oft-cited favorite for many. Thank you Judge Johnston.

In City of Rockford an Elusion random sample quality assurance test was required as part of the parties discovery plan to meet the reasonable efforts requirements of Rule 26(g). The random sample procedure proposed was found to impose only a proportional, reasonable burden under Rule 26(b)(1). What makes this holding particularly interesting is that an Elusion test is commonly employed in predictive coding projects, but here the parties had agreed to a keyword search based discovery plan. Also see: Tara Emory, PMP, Court Holds that Math Matters for eDiscovery Keyword Search,  Urges Lawyers to Abandon their Fear of Technology (Driven, (August 16, 2018) (“party using keywords was required to test the search effectiveness by sampling the set of documents that did not contain the keywords.”)

The Known Unknowns and Unknown Unknowns

Judge Johnston begins his order in City of Rockford with a famous quote by Donald Rumseld, a two-time Secretary of Defense.

“[A]s we know there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. . .”
Donald Rumseld

For those not familiar with this famous Known Knowns quip, here is a video of the original:

Here the knowledge logic is spelled out in a chart, since I know we all love that sort of thing. Deconstructing Rumsfeld: Knowledge and Ignorance in the Age of Innovation (Inovo 5/114).

Anybody who does complex investigations is familiar with this problem. Indeed, you can argue this insight is fundamental to all of science and experimental method. Logan, David C. (March 1, 2009). “Known knowns, known unknowns, unknown unknowns and the propagation of scientific enquiry”, Journal of Experimental Botany 60 (3). pp. 712–4. [I have always wanted to quote a botany journal.]

How do you deal with the known unknowns and the unknown unknowns, the information that we don’t even know that we don’t know about? The deep, hidden information that is both obtuse and rare. Information that is hard to retrieve and harder still to prove does not exist at all. Are you chasing something that might not exist? Something unknown because nonexistent? Such as an overlooked Highly Relevant document? (The stuff of nightmares!) Are you searching for nothing? Zero? If you find it, what does that mean? What can be known and what can never be known? Scientists, investigators and the Secretary of Defense alike all have to ponder these questions and all want to use the best tools and best people possible to do so. See: Deconstructing Rumsfeld: Knowledge and Ignorance in the Age of Innovation (Inovo 5/114).

Seeking Knowledge of the Unknown Elusion Error Rate

These big questions, though interesting, are not why Judge Johnston started his opinion with the Rumseld quote. Instead, he used the quote to emphasize that new e-discovery methods, namely random sampling and statistical analysis, can empower lawyers to know what they never did before. A technical way to know the known unknowns. For instance, a way to know the number of relevant documents that will be missed and not produced: the documents that elude retrieval.

As the opinion and this blog will explain, you can do that, know that, by using an Elusion random sample of the null-set. The statistical analysis of the sample transforms the unknown quantity to a known (subject to statistical probabilities and range). It allows lawyers to know, at least within a range, the number of relevant documents that have not been found. This is a very useful quality assurance method that relies on objective measurements to demonstrate success of your project, which here is information retrieval. This and other random sampling methods allow for the calculation of Recall, meaning the percent of total relevant documents found. This is another math-based, quality assurance tool in the field of information retrieval.

One of the main points Judge Johnston makes in his order is that lawyers should embrace this kind of technical knowledge, not shy away from it. As Tara Emory said in her article, Court Holds that Math Matters for eDiscovery Keyword Search:

A producing party must determine that its search process was reasonable. In many cases, the best way to do this is with objective metrics. Producing parties often put significant effort into brainstorming keywords, interviewing witnesses to determine additional terms, negotiating terms with the other party, and testing the documents containing their keywords to eliminate false positives. However, these efforts often still fail to identify documents if important keywords were missed, and sampling the null set is a simple, reasonable way to test whether additional keywords are needed. …

It is important to overcome the fear of technology and its related jargon, which can help counsel demonstrate the reasonableness of search and production process. As Judge Johnston explains, sampling the null set is a process to determine “the known unknown,” which “is the number of the documents that will be missed and not produced.” Judge Johnson disagreed with the defendants’ argument “that searching the null set would be costly and burdensome.” The Order requires Defendants to sample their null set at a 95% +/-2% margin of error (which, even for a very large set of documents, would be about 2,400 documents to review).[4] By taking these measures—either with TAR or with search terms, counsel can more appropriately represent that they have undertaken a “reasonable inquiry” for relevant information within the meaning of FRCP 26(g)(1).

Small Discovery Dispute in an Ocean of Cooperation

Judge Johnston was not asked to solve the deep mysteries of knowing and not knowing in City of Rockford. The parties came to him instead with an interesting, esoteric discovery dispute. They had agreed on a great number of things, for which the court profusely congratulated them.

The attorneys are commended for this cooperation, and their clients should appreciate their efforts in this regard. The Court certainly does. The litigation so far is a solid example that zealous advocacy is not necessarily incompatible with cooperation. The current issue before the Court is an example of that advocacy and cooperation. The parties have worked to develop a protocol for the production of ESI in this case, but have now reached an impasse as to one aspect of the protocol.

The parties disagreed on whether to include a document review quality assurance test in the protocol. The Plaintiffs wanted one and the Defendants did not. Too burdensome they said.

To be specific, the Plaintiffs wanted a test where the efficacy of any parties production would be tested by use of an Elusion type of Random Sample of the documents not produced. The Defendants opposed any specific test. Instead, they wanted the discovery protocol to say that if the receiving party had concerns about the adequacy of the producing party’s efforts, then they would have a conference to address the concerns.

Judge Johnston ruled for the plaintiff in this dispute and ordered a  random elusion sample to be taken after the defendant stopped work and completed production. In this case it was a good decision, but should not be routinely required in all matters.

The Stop Decision and Elusion Sample

One of the fundamental problems in any investigation is to know when you should stop the investigation because it is no longer worth the effort to carry on. When has a reasonable effort been completed? Ideally this happens after all of the important documents have already been found. At that point you should stop the effort and move on to a new project. Alternatively, perhaps you should keep on going and look for more? Should you stop or not?

In Legal Search we all this the “Stop Decision.” Should you conclude the investigation or continue further AI training rounds and other search. As explained in the e-Discovery Team TAR Course:

The all important stop decision is a legal, statistical decision requiring a holistic approach, including metrics, sampling and over-all project assessment.You decide to stop the review after weighing a multitude of considerations. Then you test your decision with a random sample in Step Seven.

See: TAR Course: 15th Class – Step Seven – ZEN Quality Assurance Tests.

If you want to go deeper into this, then listen in on this TAR Course lecture on the Stop decision.

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Once a decision is made to Stop, then a well managed document review project will use different tools and metrics to verify that the Stop decision was correct. Judge Johnston in City of Rockford used one of my favorite tools, the Elusion random sample that I teach in the e-Discovery Team TAR Course. This type of random sample is called an Elusion sample.

Judge Johnston ordered an Elusion type random sample of the null set in City of Rockford. The sample would determine the range of relevant documents that likely eluded you. These are called False Negatives. Documents presumed Irrelevant and withheld that were in fact Relevant and should have been produced. The Elusion sample is designed to give you information on the total number of Relevant documents that were likely missed, unretrieved, unreviewed and not produced or logged. The fewer the number of False Negatives the better the Recall of True Positives. The goal is to find, to retrieve, all of the Relevant ESI in the collection.

Another way to say the same thing is to say that the goal is Zero False Negatives. You do not miss a single relevant file. Every file designated Irrelevant is in fact not relevant. They are all True Negatives. That would be Total Recall: “the Truth, the Whole Truth …” But that is very rare and some error, some False Negatives, are expected in every large information retrieval project. Some relevant documents will almost always be missed, so the goal is to make the False Negatives inconsequential and keep the Elusion rate low.

Here is how Judge Iain Johnston explained the random sample:

Plaintiffs propose a random sample of the null set. (The “null set” is the set of documents that are not returned as responsive by a search process, or that are identified as not relevant by a review process. See Maura R. Grossman & Gordon v. Cormack, The Grossman-Cormack Glossary of Technology-Assisted Review, 7 Fed. Cts. L. Rev. 1, 25 (2013). The null set can be used to determine “elusion,” which is the fraction of documents identified as non-relevant by a search or review effort that are, in fact, relevant. Elusion is estimated by taking a random sample of the null set and determining how many or what portion of documents are actually relevant. Id. at 15.) FN 2

Judge Johnston’s Footnote Two is interesting for two reasons. One, it attempts to calm lawyers who freak out when hearing anything having to do with math or statistics, much less information science and technology. Two, it does so with a reference to Fizbo the clown.

The Court pauses here for a moment to calm down litigators less familiar with ESI. (You know who you are.) In life, there are many things to be scared of, including, but not limited to, spiders, sharks, and clowns – definitely clowns , even Fizbo. ESI is not something to be scared of. The same is true for all the terms and jargon related to ESI. … So don’t freak out.

Accept on Zero Error for Hot Documents

Although this is not addressed in the court order, in my personal view, no False Negatives, iw – overlooked  documents – are acceptable when it comes to Highly Relevant documents. If even one document like that is found in the sample, one Highly Relevant Document, then the Elusion test has failed in my view. You must conclude that the Stop decision was wrong and training and document review must recommence. That is called an Accept on Zero Error test for any hot documents found. Of course my personal views on best practice here assume the use of AI ranking, and the parties in City of Rockford only used keyword search. Apparently they were not doing machine training at all.

The odds of finding False Negatives, assuming that only a few exist (very low prevalence) and the database is large, are very unlikely in a modest sized random sample. With very low prevalence of relevant ESI the test can be of limited effectiveness. That is an inherent problem with low prevalence and random sampling. That is why statistics have only limited effectiveness and should be considered part of a total quality control program. See Zero Error Numerics: ZEN. Math matters, but so too does good project management and communications.

The inherent problem with random sampling is that the only way to reduce the error interval is to increase the size of the sample. For instance, to decrease the margin of error to only 2% either way, a total error of 4%, a random sample size of around 2,400 documents is needed. Even though that narrows the error rate to 4%, there is still another error factor of the Confidence Level, here at 95%. Still, it is not worth the effort to review even more sample documents to reduce that to a 99% Level.

Random sampling has limitations in low prevalence datasets, which is typical in e-discovery, but still sampling can be very useful. Due to this rarity issue, and the care that producing parties always take to attain high Recall, any documents found in an Elusion random sample should be carefully studied to see if they are of any significance. We look very carefully at any new documents found that are of a kind not seen before. That is unusual. Typically  any relevant documents found by random sample of the elusion set are of a type that have been seen before, often many, many times before. These “same old, same old” type of documents are of no importance to the investigation at this point.

Most email related datasets are filled with duplicative, low value data. It is not exactly irrelevant noise, but it is not a helpful signal either. We do not care if we  get all of that kind of merely relevant data. What we really want are the Hot Docs, the high value Highly Relevant ESI, or at least Relevant and of a kind not seen before. That is why the Accept On Zero Error test is so important for Highly Relevant documents.

The Elusion Test in City of Rockford 

In City of Rockford Judge Johnston considered a discovery stipulation where the parties had agreed to use a typical keyword search protocol, but disagreed on a quality assurance protocol. Judge Johnston held:

With key word searching (as with any retrieval process), without doubt, relevant documents will be produced, and without doubt, some relevant documents will be missed and not produced. That is a known known. The known unknown is the number of the documents that will be missed and not produced.

Back to the False Negatives again, the known unknown. Judge Johnston continues his analysis:

But there is a process by which to determine that answer, thereby making the known unknown a known known. That process is to randomly sample the nullset. Karl Schieneman & Thomas C. Gricks III, The Implications of Rule26(g) on the Use of Technology-Assisted Review, 2013 Fed. Cts. L. Rev. 239, 273 (2013)(“[S]ampling the null set will establish the number of relevant documents that are not being produced.”). Consequently, the question becomes whether sampling the null set is a reasonable inquiry under Rule 26(g) and proportional to the needs of this case under Rule 26(b)(1).

Rule 26(g) Certification
Judge Johnston takes an expansive view of the duties placed on counsel of record by Rule 26(g), but concedes that perfection is not required:

Federal Rule of Civil Procedure 26(g) requires all discovery requests be signed by at least one attorney (or party, if proceeding pro se). Fed. R. Civ. P. 26(g)(1). By signing the response, the attorney is certifying that to the best of counsel’s knowledge, information, and belief formed after a reasonable inquiry, the disclosure is complete and correct at the time it was made. Fed. R. Civ. P. 26(g)(1)(A). But disclosure of documents need not be perfect. … If the Federal Rules of Civil Procedure were previously only translucent on this point, it should now be clear with the renewed emphasis on proportionality.

Judge Johnston concludes that Rule 26(g) on certification applies to require the Elusion sample in this case.

Just as it is used in TAR, a random sample of the null set provides validation and quality assurance of the document production when performing key word searches.  Magistrate Judge Andrew Peck made this point nearly a decade ago. See William A. Gross Constr. Assocs., 256 F.R.D. at 135-6 (citing Victor Stanley, Inc. v. Creative Pipe, Inc., 250 F.R.D. 251, 262 (D. Md. 2008)); In re Seroquel Products Liability Litig., 244 F.R.D. 650, 662 (M.D. Fla. 2007) (requiring quality assurance).

Accordingly, because a random sample of the null set will help validate the document production in this case, the process is reasonable under Rule 26(g).

Rule 26(b)(1) Proportionality

Judge Johnston considered as a separate issue whether it was proportionate under Rule 26(b)(1) to require the elusion test requested. Again, the court found that it was in this large case on the pricing of prescription medication and held that it was proportional:

The Court’s experience and understanding is that a random sample of the null set will not be unreasonably expensive or burdensome. Moreover and critically, Defendants have failed to provide any evidence to support their contention. Mckinney/Pearl Rest. Partners, L.P. v. Metro. Life Ins. Co., 322 F.R.D. 235, 242 (N.D.Tex. 2016) (party required to submit affidavits or offer evidence revealing the nature of the burden)
Once again we see a party seeking protection from having to do something because it is so burdensome then failing to present actual evidence of burden. We see this a lot lately. Responding Party’s Complaints of Financial Burden of Document Review Were Unsupported by the Evidence, Any Evidence (e-Discovery Team, 8/5/18);

Judge Johnston concludes his “Order Establishing Production Protocol for Electronically Stored Information” with the following:

The Court adopts the parties’ proposed order establishing the production protocol for ESI with the inclusion of Plaintiffs’ proposal that a random sample of the null set will occur after the production and that any responsive documents found as a result of that process will be produced. Moreover, following that production, the parties should discuss what additional actions, if any, should occur. If the parties cannot agree at that point, they can raise the issue with the Court.

Conclusion

City of Rockford is important because it is the first case to hold that a quality control procedure should be used to meet the reasonable efforts certification requirements of Rule 26(g). The procedure here required was a random sample Elusion test with related, limited data sharing. If this interpretation of Rule 26(g) is followed by other courts, then it could have a big impact on legal search jurisprudence. Tara Emory in her article, Court Holds that Math Matters for eDiscovery Keyword Search goes so far as to conclude that City of Rockford stands for the proposition that “the testing and sampling process associated with search terms is essential for establishing the reasonableness of a search under FRCP 26(g).”

The City of Rockford holding could persuade other judges and encourage courts to be more active and impose specific document review procedures on all parties, including requiring the use of sampling and artificial intelligence. The producing party cannot always have a  free pass under Sedona Principle Six. Testing and sampling may well be routinely ordered in all “large” document review cases in the future.

It will be very interesting to watch how other attorneys argue City of Rockford. It will continue a line of cases examining methodology and procedures in document review. See eg., William A. Gross Construction Associates, Inc. v. American Manufacturers Mutual Insurance Co., 256 F.R.D. 134 (S.D.N.Y. 2009) (“wake-up call” for lawyers on keyword search); Winfield v. City of New York (SDNY, Nov. 27, 2017), where Judge Andrew Peck considers methodologies and quality controls of the active machine learning process. Also see Special Master Maura Grossman’s Order Regarding Search Methodology for ESI, a validation Protocol for the Broiler Chicken antitrust cases.

The validation procedure of an Elusion sample in City of Rockford is just one of many possible review protocols that a court could impose under Rule 26(g). There are dozens more, including whether predictive coding should be required. So far, courts have been reluctant to order that, as Judge Peck explained in Hyles:

There may come a time when TAR is so widely used that it might be unreasonable for a party to decline to use TAR. We are not there yet.

Hyles v. New York City, No. 10 Civ. 3119 (AT)(AJP), 2016 WL 4077114 (S.D.N.Y. Aug. 1, 2016):

Like a kid in the backseat of the car, I cannot help but ask, are we there yet? Hyles was published over two years ago now. Maybe some court, somewhere in the world, has already ordered a party to do predictive coding against their will, but not to our knowledge. That is a known unknown. Still, we are closer to “There” with the City of Rockford’s requirement of an Elusion test.

When we get “there,” and TAR is finally ordered in a case, it will probably arise in a situation like City of Rockford where a joint protocol applicable to all parties is involved. That is easier to sell than a one-sided protocol. The court is likely to justify the order by Rule 26(g), and hold that it requires all parties in the case to use predictive coding. Otherwise, they will not meet the  reasonable effort burdens of Rule 26(g). Other rules will be cited too, of course, including Rule 1, but Rule 26(g) is likley to be key.

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