Author: TSA Press

It’s the first week of September, and no matter what your age, it’s highly likely that your social media feeds have been infiltrated by hundreds of back-to-school pictures and status updates posted by proud parents — and sometimes, by even prouder law school classmates (and while we’re at it, admit it, you’ve probably even posted about your one or two of your own educational milestones at the beginning of the school year).

Guess which celebrity law student posted her own back-to-school photo this week?

Earlier this week, Trump celebrated her “last first day of school” at Georgetown Law, where she’s set to complete her third and final year of law school this spring. Although we’re not quite sure what Trump has planned for the future after graduation, we believe she may take steps to continue in her criminal justice reform work.

Best of luck as a 3L, Tiffany!

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Staci Zaretsky is a senior editor at Above the Law, where she’s worked since 2011. She’d love to hear from you, so please feel free to email her with any tips, questions, comments, or critiques. You can follow her on Twitter or connect with her on LinkedIn.

Ed. note: This is the second article in a two-part series about artificial intelligence, its potential impact on how organizations approach security, and the accompanying considerations around implementation, efficacy, and compliance. Read the first article here.

When you think of artificial intelligence (AI), you probably think of smart home devices turning off lights and setting thermostats, or sending notifications when new seasons of your favorite Netflix show launch.  In fact, the most common use of AI is in detecting and deterring security intrusions, according to research from the Consumer Technology Association.  Nearly half of organizations (44 percent), report the use of AI-enhanced tools to protect data, over pursuits such as financial trading (at 17 percent) or anticipating consumer buying preferences (at 19 percent).

The use of AI to solve against security challenges through predictive machine learning already underway.  One salient proof-point is DARPA’s recent investment of more than $2 billion in new and existing programs to push beyond second-wave machine learning techniques towards contextual reasoning capabilities. Experts assess that AI will bring greater speed and accuracy in detecting and responding to breaches, analyzing user behavior, predicting new threats and uncovering installed malware on the network. Yet this technology is by no means a panacea, and as with any new technology, there are risks in lockstep with the rewards.

Create a Solid Foundation

Before integrating AI into your security protocols, make sure to first have a strong technical security foundation in place, says Will Pearce, Senior Operator with Silent Break Security, a cybersecurity consulting firm. “Don’t look to machine learning to make up for deficiencies in technical controls or policy.”  He also advises that security teams apply AI to a problem that’s already well understood in the organization. “If you don’t understand Windows events before machine learning, you’re not going to understand them after.”

After all, as with any major technology change, a poorly planned AI strategy is a waste of time and money.  Assessing the environment for AI is therefore a critical first step.  Document the overall state of network security, including the completeness of data which any security tools collect, says Peter Clay, COO of Dark Cubed, a cybersecurity software platform that detects cyber threats. More specifically, the head of IT security should know what “normal” looks like for the network and/or area of defense so that this baseline can inform irregularities as they present.

It’s vital that companies have a firm grasp on the network architecture, its scope, and assets before proceeding with AI.  Without that knowledge, AI can change from a benefit to a liability. “What do you get when you automate stupid?” Clay asks. “Faster stupid.”

Next, before selecting tools, conduct a thorough assessment of security risks and mitigation strategies.  Answer three simple questions, Clay advises:

• Where does my valuable/protected data live?
• What is protecting my data today?
• What do I need to have to protect my data tomorrow?

AI excels at automating tasks with great rapidity and accuracy.  In that light, Clay suggests starting with an AI tool for endpoint detection and response (EDR) on laptops and mobile devices. EDR is endpoint security software designed to help organizations identify, stop, and react to threats that have bypassed other defenses.  “There are a number of products on the market that allow you to scale one administrator for up to 65,000 users because of the automation and capabilities,” Clay says.

Manage the Risks

As with any new technology, implementation is key.  Taking time to understand business needs for security and the limits of AI can set the stage for a successful deployment.  Those limits, say Pearce, relate to immaturity of the technology and how to deploy it.  “Machine learning systems still suffer from the classic issues of false positives, poor software development practices, misconfigurations, and a lack of network logging.”

Going back to the all-important ingredient of comprehensive, accurate data to feed the machine learning engine, Pearce advises that security organizations invest in network logging and alerting products and allot sufficient time to learning the products.  Top considerations include having the required budget for advanced logging systems or services and taking into account all privacy considerations that accompany the collection and responsible management of data sets.  It’s also necessary to integrate data from different systems to feed into the AI tool, yet that means greater challenges in protecting personally identifiable information of employees and customers. IT will need to collaborate closely with legal teams to evaluate the legal risk of using even anonymized customer data in these machine learning engines.

Beyond data accuracy and privacy, according to Clay, IT stakeholders should determine whether it’s possible to “tune” the algorithms without highly specialized AI expertise. “Is it akin to black magic or can I understand and adjust the logic to meet my needs?” And although difficult to calculate, don’t move forward with AI unless it has a lower total cost of ownership than the current systems and human analysis in place for security management, Clay advises.

Tread Lightly into the Great Unknown

It’s easy to get caught up in the appeal of artificial intelligence.  But the reality is that a 360-degree view of AI which takes into account all the benefits and risks will pay the biggest dividends.  And, any AI tool on your network could feasibly be turned against your organization to initiate an attack by a talented hacker. So start slowly and measure the results of early efforts frequently as you go.

In a 2018 Harvard Business School survey, 75 percent of the 250 executives familiar with their companies’ use of cognitive technology report that AI will substantially transform their companies within three years. Notably, the study of 152 projects demonstrated a lack of confidence in highly ambitious “moon shot” projects encompassing AI over less ambitious projects to enhance business processes.

The study drives home the adage of not putting all the eggs in one basket, a sentiment echoed by industry experts like Pearce: “Arguably the confusion or skepticism surrounding machine-learning solutions comes from applying machine learning arbitrarily to every problem. Machine learning is just a tool, one of many tools organizations can choose from to protect and defend systems and data from breach.”

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Jennifer DeTrani is General Counsel and EVP of Nisos, a technology-enabled cybersecurity firm.  She co-founded a secure messaging platform, Wickr, where she served as General Counsel for five years.  You can connect with Jennifer on Wickr (dtrain), LinkedIn or by email at dtrain@nisos.com.

Today’s marketplace is teeming with new AI-powered technologies promising to transform the practice of law with ever-greater efficiencies and cost savings. Amid all the hype, selecting the right AI tool for your practice can be daunting.

Our friends at Mindcrest are offering a free webinar, Choosing the Right AI Practice Tools: Transactional and Litigation Use Cases, on September 24 at 1 p.m. ET. Mindcrest’s experts will share their widespread, firsthand experience using AI tools on transactional (contract lifecycle management) and litigation (eDiscovery) projects. The webinar is designed for in-house counsel as well as legal operations and project management teams seeking a practical framework on selection and use of CLM and Litigation tools.

Register for the webinar here.

Ed. note: Please welcome Dr. Adam Feldman of Empirical SCOTUS to the pages of Above the Law.

Since Donald Trump’s inauguration in 2017, his administration has made major changes to the federal judiciary. This includes filling two Supreme Court seats along with a total of 146 confirmed Article III (federal district, appellate, Supreme Courts, and the court of international trade) judges. Democrats saw their last Supreme Court nominee, Merrick Garland, fade away without a confirmation hearing. The entire balance of the federal judiciary has been transformed over the past couple of years and for the first time in recent history, Democratic groups are trying to make a campaign issue out of federal court reform. Several groups have come forward with plans to reform or transform the federal judiciary to varying degrees. These plans range from Pete Buttigieg’s court packing plan to attempts to employ different procedures for selecting judges and justices. One consistent critique that follows Democratic nominees though is their lack of consensus on a plan for the courts.

Last week, the group Demand Justice, headed by Brian Fallon and Christopher Kang, laid out a novel plan for judicial reform in an article in The Atlantic. This plan has the goal of bringing diversity to the federal judiciary and limiting nominees to lawyers that have not worked as corporate “Biglaw” partners. To this end, Fallon and co-author Christopher Kang wrote, “If Democrats truly want the next president’s arrival to mark an inflection point when it comes to the composition of the judiciary, we should elevate those whose full-time jobs were on the front lines defending our democracy during this dark period, not those whose paychecks were drawn from corporate clients.”

They follow this by laying out some bare bones methodology for how this would look in practice: “For starters, there is the question of how to define who counts as a corporate lawyer. We would define it as a lawyer who achieves partner status at a corporate-law firm — such as the large firms known collectively as Biglaw — or who serves as in-house counsel at a large corporation. This would mean lawyers who briefly worked as associates at firms during an early phase in their career would not be excluded.”

While corporate law experience may not be the optimal choice to fill 100 percent of the slots in the federal courts, an effort at increasing diversity is a far cry from total exclusion.  UC Irvine’s Professor Rick Hasen provided a thoughtful response explaining some of the problematic aspects he saw with this plan.  Concurrently, civil rights attorney Sasha Samberg-Champion opined that this proposal could have been designed as a bargaining chip to start the conversation of judicial reform rather than as a (potentially) binding contract. Democratic nominee Elizabeth Warren came out in favor of the substance of the plan, while not clearly on the side of committing to a no corporate lawyer rule.

Even with the laudable notion of a more diverse judiciary, aspects of this plan may actually function towards cutting off the nose to spite the face. That is to say — while diversity is an unquestionably noble attribute and something sorely needed in the courts, means to get there that include excluding a whole subset of incredibly qualified judicial candidates raise potential problems in their own rights.  There are examples of corporate lawyers who became great jurists, just as there are of corporate lawyers who have a penchant to rule in favor of big businesses (a common critique of the Roberts Court from the likes of Senator Sheldon Whitehouse among others).

Leaving the merits of this plan aside for a moment to focus on researching the link between a background as a corporate partner and federal judging, two things are immediately clear. In terms of free resources, even comprehensive sources like the FJC’s Biographical Directory of Article III Judges lacks specific information on judges’ work experience.  Take for instance Judge Leslie Abrams who sits on the District Court for the Middle District of Georgia.  The FJC’s website lists her law firm experience as “private practice” for the years 2003 through 2010. If we want to learn more, we cannot go to her bio on her court’s website. The court’s website actually doesn’t provide any information about Judge Abrams’s background.  Indeed, the most reliable source for this information appears to judges’ senate questionnaires filled out during the nomination process.  From this we see that Abrams worked as an associate at Skadden Arps and Kilpatrick Townsend during this period (note that Wikipedia and Ballotpedia have biographical information on many federal judges but it is often in summary form and thus is almost always incomplete).

Actually evaluating the merits of this no corporate lawyer as judges policy involves more than just theorizing. The Atlantic article, for example, states that upon examining current federal appeals court judges, “60 percent were once corporate-law partners.” The implications of this statistic are really in the eye of the beholder.  To some this may seem staggering, but to others this might be quite obvious. This statistic shows that the backgrounds of federal judges are not representative of the attorney population across the United States as a whole. But is this consequential?  Again, more information may help individual voters determine their positions on this issue for themselves.

It is important to unpack proposals such as that from Demand Justice to understand the ramifications and therefore grasp the costs and benefits of such a plan.  Demand Justice’s methodology for determining who fits within the exclusionary category is a bit vague.  This methodology, as explained earlier, would simply exclude former Biglaw partners and in-house counsel for large corporations from federal judgeships.  One way to begin gauging the merits of such a proposal is through examining what the repercussions would have been had such a plan been in effect at an earlier time. Since this is a proposal for Democratic candidates, we should focus on judges placed on federal courts by a Democratic president.

According to FJC counts, 271 judges were confirmed for their first federal judgeship under President Obama. Obama also nominated several already sitting judges to higher positions (for example, Justice Sotomayor from appeals court judge to Supreme Court Justice), but these elevated judges are not the focus here.  One vague point in Demand Justice’s methodology is its description of Biglaw partners. While there is no universal definition for a Biglaw partner, firm size is the major determining factor here. This analysis used 50 lawyers in a firm as a cut point between big and small law.  Fifty lawyers in a firm is generally good indicia that a firm has the capacity to handle matters for large corporate clients, although it is by no means the only way to gauge this.  Biglaw might mean much larger firms and so the number of judges who previously practiced in such firms might actually be much smaller than those identified herein. Since this notion of a strict cut point for Biglaw is inherently arbitrary though, and because lawyers in a boutique firm might very well handle the same corporate matters as big law attorneys, this at least gives us a place to start the conversation.

Approximately 98 of the 271 new judges, or about 36 percent of the judges identified in this sample, worked as partners at Biglaw firms (those over 50 attorneys in size) at some point prior to their nomination for federal judgeship. These 98 judges are composed of one international trade court judge, 15 courts of appeals judges, and 82 district court judges. While Justice Kagan was an associate at Williams & Connolly, she was never a partner and therefore would not be disqualified under the previously described standards.

The following figure has the positions for all 98 judges who would not be viable nominees if democrats banned nominations of previous Biglaw partners who worked at firms with a minimum of 50 attorneys (click to enlarge).^[1]

Three Obama nominees were previously partners at Morrison Foerster, three at Mayer Brown, and three at Munger Tolles. Munger Tolles employees between 450 and 500 attorneys including Obama’s Solicitor General, Donald Verrilli. Morrison Foerster has over 1,000 attorneys worldwide and Mayer Brown has over 1,500.

Not all firms on this list have such a large number of attorneys, though in multiple offices. Lightfoot Franklin, for example, has two offices, in Houston and Birmingham, and around 60 total attorneys.  When you look at the matters they handle though, many are for large corporate clients and involve large dollar value actions.

Under Demand Justice’s policy, not all potential judges who worked at these firms would be banned from judicial nominations. Several, like Kagan, worked as associates at these firms but did not continue on to the partner level. To get a sense of the firms where more than one of Obama’s first-time judges worked as either an associate or a partner, the next figure has these aggregate data (click to enlarge).

Five of Obama’s first-time judges worked at Morrison Foerster. Since we know from the previous figure that three were partners, two must have been associates. The other firms on this list are primarily some of the largest and most successful national and international law firms. These 54 attorneys turned judges hailing from prominent firms in one capacity or another underscores the importance presidents place on experience in highly recognized law practices. This is where many lawyers are trained, and where the majority of lawyers, especially those who matriculate from top-tier schools, go after law school.

Law.com regularly details the statistics of law grads that begin their legal work in Biglaw firms. The schools with the highest percent of 2018 grads going on to work in Biglaw firms according to law.com’s statistics were the following (click to enlarge):

Columbia had the highest percentage of grads beginning work at large law firms at 78 percent. The top 10 schools rounded out with Harvard at 58 percent, Georgetown at 56 percent, and University of Chicago at 55 percent of 2018 grads starting their legal careers in Biglaw firms. Suffice it to say that large numbers of lawyers graduating from top schools at least begin their careers in big law.

When we look at Obama’s confirmed judges, many came from the same top-tier schools (click to enlarge).

The vast majority of Obama’s confirmed federal judges came from the top three ranked law schools in the country: Harvard, Yale, and Stanford.  Beyond this, in general we see that Obama’s judges mainly hailed from a handful of top-tier law schools, and these law schools were predominately the nation’s top schools, and the schools where the largest percentage of grads went into Biglaw practice.  Perhaps one of the deficits in diversity stems from the few law schools that graduate the majority of federal judges.

Below is a table of all judges that would be potentially banned from seeking judgeships according to the Biglaw partner policy, along with their previous employment and the law school they attended.

Judge	Job	Law School	Court Level
Judge Robert Bacharach	Shareholder at Crowe and Dunleavy	Washington University School of Law	U.S. Court of Appeals
Judge Susan Carney	Counsel at Bredhoff Kaiser	Harvard Law School	U.S. Court of Appeals
Judge Morgan Christen	Partner at Preston Gates	Golden Gate University School of Law	U.S. Court of Appeals
Judge Michelle Friedland	Partner at Munger Tolles	Stanford Law School	U.S. Court of Appeals
Judge Pamela Harris	Partner at O’Melveny	Yale Law School	U.S. Court of Appeals
Judge Andrew Hurwitz	Osborn Maledon	Yale Law School	U.S. Court of Appeals
Judge William Kayatta	Partner Pierce Atwood	Harvard Law School	U.S. Court of Appeals
Judge Cheryl Krause	Partner at Dechert	Stanford Law School	U.S. Court of Appeals
Judge Carolyn McHugh	Partner at Parr Brown	University of Utah College of Law	U.S. Court of Appeals
Judge Patricia Millett	Partner at Akin Gump	Harvard Law School	U.S. Court of Appeals
Judge John Owens	Partner at Munger Tolles	Stanford Law School	U.S. Court of Appeals
Judge Jimmie Reyna	Partner at Williams Mullen	University of New Mexico School of Law	U.S. Court of Appeals
Judge Srikanth Srinivasan	Partner at O’Melveny	Stanford Law School	U.S. Court of Appeals
Judge Kara Stoll	Partner at Finnegan Henderson	Georgetown University Law Center	U.S. Court of Appeals
Judge Paul Watford	Partner at Munger Tolles	University of California, Los Angeles, School of Law	U.S. Court of Appeals
Judge Jennifer Groves	Partner at Hughes Hubbard	Columbia Law School	U.S. Court of International Trade
Judge Madeline Arleo	Partner at Tompkins, McGuire, Wachenfeld & Barry, LLP	Seton Hall University School of Law	U.S. District Court
Judge Kristine Baker	Partner at Quattlebaum, Grooms, Tull & Burrow	University of Arkansas School of Law	U.S. District Court
Judge Stanley Bastian	Partner at Jeffers, Danielson, Sonn & Aylward,	University of Washington School of Law	U.S. District Court
Judge Wendy Beetlestone	Partner at Schnader, Harrison, Segal & Lewis LLP	University of Pennsylvania Law School	U.S. District Court
Judge Cathy Bencivengo	Partner at DLA Piper	University of Michigan Law School	U.S. District Court
Judge Loretta Biggs	Partner at Davis, Harwell & Biggs and in-house at Coca-Cola	Howard University School of Law	U.S. District Court
Judge Cathy Bissoon	Director at Cohen Grigsby	Harvard Law School	U.S. District Court
Judge Timothy Black	Director at Graydon Head	Northern Kentucky University, Salmon P. Chase College of Law	U.S. District Court
Judge Victor Bolden	Counsel at Wiggin and Dana	Harvard Law School	U.S. District Court
Judge Vernon Broderick	Partner at Weil Gotshal	Harvard Law School	U.S. District Court
Judge Debra Brown	Shareholder at Wise Carter Child & Caraway	University of Mississippi School of Law	U.S. District Court
Judge Nannette Brown	Partner at Chaffe McCall	Tulane University Law School	U.S. District Court
Judge Allison Burroughs	Partner at Nutter McClennen	University of Pennsylvania Law School	U.S. District Court
Judge Claire Cecchi	Partner at McElroy, Deutsch, Mulvaney & Carpenter	Fordham University School of Law	U.S. District Court
Judge Julianna Childs	Partner at Nexsen Pruet	University of South Carolina School of Law	U.S. District Court
Judge Theodore Chuang	Counsel at WilmerHale	Harvard Law School	U.S. District Court
Judge Tanya Chutkan	Partner at Boies Schiller	University of Pennsylvania Law School	U.S. District Court
Judge Mark Cohen	Partner at Troutman Sanders	Emory University School of Law	U.S. District Court
Judge William Conley	Partner at Foley Lardner	University of Wisconsin Law School	U.S. District Court
Judge Christopher Cooper	Partner at Baker Botts	Stanford Law School	U.S. District Court
Judge Daniel Crabtree	Partner at Stinson Morrison	University of Kansas School of Law	U.S. District Court
Judge Waverly Crenshaw	Partner at Waller Lansden	Vanderbilt University Law School	U.S. District Court
Judge Jon DeGuilio	Partner at Barnes and Thornburg	Valparaiso University Law School	U.S. District Court
Judge Pedro Delgado Hernández	Partner at O’Neill & Borges	University of Puerto Rico School of Law	U.S. District Court
Judge James Donato	Partner at Cooley	Stanford Law School	U.S. District Court
Judge Thomas Durkin	Partner at Mayer Brown	DePaul University College of Law	U.S. District Court
Judge Sara Ellis	Counsel at Schiff Harden	Loyola University Chicago School of Law	U.S. District Court
Judge Paul Engelmayer	Partner at WilmerHale	Harvard Law School	U.S. District Court
Judge Katherine Failla	Partner at Morgan Lewis	Harvard Law School	U.S. District Court
Judge Gary Feinerman	Partner at Mayer Brown	Stanford Law School	U.S. District Court
Judge Audrey Fleissig	Partner at Peper, Martin, Jensen, Maichel and Hetlage	Washington University School of Law	U.S. District Court
Judge Katherine Forrest	Partner at Cravath	New York University School of Law	U.S. District Court
Judge Haywood Gilliam	Partner at Bingham McCutcheon	Stanford Law School	U.S. District Court
Judge Mark Goldsmith	Partner at Honigman Miller	Harvard Law School	U.S. District Court
Judge Madeline Haikala	Partner at Lightfoot Franklin	Tulane University Law School	U.S. District Court
Judge LaShann Hall	Partner at Morrison Foerster	Howard University School of Law	U.S. District Court
Judge Ellen Hollander	Partner at Frank, Bernstein, Conaway & Goldman	Georgetown University Law Center	U.S. District Court
Judge Charlene Honeywell	Partner at Hill Ward	University of Florida College of Law	U.S. District Court
Judge Mark Hornak	Partner at Buchanan Ingersoll	University of Pittsburgh School of Law	U.S. District Court
Judge Diane Humetewa	Counsel at Squire Sanders	Arizona State University College of Law	U.S. District Court
Judge Amy Jackson	Partner at Venable	Harvard Law School	U.S. District Court
Judge Brian Jackson	Liskow and Lewis	Georgetown University Law Center	U.S. District Court
Judge Ketanji Jackson	Counsel at Morrison Foerster	Harvard Law School	U.S. District Court
Judge Richard Jackson	Partner at Holland and Hart	Harvard Law School	U.S. District Court
Judge Abdul Kallon	Partner at Bradley Arant	University of Pennsylvania Law School	U.S. District Court
Judge Lucy Koh	Partner at McDermott	Harvard Law School	U.S. District Court
Judge John Kronstadt	Partner at Arnold & Porter	Yale Law School	U.S. District Court
Judge William Kuntz	Partner at Seward & Kissel	Harvard Law School	U.S. District Court
Judge John Lee	Partner at Freeborn & Peters	Harvard Law School	U.S. District Court
Judge Matthew Leitman	Partner at Miller Canfield	Harvard Law School	U.S. District Court
Judge John McConnell	Partner at Motley Rice	Case Western Reserve University School of Law	U.S. District Court
Judge Travis McDonough	Partner at Miller and Martin	Vanderbilt University Law School	U.S. District Court
Judge Kevin McNulty	Partner at Gibbons PC	New York University School of Law	U.S. District Court
Judge Amit Mehta	Partner at Zuckerman Spader	University of Virginia School of Law	U.S. District Court
Judge Laurie Michelson	Partner at Butzel Long	Northwestern University School of Law	U.S. District Court
Judge James Moody	Partner at Wright, Lindsey, Jennigns	University of Arkansas School of Law	U.S. District Court
Judge Raymond Moore	Parner at Davis Graham	Yale Law School	U.S. District Court
Judge Randolph Moss	Partner at WilmerHale	Yale Law School	U.S. District Court
Judge Susan Nelson	Partner at Robins Kaplan	University of Pittsburgh School of Law	U.S. District Court
Judge James Oetken	GC at Cablevision Systems	Yale Law School	U.S. District Court
Judge William Orrick	Partner at Coblentz Patch	Boston College Law School	U.S. District Court
Judge Gerald Pappert	Partner at Ballard Spahr	Notre Dame Law School	U.S. District Court
Judge Linda Parker	Partner at Dickinson Wright	George Washington University Law School	U.S. District Court
Judge Jill Parrish	Partner at Parr Brown	Yale Law School	U.S. District Court
Judge James Peterson	Partner at Godfrey Kahn	University of Wisconsin Law School	U.S. District Court
Judge Edgardo Ramos	Partner at Day Pitney	Harvard Law School	U.S. District Court
Judge Yvonne Rogers	Partner at Cooley	University of Texas School of Law	U.S. District Court
Judge Robin Rosenberg	Partner at Holland & Knight	Duke University School of Law	U.S. District Court
Judge Lorna Schofield	Partner at Debevoise	New York University School of Law	U.S. District Court
Judge Richard Seeborg	Partner at Morrison Foerster	Columbia Law School	U.S. District Court
Judge Michael Shea	Partner at Day Pitney	Yale Law School	U.S. District Court
Judge Robert Shelby	Partner at Snow Christensen	University of Virginia School of Law	U.S. District Court
Judge Michael Simon	Partner at Perkins Coie	Harvard Law School	U.S. District Court
Judge Josephine Staton	Partner at Morrison Foerster	Harvard Law School	U.S. District Court
Judge Leonard Strand	Partner at Simmons Perrine	University of Iowa College of Law	U.S. District Court
Judge Indira Talwani	Partner at Altshuler Berzon	University of California, Berkeley, Boalt Hall School of Law	U.S. District Court
Judge John Tharp	Partner at Mayer Brown	Northwestern University School of Law	U.S. District Court
Judge Jon Tigar	Partner at Keker & Van Nest	University of California, Berkeley, Boalt Hall School of Law	U.S. District Court
Judge Michael Urbanski	Partner at Woods Rogers	University of Virginia School of Law	U.S. District Court
Judge Derrick Watson	Partner at Farella Braun + Martel	Harvard Law School	U.S. District Court
Judge Robert Wilkins	Partner at Venable	Harvard Law School	U.S. District Court
Judge Gregory Woods	Partner at Debevoise	Yale Law School	U.S. District Court

Many of the judges who would not be disqualified under this Biglaw partner rule worked in private practices as solo practitioners or with a limited number of partners. This does not necessarily mean they represented different clients than other judicial nominees, but with smaller firms they likely had fewer resources to manage multiple large accounts. Thus, firm size might be a good proxy for the extent of this type of business a firm or attorney can handle.

In the end, this is a question of what matters in judging. Even if we take the importance of diversity on the federal bench as a given, the means to seek this are still unclear.  The bottom line is that voters need to ask themselves what needs to be reformed. We have clusters of judges coming from a top law schools, going into big firm jobs, and we see some continue in these jobs while others go into public interest work, academia, or other non-Biglaw jobs.  Many of Obama’s picks for federal judges worked in the Biglaw world and a large minority went on to become partner in such firms. This may affect the the way they decide cases, but other factors such as political preferences have been shown to explain a large percentage of voting behavior as well.  Further statistical research into this subject may illuminate whether the relationship between practicing as a Biglaw partner and supporting corporate interests in judging is truly robust. Without this research, such inferences remain at least somewhat speculative.

Ultimately, voters need to decide what the most important factors are in choosing future federal judges just as politicians must decide on certain criteria that will determine the pool of potential judges they choose from. This may be related to judges’ previous work experiences although this equally may be related to other factors that influence judges’ decisions.  More studies and greater resources may help us understand the correlates between judges’ backgrounds and votes for corporate causes. At this point though, even if the public’s goal is to minimize judges’ votes for corporate causes, the direct link between practicing as a corporate partner and such votes is unclear, and evidence is sparse that practicing as a corporate partner is the most decisive attribute leading to such voting practices.

[1] Note that the firms collected here are determined by size and not by practice specialization. It is entirely possible that some firms would not fit the corporate law model described by Demand Justice. Additional information on exactly what constitutes a corporate firm would help further clarify which firms might not fit into this category.

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Dr. Adam Feldman is an attorney with a J.D. from U.C. Berkeley, Boalt Hall School of Law. He also has a Ph.D. in political science from the University of Southern California. Dr. Feldman previously practiced as a trial lawyer in large and boutique firm settings in the Los Angeles area. He has expertise in statistical analysis and data modeling and has published a variety of articles in peer and law reviewed journals and book chapters in these areas. Dr. Feldman currently teaches political science and law courses and consults on legal strategy for law firms and interest groups.

A federal judge for the District of Columbia ruled on Wednesday that the CVS-Aetna merger that he was reviewing is in the “public interest” thereby removing a final barrier to create a healthcare behemoth.

Judge Richard Leon was probing the merger that the government had approved and there was a chance that he would block the deal, even though CVS formally completed the acquisition of Aetna in November 2018.

In response to the favorable decision, CVS issued a short statement:

CVS Health and Aetna have been one company since November 2018, and today’s action by the district court makes that 100 percent clear.  We remain focused on transforming the consumer health care experience in America.

Opponents of the merger, which included the American Medical Association had argued that Aetna’s divestiture of WellCare — was not enough to alleviate the harm to patients as lack of competition in the marketplace would occur. They argued that WellCare is even a smaller player than Aetna in the prescription drug plan market. However, the judge appeared to be persuaded by CVS’ argument that CMS makes it very easy for people to switch such plans. That was one among several arguments against the union.

Judge Leon concluded:

Although amici raised substantial concerns that warranted serious consideration, CVS’ s and the Government’s witnesses, when combined with the existing record, persuasively support why the markets at issue are not only very competitive today, but are likely to remain so post-merger. Consequently, the harms to the public interest the amici raised were not sufficiently established to undermine the Government’s conclusion to the contrary

The reaction from opponents of the merger and the government’s consent decree to allow the merger to occur under certain guidelines was swift.

“Despite an unprecedented review that dragged many details of this merger into the light, today’s decision ultimately fails patients, will likely raise prices, lower quality, reduce choice, and stifle innovation,” said Dr. Patrice A. Harris, president, American Medical Association in an emailed statement. “The American people and our health system will not be served well by allowing a merger that combines health insurance giant Aetna Inc. with CVS Health Corporation – the nation’s largest retail pharmacy chain, specialty pharmacy, pharmacy benefit management (PBM) and Medicare Part D Stand-Alone Prescription Drug Plan (PDP) insurer.”

How does crazy shit like this even happen? How does Donald Trump wind up on national television next to a weather map which has very clearly been doctored with a Sharpie to put the state of Alabama in Hurricane Dorian’s path?

DHS director Kevin McAleenan cannot possibly have showed up in the Oval Office with that map. The man is not a complete idiot; he’d at least have used Wite-Out.

But you know who does like to write in Sharpie?

And indeed Bloomberg confirmed that Trump “personally altered” the weather prediction, adding “Doctoring Weather Maps” to his list of, uh … “achomlishments.”

But he’s a modest fellow, so he demurred when asked if he colored that pretty picture all by himself.

The president repeatedly insisted on Sunday that Hurricane Dorian was going to strike the Atlantic coast and the Gulf Coast, spreading destruction clear across the Florida panhandle into Alabama, while simultaneously wreaking havoc in Georgia and the Carolinas. Whether the inclusion of the Yellowhammer State in storm’s flightpath “just came up” or was from “original scenarios” Trump could not say. But it was most definitely not forecast on Sunday, August 29, when Trump was telling Alabama to stay safe.

Here’s Donald Trump receiving a briefing on Sunday, September 1, inexplicably using a real map that was already three days out of date, but still showed no danger to Alabama. (Only the best people!)

Which didn’t stop him from scream-tweeting at ABC’s Jon Karl for pointing out that he’d pulled that threat to Alabama out of his ample rear end.

Why would Trump use an outdated hurricane map from August 29 on September 1 and again yesterday on the 4th, albeit in Sharpie-edited form? To prove his detractors wrong, of course!

It was going toward the Gulf. That was what was originally projected, and it took a right turn and ultimately — hopefully, we’re going to be lucky.

No doubt the New York Times will accuse President Geography Bee of being “meteorologically insensitive.” But here on Planet Earth, it was never “going toward the Gulf.” That would be … A LIE.

And yet, he’s arglebargling still, citing ever-earlier forecasts to prove somehow that Alabama was actually in danger on Sunday.

Can we zoom in on the date there?

How is this even real? And what does it have to do with the law, aside from the 25th Amendment solution, which is clearly never going to happen since Cabinet members Kevin McAleenan and Alex Azar sat right there looking at this week-old, defaced map like it was totally normal.

Well, as Forbes science writer Dennis Mersereau pointed out on Twitter,
18 U.S. Code §2704 provides that:

Whoever knowingly issues or publishes any counterfeit weather forecast or warning of weather conditions falsely representing such forecast or warning to have been issued or published by the Weather Bureau, United States Signal Service, or other branch of the Government service, shall be fined under this title or imprisoned not more than ninety days, or both.

And this “counterfeit weather forecast” which Trump falsely attributed to the National Weather Service, pretty clearly fits the bill.

No doubt Attorney General Barr will get right on it. But probably he’ll wait ’til after the holidays, since he booked that $30,000 Christmas party at the Trump International Hotel in D.C., and it would be such a downer to lose his deposit.

THIS IS FINE.

Trump Altered Dorian Map to Show Storm Threatened Alabama [Bloomberg]

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Elizabeth Dye lives in Baltimore where she writes about law and politics.

In last week’s column, I shared some of the ILTACON 2019 news that caught my eye while I was in Orlando covering the conference in mid-August. In today’s post, the second and final installment of my ILTACON news roundup, you’ll learn about some of the latest announcements from Intapp, NetDocuments, iManage, and LexisNexis.

First up, Intapp, a company I’ve followed closely over the years.  Not only is their legal software product line always evolving and forward-thinking, but their ILTACON parties are my hands-down favorite! This year’s party did not disappoint, with lots of great wine and fantastic food, along with an incredible view from the Four Seasons rooftop to boot.

But parties aside, let’s move on to what really matters: Intapp’s legal software. To learn about their ILTACON news, I spoke with Jose Lazares, Vice President of Product Strategy and Business Management. He told me about Intapp Conflicts, their latest release which dramatically reduces the time spent on conflict check searches by using AI natural language processing to sort through and surface relevant conflicts. According to Lazares, Intapp Conflicts eliminates 90 percent of irrelevant hits and provides “hit” results that are sorted into 3 buckets: 1) known hits, 2) possible hits, and 3) irrelevant results. From there the user is able to easily sort through the results to determine if any conflicts exist.

Next, I spoke with Mike Sanders, Senior Solutions Expert with NetDocuments. For starters, he shared news of its growth: 35 percent annual growth, with more than 435 customer firms having gone live on NetDocuments in the last 12 months.  We also discussed some of their newer products like SetBuilder, Collabspaces, and ndMail.  With SetBuilder, customers can coordinate closings, automate manual closing binder processes, and drastically accelerate the time-consuming and manual closing process. CollabSpaces makes it easy for customers to share content securely with external users without having to transfer content to a third-party file sharing service or extranet platform. Finally, ndMail is a predictive email management platform that provides filing recommendations that help users quickly and easily file, store, and organize emails into the firm’s matter files stored in NetDocuments.

I also met with Dan Carmel, CMO, and Anne Kokke, VP of Marketing, at iManage. Among other things, we discussed iManage’s new Threat Manager tool which uses AI technology to provide firms with a proactive approach to protecting sensitive client data. One way Threat Manager does this is by identifying patterns in the typical behavior of past employees who have left firms and taken sensitive data with them, in part based on the AI’s understanding of the type of client and matter. By doing so, the software offers continuous protection from both internal and external threats by building a digital model of a user’s activity based on the AI’s understanding of that user’s behavior in the context of a particular matter. Patterns detected by the software can then be used to identify potential threats and thus prevent the loss of confidential data.

And last, but not least, I spoke with Serena Wellen, Senior Director with LexisNexis, and she demoed their new Lexis Advance AI research assistant, Counsel. This intelligent research assistant makes research more personal, guided, and conversational. It does this by transparently tracking a user’s activity in Lexis to provide context and personalized results. One thing it does with that information is create a map of the user’s activity in the sidebar. The sidebar includes the user’s step-by-step activity roadmap, making it easy for the user to easily return to a specific point in the research trail. Counsel also allows a user to search for a term within the sidebar, and then search within those search results for a sub-term in order to locate documents from a past search that might be relevant to a new case or issue. Finally, Counsel can also be used to search for definitions or information in a case you’re viewing and in the future it will also link to other types of data from LexisNexis products like Lex Machina. Right now, Counsel has been released in beta to a small subset of customers, but will be released more widely in the future.

So that’s a wrap! ILTACON 2019 is behind us, and I’m looking forward to next year’s conference. Hope to see you there!

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Nicole Black is a Rochester, New York attorney and the Legal Technology Evangelist at MyCase, web-based law practice management software. She’s been blogging since 2005, has written a weekly column for the Daily Record since 2007, is the author of Cloud Computing for Lawyers, co-authors Social Media for Lawyers: the Next Frontier, and co-authors Criminal Law in New York. She’s easily distracted by the potential of bright and shiny tech gadgets, along with good food and wine. You can follow her on Twitter @nikiblack and she can be reached at niki.black@mycase.com.

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* Greg Craig was acquitted! Good news for all the lobbyists and foreign agents out there who (wink wink) aren’t lobbyists and foreign agents. [WSJ]

* Department of Labor official resigns after anti-Semitic social media posts surface. Frankly, one would’ve expected him to stay to own the libs. [Bloomberg Law]

* CVS and Aetna get their clearance to merge because despite all Judge Leon’s rage at DOJ he’s still just a rat in a cage that happens to keep people from caring about antitrust enforcement. [Law360]

* Simple way to fix harassment in Silicon Valley. [The Atlantic]

* It’s a day that ends in “y” so Dentons just got bigger. [Dentons]

* Does Chambers have a blindspot for women? [Careerist]

* For those of you following the Alphabet/Google CLO shenanigans, the GC just married an employee this weekend, but not the employee who says he neglected their baby after he had an affair with her while married to yet another person. [CNBC]

FILE PHOTO: Zimbabwean Finance Minister Mthuli Ncube looks on before the swearing in of new cabinet ministers at State House in Harare, Zimb

The gate at the former tobacco farmer, Shandu Gumede’s farm in Nyamandlovu, Umguza District, in Matabeleland North Province, Zimbabwe that she is now reportedly leasing to a cattle farmer. [Daylife]