The first of its kind, Paul T. Jaeger & Natalie Greene Taylor, Foundations of Information Policy (ALA Neal-Schuman, 2019) provides a much-needed introduction to the myriad information policy issues that impact information professionals, information institutions, and the patrons and communities served by those institutions. In this key textbook for LIS students and reference text for practitioners, noted scholars Jaeger and Taylor —

  • draw from current, authoritative sources to familiarize readers with the history of information policy;
  • discuss the broader societal issues shaped by policy, including access to infrastructure, digital literacy and inclusion, accessibility, and security;
  • elucidate the specific laws, regulations, and policies that impact information, including net neutrality, filtering, privacy, openness, and much more;
  • use case studies from a range of institutions to examine the issues, bolstered by discussion questions that encourage readers to delve more deeply;
  • explore the intersections of information policy with human rights, civil rights, and professional ethics; and
  • prepare readers to turn their growing understanding of information policy into action, through activism, advocacy, and education.

The ABA Profile of the Legal Profession survey reports when lawyers begin a research project 37% say they start with a general search engine like Google, 31% start with a paid online resource, 11% start with a free state bar-sponsored legal research service and 8% start with print resources.

A large majority (72%) use fee-based online resources for research. Westlaw is the most-used paid online legal research service, used by nearly two-thirds of all lawyers (64%) and preferred over other paid online services by nearly half of all lawyers (46%).

When it comes to free websites used most often for legal research 19% said Cornell’s Legal Information Institute, followed by Findlaw, Fastcase and government websites (17% each), Google Scholar 13%, and Casemaker 11%. Despite the popularity of online sources, 44% still use print materials regularly.

The survey also reports that 10% of lawyers say their firms use artificial intelligence-based technology tools while 36% think artificial intelligence tools will become mainstream in the legal profession in the next three to five years.

In thinking about dual provider choices for legal information vendors in the BigLaw market, I believe we tend to think the licensing equation is (Westlaw + Lexis Advance). Why? The answer may be that we tend to divide the marketplace for commercial legal information into two unique and close-to-mutually exclusive segments: general for core legal search provided by WEXIS and specialty for practice-specific legal search provided by Bloomberg BNA and Wolters Kluwer. This perspective assumes the adoption of BBNA and WK is only on a practice group/per seat basis while the adoption of WEXIS is on an enterprise/firm-wide basis. In addition to perceptions on editorial quality, where topical deep dives are expected from BBNA and WK but not WEXIS, perceived vendor pricing policies have influenced our take on the structure of this market.

According to Feit Consulting, the reality is quite different. Approximately 89% of AmLaw 200 firms license Wolters Kluwer and 72% of those WK firms license this service in an enterprise/firm-wide pricing plan, not on a practice group/per seat plan. That 72% figure means WK’s firm-wide install base in the AmLaw 200 is approximately 64%, or almost the same as Lexis Advance’s install rate in BigLaw.

The dual provider licensing equation really appears to be (Westlaw) + (Lexis or Wolters Kluwer). This is reinforced by statistics from Feit on the likelihood of vendor cancellation. Only 14% of Westlaw firms and 12% of WK firms are extremely or moderately likely to be eliminated. That’s less than half the number of firms extremely or moderately likely to eliminate Lexis (30%) and BBNA (29%). For dual provider firms, (Westlaw) + (Lexis or Wolters Kluwer) appears to be a well established equation.

ROSS Intelligence goes after “legacy” search platforms (i.e., WEXIS) in this promotional blog post, How ROSS AI Turns Legal Research On Its Head, Aug. 6, 2019. The post claims that ROSS supplants secondary analytical sources and makes West KeyCite and LexisNexis Shepard’s obsolete because its search function provides all the relevant applied AI search output for the research task at hand. In many respects, Fastcase and Casetext also could characterize their WEXIS competitors as legacy legal search platforms. Perhaps they have and I have just missed that.

To the best of my recollection, Fastcase, Casetext and ROSS have not explicitly promoted competition with each other. WEXIS has always been the primary target in their promotions. So why are Fastcase, Casetext and ROSS competing with each other in the marketplace? What if they joined forces in such a compelling manner that users abandon WEXIS for core legal search? Two or all three of the companies could merge. In the alternative, they could find a creative way to offer license-one-get-all options.

Perhaps the first step is to reconsider the sole provider option. It’s time to revise the licensing equation; perhaps it should be (Westlaw or Lexis) + (Fastcase or Casetext or ROSS).

H/T to Bob Ambrogi for featuring results from the 2019 Aderant Business of Law and Legal Technology Survey. The survey results answered the question: What technology tools rank most important to lawyers in driving efficiency? In the section on technology tools and cloud adoption, the survey asked lawyers about the technology tools that have the greatest impact on their ability to work efficiently and manage their work effectively. Out of 18 categories of tools, the two lowest ranked were AI and blockchain. Knowledge management ranked seventh. Details on LawSites.

Andrew Martineau’s Reinforcing the ‘Crumbling Infrastructure of Legal Research’ Through Court-Authored Metadata, Law Libr. J. (Forthcoming) “examines the role of the court system in publishing legal information and how that role should be viewed in a digital, online environment. In order to ensure that the public retains access to useful legal information into the future, courts should fully embrace the digital format by authoring detailed, standardized metadata for their written work product—appellate-level case law, especially. If court systems took full advantage of the digital format, this would result in immediate, identifiable improvements in free and low-cost case law databases. Looking to the future, we can speculate on how court-authored metadata might impact the next generation of “A.I.”-powered research systems. Ultimately, courts should view their metadata responsibilities as an opportunity to “reinforce” the structure of the law itself.”

“Natural language generation (NLG) is a subset of natural language processing (NLP) that aims to produce natural language from structured data,” wrote Sam Del Rowe. “It can be used in chatbot conversations, but also for various types of content creation, such as summarizing data and generating product descriptions for online shopping. Companies in the space offer various use cases for this type of automated content creation, but the technology requires human oversight—a necessity that is likely to remain in the near future.” For more, see Get Started With Natural Language Content Generation, EContent, July 22, 2019.

Your litigation analytical tool says your win rate for summary judgement motions in class action employment discrimination cases is ranked the best in your local jurisdiction according to the database used. Forget the problem with using PACER data for litigation analytics, possible modeling error or possible bias embedded in the tool. Can you communicate this applied AI output to a client or potential client? Are you creating an “unjustified expectation” that your client or potential client will achieve the same result for your next client matter?

According to the ABA’s Model Rules of Professional Conduct Rule 7.1, you are probably creating an “unjustified expectation.” However you may be required to use that information under Model Rule 1.1 because that rule creates a duty of technological competence. This tension between Model Rule 7.1 and Model Rule 1.1 is just begining to be played out.

For more, see Roy Strom’s The Algorithm Says You’ll Win the Case. What Do You Say? US Law Week’s Big Law Business column for August 5, 2019. See also Melissa Heelan Stanzione, Courts, Lawyers Must Address AI Ethics, ABA Proposal Says, Bloomberg Law, August 6, 2019.

From the introduction to Resolutions to Censure the President: Procedure and History (R45087, Updated August 1, 2019):

Censure is a reprimand adopted by one or both chambers of Congress against a Member of Congress, President, federal judge, or other government official. While Member censure is a disciplinary measure that is sanctioned by the Constitution (Article 1, Section 5), non-Member censure is not. Rather, it is a formal expression or “sense of” one or both houses of Congress.

Resolutions attempting to censure the President are usually simple resolutions. These resolutions are not privileged for consideration in the House or Senate. They are, instead, considered under the regular parliamentary mechanisms used to process “sense of” legislation.

This report summarizes the procedures that may be used to consider resolutions of censure and the history of attempts to censure the President (1st-115th Congresses). It also provides citations to additional reading material on the subject.

The Association of Research Libraries (ARL) has published the ARL Annual Salary Survey 2018–2019. From the announcement:

“This report analyzes salary data for all professional staff working in the 124 ARL member libraries during FY2018–2019. Data for 10,718 professional staff members were reported this year for the 116 ARL university libraries, including their law and medical libraries (856 staff members reported by 72 medical libraries and 712 staff members reported by 74 law libraries). For the eight nonuniversity ARL members, data were reported for 3,318 professional staff members.”

The 2018–2019 data show that salaries for professionals in Canadian and US ARL university libraries salaries did not keep pace with inflation; whereas, salaries for professionals in US federal and public ARL libraries surpassed inflation. The median salary for professionals in US ARL university libraries in 2018–2019 was $74,482, an increase of 1.5% over the 2017–2018 median salary of $73,357. The US CPI rose 2.9% during the same period. The Canadian CPI rose 3%, and median salaries in Canadian university libraries increased from $99,912 (Canadian dollars) to $100,699 (Canadian dollars), a rise of 0.8%. The median salary for US federal and public ARL libraries increased 5.7% from $90,067 in 2017–2018 to $95,166 in 2018–2019.

H/T InfoDocket.

From the abstract for Josh Blackman, Originalism and Stare Decisis in the Lower Courts (July 24, 2019):

The tension between originalism and stare decisis is well known. Many of the Supreme Court’s most significant constitutional decisions are completely unmoored from the original public understanding of the Constitution. A Supreme Court Justice may recognize that a given precedent is non-originalist, but follow it anyway because of the doctrine of stare decisis. Or, a Supreme Court Justice may decide to deviate from stare decisis because that precedent is non-originalist. The Supreme Court’s unique status, which is perched atop our judiciary, affords its members leeway to make either decision.

Lower court judges, however, do not have that sort of discretion. Consider a judge on a federal circuit court of appeals. First, she is bound by Supreme Court precedents interpreting the Constitution, regardless of whether those precedent are originalist or not. No matter how wrong a given Supreme Court case is, that precedent must be followed. Second, she is bound by circuit precedent interpreting the Constitution, regardless of whether that precedent is originalist or not. Only an en banc majority can reverse circuit precedent, and those proceedings are quite rare.

An originalist circuit judge would only have free jurisprudential rein in the rare case of first impression, where neither the Supreme Court nor the circuit court had considered a particular constitutional question. Those cases are even rarer. Even then, the circuit judge would still be at a disadvantage. Circuit courts seldom receive the wealth of originalist party and amicus briefs that are directed to the Supreme Court. Here, the circuit judge will often have to do all of her own originalist research—the proverbial law office history report—without the benefit of the adversarial process.

In short, it’s tough for a lower-court judge to be a constitutional originalist. But it can be done. Part I of this essay explains when a lower-court judge can be an originalist. Part II explains how a lower-court judge can be an originalist.

From the abstract for Clark D. Asay, Artifical Stupidity, 61 William & Mary Law Review (2020, Forthcoming):

Artificial intelligence is everywhere. And yet, the experts tell us, it is not yet actually anywhere. This is so because we are yet to achieve true artificial intelligence, or artificially intelligent systems that are capable of thinking for themselves and adapting to their circumstances. Instead, all the AI hype — and it is constant — concerns rather mundane forms of artificial intelligence, which are confined to performing specific, narrow tasks, and nothing more. The promise of true artificial intelligence thus remains elusive. Artificial stupidity reigns supreme.

What are the best set of policies to achieve true artificial intelligence? Surprisingly, scholars have paid little attention to this question. Scholars have spent considerable time assessing a number of important legal questions relating to artificial intelligence, including privacy, bias, tort, and intellectual property issues. But little effort has been devoted to exploring what set of policies are best suited to helping artificial intelligence developers achieve greater levels of innovation. And examining such issues is not some niche exercise, since artificial intelligence has already or soon will affect every sector of society. Hence, the question goes to the heart of future technological innovation policy more broadly.

This Article examines this question by exploring how well intellectual property rights promote innovation in artificial intelligence. I focus on intellectual property rights because these are often viewed as the most important piece of United States innovation policy. Overall, I find that intellectual property rights, particularly patents, are ill-suited to promote radical forms of artificial intelligence innovation. And even the intellectual property forms that are a better fit for artificial intelligence innovators, such as trade secrecy, come with problems of their own. In fact, the poor fit of patents in particular is likely to contribute to heavy industry consolidation in the AI field, and heavy consolidation in an industry is typically associated with lower levels of innovation than ideal.

I conclude by arguing, however, that neither strengthening AI patents rights nor looking to other forms of law, such as antitrust, holds much promise in achieving true artificial intelligence. Instead, as with many earlier radical innovations, significant government backing, coupled with an engaged entrepreneurial sector, is at least one key to avoiding enduring artificial stupidity.