AI and Legal Services in 2020

By Alex Crowley posted 02-04-2020 14:49


By Alex Crowley[i]

With the start of the new year, some in the legal industry predict that artificial intelligence (“AI”) will play an increasingly influential role in the delivery of legal services.[ii] Predictions like “2020 is the year we move from AI hype to AI in practice” and “2020 [will] bring a greater understanding of how [data analytics and AI] enhance, rather than overtake, the work of lawyers” are notable because they hint at how the discussion around AI’s role in legal-services delivery is maturing.[iii] This article explores how AI is currently used in the legal marketplace, AI’s limitations, and critical questions that lawyers should ask about the continued rise of AI.

Definition of AI

Put simply, AI is the use of “technology to automate tasks that ‘normally require human intelligence.’”[iv] Such tasks include the human abilities of reasoning, analyzing, generalizing, problem-solving, and iterative learning.[v] AI systems are classified as “strong” or “weak” based on the degree to which they can perform human abilities.[vi] That distinction helps delineate the scope of existing AI systems’ capabilities, although those capabilities fall across a spectrum rather than into distinct categories.[vii]

AI is truly “strong” when it can at least “successfully perform any intellectual task that a human being can,” if not also have the ability to experience consciousness and truly understand concepts like language.[viii] That kind of AI, also known as artificial general intelligence, does not exist yet.[ix]

Moving down the capabilities spectrum, AI is “weaker” or “narrower” the fewer problems it solves.[x] Thus far, humanity has only developed weak AI systems.[xi] Even AlphaGo, the AI system that famously conquered the complicated game of Go in 2016, is classified as weak AI because it can only play the game—it cannot, for example, understand the written game rules and then create a Go-playing computer program.[xii] Despite the “weakness” of AI systems like AlphaGo, weak AI can perform a wide variety of human tasks.[xiii] Companies use weak AI for applications ranging from price setting to self-driving cars and loan approval to warehouse robot operation.[xiv] Researchers have used weak AI to identify breast cancer and the outbreak of infectious viruses like the “Wuhan Virus.”[xv] Consumers use weak AI whenever they run a search on Google or Bing.[xvi] They speak with weak AI whenever they ask Alexa, Bixby, Cortana, Google Assistant, or Siri a question.[xvii] Lawyers are using weak AI for a wide variety of applications too.[xviii]

Utility of AI for the Legal Industry

Specific to its use in the legal industry, AI can be defined as automating tasks via software to achieve the same outcome “as if a legal practitioner had done the work.”[xix] Legal tasks that AI systems currently perform can be grouped into one of three areas: document analysis, legal research, and practice automation.[xx]

The broad category of document analysis includes contract analysis, document review and e-discovery, and due diligence.[xxi] Companies old and new offer AI-powered document analytics tools.[xxii] For example, JPMorgan has used its proprietary program Contract Intelligence, nicknamed “COIN,” to decrease its annual contract review time by 360,000 hours.[xxiii] Newer companies like Kira Systems, eBrevia, and many others offer time and thus cost-savings benefits based on their use of AI for due diligence and contract analysis.[xxiv] Also, the use of AI for e-discovery has increased dramatically given the efficiencies that it offers.[xxv]

AI-based legal research tools offer a variety of analytical and predictive capabilities.[xxvi] Many legal research companies have created brief-analysis tools that identify relevant cases not included in an uploaded brief.[xxvii] Some offer litigation analytics tools that analyze precedent case data and other data to aid lawyers in predicting case outcomes.[xxviii] A Canadian lawyer reported saving her “clients exorbitant amounts of costs and time” by using an AI-based tool call Alexsei that automatically creates a legal memorandum in response to an input legal research question.[xxix] The Supreme People’s Court in China created an AI-based tool called FaXin to help judges identify precedent.[xxx] A company called Intraspexion leverages deep learning, a type of AI, to predict and warn users of their litigation risks.[xxxi] Predictive analytics company CourtQuant has partnered with two litigation financing companies to help evaluate litigation funding opportunities using AI.[xxxii] Intellectual property lawyers can use AI-based software from companies like TrademarkNow and Anaqua to perform IP research, brand protection, and risk assessment.[xxxiii]

The practice automation category refers to the use of AI to perform tasks ranging from document automation to e-billing management.[xxxiv] Neota Logic’s PerfectNDA tool leverages the company’s AI platform to streamline the process of creating non-disclosure agreements.[xxxv] LegalMation uses AI to automate generation of various litigation-related documents such as pleadings and discovery requests.[xxxvi] WeVorce and Hello Divorce automate divorce-related processes via AI.[xxxvii] Allstate uses AI to automate claim summary generation.[xxxviii] In the UK, Keoghs has created multiple AI-powered systems that automate litigation for personal injury claims.[xxxix] In patent prosecution, Specifio’s AI-based software automatically drafts a first-draft patent application from a user-provided set of claims.[xl] CLOEM S.U.S.A. generates variants of input claims to help patent drafters properly define the scope of their invention.[xli] Finally, in e-billing, companies like Brightflag and CaseGlide leverage AI to automate legal bill review.[xlii]

As shown by those three categories, AI could introduce improved efficiencies and cost savings to legal services delivery.[xliii] The lower-cost services enabled by the use of AI could even increase access to legal services.[xliv] Ultimately, researchers at Deloitte, McKinsey, and elsewhere predict that automation technologies like AI could someday automate significant portions of legal jobs.[xlv] But some associate lawyers have found ways to adapt to the changing nature of legal services delivery.[xlvi] Their roles have “become more strategic, high level and supervisorial and much less ministerial” as they lead legal projects leveraging AI.[xlvii] Attorneys who understand how to improve legal services delivery using emerging technologies like AI are and will be valuable assets for their firms.[xlviii]

While AI has the potential to significantly impact the delivery of legal services, lawyers have been slow to adopt AI-based tools.[xlix] “It seems like the conversation around AI has moved from tasks that replace people to tasks that supplement people,” but the International Legal Technology Association estimates that only about 20% of firms are actually using or testing AI solutions.[l] Lawyers in large law firms appear to recognize that AI could make an important impact on legal services, but they may be only “somewhat interested” in actually using the technology.[li] Furthermore, the American Bar Association has found that over 50% of lawyers reported concerns about AI’s accuracy, reliability, and implementation cost.[lii]

Limitations of AI

The development and use of AI are limited with regard to data, algorithms, and implementation.

Data plays a central role in AI systems as both training material for developing AI algorithms and input material for the actual use of AI.[liii] But the development and use of AI algorithms is limited by a lack of easily accessible and analyzable data.[liv] “Law firms are ‘document rich and data poor’” and public data such as judicial opinions are either not available or so varied in format as to be difficult to use effectively.[lv] Furthermore, poor quality or flawed datasets can cause AI systems to output biased results.[lvi] Datasets may have poor quality or flaws for a variety of reasons.[lvii] For example, the data may exhibit human bias, such as recruiters’ gender discrimination of job candidates.[lviii] Data collection or preparation techniques may result in statistical biases in the dataset such as unrepresentative samples (selection bias).[lix] Datasets may even be intentionally manipulated or corrupted to yield discriminatory analyses.[lx] Beyond data quality issues, significant data privacy and cybersecurity concerns also arise with the use of massive quantities of data by AI systems.[lxi]

Algorithmic limitations also impact the use of AI. As discussed above, all AI systems currently available are “weak” and thus can perform only a specific set of tasks.[lxii] AI works best when there are clear data patterns and definitive answers; it performs poorly when applied to the abstract or open-ended situations requiring judgment, such as the situations that lawyers often operate in.[lxiii] And human expertise and intelligence are still critical to the development and use of AI because AI is not sophisticated enough to “grasp and adapt to nuance, intuit and respond to expectations and layered meaning, and comprehend the practicalities of human experience.”[lxiv] Thus, AI won’t completely replace humans’ jobs yet.[lxv]

Some researchers worry that AI algorithms may be limited in their accuracy, reliability, and impartiality.[lxvi] Those limitations may be the direct result of biased data, but they may also stem from how the algorithms are created.[lxvii] Programmers make many decisions when coding and training an AI algorithm.[lxviii] For example, they choose a set of variables to include in the algorithm.[lxix] Deciding how to use those variables, such as deciding whether to maximize profit margins or maximize loan repayments, can lead to a biased algorithm.[lxx] Programmers may also struggle to understand how an AI algorithm generates its outputs—the algorithm may be unpredictable.[lxxi] Thus, validating “correctness” or accuracy of those outputs when piloting a new AI system may be challenging.[lxxii] But some researchers argue that limited accuracy is sometimes “’good enough’ for particular tasks” or that algorithms can be useful despite their imperfections.[lxxiii] Nonetheless, prudent users should be careful in how they develop or deploy AI algorithms.[lxxiv]

The use of AI is further limited by challenges arising during an organization’s AI implementation process. First, business models based on hours billed, such as those commonly used by law firms, may not incentivize the efficiency improvements that AI systems can provide.[lxxv] Second, those deciding which AI system to use may have little experience evaluating software, don’t know how AI systems would help them or their clients, or don’t have enough reliable data about the AI systems to thoughtfully choose between them.[lxxvi] Third, effective deployment of AI requires a clearly defined use-case and work process, strong technical expertise, extensive personnel and algorithm training, well-executed change management processes, and an “appetite for change and a willingness to work with the tech.”[lxxvii] Potential AI users should recognize that effectively deploying the technology may be harder than they would expect.[lxxviii] Indeed, the greatest challenge may be simply getting potential users to trust the technology, not deploying it.[lxxix]

Critical Questions for Lawyers and the Rise of AI

When considering the utility and limitations of adopting AI for legal service delivery, many questions arise related to AI’s role in the legal marketplace.[lxxx] For lawyers, perhaps the most important questions pertain to legal ethics and lawyers’ roles in the age of AI.

How does AI affect a lawyer’s ethical duties?

Precisely how a lawyer’s ethical duties are and will be impacted by AI is not entirely clear.[lxxxi] Some argue that lawyers will have an ethical duty to use AI to improve the affordability of legal fees and provide the best possible services to clients.[lxxxii] Others point out that there may be legal situations in which AI should not be used, such as to protect clients from biased algorithms, and that “lawyer[s] must know where to draw the line” about the appropriate uses of AI.[lxxxiii] However AI is ultimately used, researchers postulate that AI will affect lawyers’ ethical duties regarding technology competence, confidentiality, supervision, client communication, independent judgment, former clients, the unauthorized practice of law, overcharging, and conflicts of interest.[lxxxiv] AI’s rapid pace of development makes specifying the bounds of those duties an urgent issue.[lxxxv]

How should lawyers engage with and adapt to the rise of AI in legal services delivery?

AI has the potential to dramatically impact the legal profession.[lxxxvi] Some fear that AI will hurt lawyers and other legal professionals as the technology replaces at least portions of their jobs and reduces training opportunities.[lxxxvii] Others predict that AI will free lawyers to evolve their practices to focus on higher-value tasks and new roles or service delivery opportunities.[lxxxviii] Yet others advocate for “more realistic explorations of how lawyers can and should use AI to augment their efficacy and skills.”[lxxxix] Many have provided practical advice for how lawyers might adapt to AI’s use in the legal industry.[xc] And, as discussed above, some associates are already finding ways to engage in using AI for legal work.[xci] Ultimately, how each lawyer engages with and adapts to AI in the legal marketplace is a question that they must answer for themselves.


The intersection of AI and law has been a topic of research and discussion for at least fifty years.[xcii] But the development and deployment of AI is accelerating as a new “age of AI” emerges.[xciii] Perhaps 2020 will be as monumental year as predicted for AI’s continued adoption in the legal marketplace.[xciv] Despite AI’s limitations, and perhaps because of them, lawyers should consider how the rise of AI will impact their ethical duties.[xcv] Legal professionals generally should explore how they might leverage the opportunity now to improve legal-services delivery via new processes and technology and ensure the ethical development of AI.[xcvi] In so doing, they will be better able to navigate coming changes in the legal marketplace.

[i] Alex Crowley is currently a second-year law student at Northwestern University Pritzker School of Law and an incoming 2020 summer associate at Baker McKenzie.

[ii] See e.g., Zach Warren, Legal Tech's Predictions for Artificial Intelligence in 2020, LAW.COM Legaltech news (Jan. 06, 2020),; Doug Hargrove, Technology predictions for 2020 – the impact of AI in the legal sector, ITProPortal (Dec. 25, 2019),; ILTA Publication, 2020 Legal Tech Trend Predictions, ILTA (Jan. 8, 2020),; 25+ Legal Tech and Business of Law Predictions for 2020, Aderant, (last visited Jan. 24, 2020).

[iii] See Warren, supra note 2 (quoting Cat Casey and Scott Forman).

[iv] Harry Surden, Artificial Intelligence and Law: An Overview, 35 Ga. St. U. L. Rev. 1305, 1307 (2019) [hereinafter Surden, Artificial Intelligence] (quoting Artificial Intelligence, Eng. Oxford Living Dictionaries, (last visited Feb. 27, 2019)). See also Edwina L. Rissland, Artificial Intelligence and Law: Stepping Stones to a Model of Legal Reasoning, 99 Yale L.J. 1957, 1958 (Jun. 1990); The Law Library of Cong., Regulation of Artificial Intelligence in Selected Jurisdictions 1, 27 (2019) (providing five definitions for artificial intelligence).

[v] See B.J. Copeland, Artificial intelligence, Encyclopedia Britannica (last visited Feb. 4, 2020),

[vi] Kathleen Walch, Rethinking Weak Vs. Strong AI, Forbes (Oct. 4, 2019),

[vii] See id.

[viii] See Id.

[ix] See id.

[x] See id.

[xi] Id. See also Marco Iansiti & Karim R. Lakhani, Competing in the Age of AI, Harvard Business Review (Jan. 2020),

[xii] See Artificial Intelligence, Stanford Encyclopedia of Philosophy (Jul. 12, 2018), See also Cade Metz, In Two Moves, AlphaGo and Lee Sedol Redefined the Future, Wired (Mar. 16, 2016),

[xiii] See Iansiti, supra note 11.

[xiv] See id.

[xv] Scott M. McKinney, et al., International evaluation of an AI system for breast cancer screening, 577 Nature 89 (Jan. 1, 2020),; Eric Niiler, An AI Epidemiologist Sent the First Warnings of the Wuhan virus, Wired (Jan. 25, 2020),

[xvi] Iansiti, supra note 11.

[xvii] See David Pierce, What Siri, Alexa and Google Assistant Can Do—and Annoyingly Still Can’t Do, The Wall Street Journal (Jun. 2, 2019),

[xviii] See Edgar A. Rayo, AI in Law and Legal Practice – A Comprehensive View of 35 Current Applications, Emerj (last updated Nov. 21, 2019),

[xix] See Sergio D. Becerra, The Rise of Artificial Intelligence in the Legal Field: Where We Are and Where We Are Going, 11 J. Bus. Entrepreneurship & L. 27, 38 (2018).

[xx] See Ronald Yu & Gabriele S. Alì, What's Inside the Black Box? AI Challenges for Lawyers and Researchers, 19 Legal Information Management 2, 2 (Apr. 24, 2019),

[xxi] See id.

[xxii] See Rayo, supra note 18.

[xxiii] Hugh Son, JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours, Bloomberg (Feb. 27, 2017),

[xxiv] See Rayo, supra note 18.

[xxv] See Ben Allgrove & Yoon Chae, Considerations for Attorneys Using Artificial Intelligence, Law360 (Feb. 14, 2018),; Michael Simon, et al., Lola v Skadden and the Automation of the Legal Profession, 20 Yale J.L. & Tech. 234, 278–81 (2018).

[xxvi] Yu, supra note 20, at 2.

[xxvii] Robert J. Ambrogi, Now Comes Another Brief Analyzer, this from Bloomberg Law, LawSites (Jul. 16, 2019),

[xxviii] See Rayo, supra note 18; Researching Firms and Judges: Analytics for Judges and Law Firms, Loyola Marymount University Williams M. Rains Library (last updated Sep. 6, 2019),  

[xxix] Tara Vasdani, AI legal research tools save money, time, and judges’ emnity [sic], Canadian Lawyer (Mar. 25, 2019),

[xxx] Bill Novomisle, Deploying AI in the Legal Department, In-House Community (Mar. 21, 2018),

[xxxi] See Software POC, Intraspexion (last visited Feb. 4, 2020),

[xxxii] See CourtQuant Partners With Second Litigation Finance-Related Business, Sentry Funding, Artificial Lawyer (Sep. 6, 2019),

[xxxiii] Heikki Vesalainen, et al., Artificial Intelligence in Trademark Software, TrademarkNow, at 7 (last visited Jan. 24, 2020),

[xxxiv] Yu, supra note 20, at 2.

[xxxv] PerfectNDA, Neota Logic (last visited Jan. 24, 2020),

[xxxvi] LegalMation (last visited Jan. 24, 2020),

[xxxvii] Diana Shepherd & Aimee Laurence, How Artificial Intelligence Could Impact the Future of Family Law, Family Lawyer Magazine (Sep. 12, 2019),; Jennifer Walter, AI Could Give Millions Online Legal Help. But What Will the Law Allow?, Discover (Jun. 21, 2019),

[xxxviii] How Allstate Leverages Technology Along an ‘Innovation Continuum’, Bloomberg Law (May 17, 2019),

[xxxix] Doug Hargrove, supra note 2; What Lauri does, Lauri (last visited Jan. 29, 2020),

[xl] See Specifio (last visited Jan. 24, 2020),; Allgrove, supra note 25.

[xli] See Features, Cloem (last visited Feb. 4, 2020),; Technology, Cloem (last visited Feb. 4, 2020),; Allgrove, supra note 25.

[xlii] Legal Bill Review, CaseGlide (last visited Jan. 24, 2020),; Product, Brightflag (last visited Jan. 24, 2020),

[xliii] See e.g., Yu, supra note 20, at 2; Vasdani, supra note 29; Allgrove, supra note 25.

[xliv] Dan Mangan, Lawyers could be the next profession to be replaced by computers, CNBC (Feb. 17, 2017), See also Sterling Miller, Artificial Intelligence adoption & ethical considerations for legal providers, Thomson Reuters (last visited Jan. 25, 2020), (“AI opens up the possibility of actually increasing service while spending less money.”).

[xlv] See Lauri Donahue, A Primer on Using Artificial Intelligence in the Legal Profession, JOLTdigest (Jan. 3, 2018),

[xlvi] Nick Ismail, Artificial intelligence in the legal industry; Adoption and strategy – Part 1, Information Age (Aug. 6, 2018), [hereinafter Ismail, Part 1].

[xlvii] Id. (quoting Geoffrey A. Vance). See also Brad Kaufman, How Associate Life Has Evolved Over the Past Decade, Law360 (Jan. 16, 2020),

[xlviii] See Nicholas Gaffney, How Artificial Intelligence Is Changing Law Firms and the Law, Part II, ABA Law Practice Today (May 14, 2019), [hereinafter Gaffney, Part II]; Ayesha Hayat, The future of the law: White & Case on artificial intelligence, Chambers Associate (Nov. 2019), (“junior lawyers will be in demand to ‘train’ the machines”).

[xlix] See ILTA’s 2019 Technology Survey: Executive Summary, ILTA, at 18 (Sep. 2019),

[l] Id. (Note that it is unclear what the ILTA survey respondents defined as AI/ML tools or options. For example, rules-driven expert systems or tools that incorporate but do not focus on AI, such as those used in legal research, may not have been counted.). See also ABA releases 2019 TECHREPORT and Legal Technology Survey Report on legal tech trends, American Bar Association (Oct. 23, 2019),; Catalog of Law Firm Innovations, Legal Services Innovation Index (last visited Jan. 29, 2020), (listing AI systems that large law firms have created).

[li] See Legal Executive Institute, Large Law Firm Technology Survey: Law Firm Leader Perceptions of the Value of Technology, Thomson Reuters, at 13, 15, 21 (Jan. 27, 2020),

[lii] Victoria Hudgins, Uninformed or Underwhelming? Most Lawyers Aren’t Seeing AI’s Value, LAW.COM Legaltech news (Oct. 29, 2019),

[liii] See Yu, supra 20, at 4; Jason Tashea, Want to improve AI for law? Let’s talk about public data and collaboration, ABA Journal (May 22, 2018),

[liv] Id.; Benjamin Alarie, et al., How Artificial Intelligence Will Affect the Practice of Law, at 10 (Nov. 9, 2017), Available at SSRN:

[lv] See Hudgins, supra note 52 (quoting Andrew Baker); Tashea, supra note 53; Alarie, supra note 54, at 10.

[lvi] Yu, supra note 20, at 4. See also Big Data and Big Brother, How General Counsel Cope with Artificial Intelligence in an Era of Economic Nationalism, LexMundi, at 6 (2019), (stating, “AI is only as good as the data it’s fed, so if the information is biased, the AI’s decisions will reflect this as well.”); Will Knight, Forget Killer Robots—Bias Is the Real AI Danger, MIT Technology Review (Oct. 3, 2017),

[lvii] See Yu, supra note 20, at 4; Frederik Zuiderveen Borgesius, Discrimination, artificial intelligence, and algorithmic decision-making, Council of Europe, at 10 (Feb. 7, 2019),

[lviii] See Yu, supra note 20, at 4.

[lix] See id.; Tomi Mester, Statistical Bias Types explained (with examples ) – part 1, data36 (Aug. 21, 2017),

[lx] See Yu, supra note 20, at 4; Borgesius, supra note 57, at 13.

[lxi] See Jeffrey L. Poston, et al., A Tangled Web: How the Internet of Things and AI Expose Companies to Increased Tort, Privacy, and Cybersecurity Litigation, Crowell Moring (Jan. 22, 2020),; Dennis Garcia, Preparing for Artificial Intelligence in the Legal Profession, Lexis Practice Advisor Journal (2017),

[lxii] See Iansiti, supra note 11; Walch, supra note 6.

[lxiii] See Surden, Artificial Intelligence, supra note 4, at 1322, 1330.

[lxiv] See Gaffney, Part II, supra note 48.

[lxv] See id.

[lxvi] Yu, supra note 20, at 3. See also Michael Li, Addressing the Biases Plaguing Algorithms, Harvard Business Review (May 13, 2019),

[lxvii] See Yu, supra note 20, at 3.

[lxviii] Annette Zimmermann, et al., Technology Can’t Fix Algorithmic Injustice, Boston Review (Jan. 9, 2020),

[lxix] See id.

[lxx] See id.; Karen Hao, This is how AI bias really happens—and why it’s so hard to fix, MIT Technology Review (Feb. 4, 2019),

[lxxi] See Yu, supra note 20, at 5; TJ Johnson, et al., We Expect More…AI Promise Meets Reality, Peer to Peer 28, 30 (2019),

[lxxii] See Yu, supra note 20, at 5; Johnson, supra note 71.

[lxxiii] See Sam Corbett-Davies, et al., Even Imperfect Algorithms Can Improve the Criminal Justice System, The New York Times (Dec. 20, 2017),; Harry Surden, Machine Learning and Law, 89 Wash. L. Rev. 87, 99 (2014) [hereinafter Surden, Machine Learning].

[lxxiv] See Yu, supra note 20, at 3; Zimmermann, supra note 68.

[lxxv] See Gaffney, Part II, supra note 48; Nicholas Gaffney, How Artificial Intelligence Is Changing Law Firms and the Law, ABA Law Practice Today (Apr. 12, 2019), [hereinafter Gaffney, Part I].

[lxxvi] See Hudgins, supra note 52; Gaffney, Part I, supra note 75.

[lxxvii] See Hayat, supra note 48; Gaffney, Part I, supra note 75; Steve Stover, AI Success Relies on Strong Organizational Change Management, CMS Wire (Apr. 11, 2019),; Novomisle, supra note 30; Allgrove, supra note 25; Bruce G. Buchanan & Thomas E. Headrick, Some Speculation About Artificial Intelligence and Legal Reasoning, 23 Stan. L. Rev. 40, 45 (Nov. 1970).

[lxxviii] Gaffney, Part I, supra note 75. See also Casey Flaherty, Don’t Eat the Donut, 3 Geeks and A Law Blog (Feb. 2, 2020), (discussing how selecting an AI system and deploying it is not simple or easy).

[lxxix] Lawyers and Robots? Conversations around the future of the legal industry, at 12, LexisNexis (2017),

[lxxx] See e.g., Gary Marchant, Artificial Intelligence and the Future of Legal Practice, 14 The SciTech Lawyer 20, 23 (2017),

[lxxxi] See Drew Simshaw, Ethical Issues in Robo-Lawyering: The Need for Guidance on Developing and Using Artificial Intelligence in the Practice of Law, 70 Hastings L.J. 173, 195–96 (Jan. 2, 2019) (observing that the ABA’s modifications of and comments on several model rules of professional ethics and their relation to developing technology do not sufficiently address AI’s impact on law practice); Jan L. Jacobowitz & Justin Ortiz, Happy Birthday Siri! Dialing in Legal Ethics for Artificial Intelligence, Smartphones, and Real Time Lawyers, Tex. A&M J. Prop. L. 407, 410, 416 (2018) (noting a debate about “whether the use of an AI machine or “bot” constitutes the unauthorized practice of law”); Garcia, supra note 61.

[lxxxii] Simshaw, supra note 81, at 183. See also Neil Sahota, Will A.I. Put Lawyers Out of Business?, Forbes (Feb. 9, 2019),; Alarie, supra note 54, at 13.

[lxxxiii] See Zimmermann, supra note 68; Frank Pasquale, The Second Wave of Algorithmic Accountability, Law and Political Economy (Nov. 25, 2019),; Janine Cerny, et al., Legal Ethics in the Use of Artificial Intelligence, Squire Patton Boggs, at 3–4 (Feb. 2019),; Simshaw, supra note 81, at 210.

[lxxxiv] David Lat, The Ethical Implications of Artificial Intelligence, Above the Law Law2020 (last visited Jan. 28, 2020),; Anita Bernstein, Minding the Gaps in Lawyers’ Rules of Professional Conduct, 72 Okla. L. Rev. 125, 139–142 (2019); Cerny, supra note 83, at 4; Simshaw, supra note 81, at 196–205.

[lxxxv] See Simshaw, supra note 81, at 206.

[lxxxvi] See Simshaw, supra note 81, at 206; Simon, supra note 25, at 289.

[lxxxvii] See Simon, supra note 25, at 285, 289; See also Nick Ismail, Artificial intelligence in the legal industry: AI’s broader role in law – Part 2, Information Age (Aug. 13, 2018), [hereinafter Ismail, Part 2] (quoting Alvin F. Lindsay, “the work traditionally done by young lawyers will be largely supplanted by machines); Erin Winick, Lawyer-Bots Are Shaking Up Jobs, MIT Technology Review (Dec. 12, 2017), (quoting Todd Solomon); Marchant, supra note 80, at 21 (citing “alarming headlines and predictions of artificial intelligence (AI) replacing lawyers”).

[lxxxviii] See Alyson Carrel, Legal Intelligence Through Artificial Intelligence Requires Emotional Intelligence: A New Competency Model for the 21st Century Legal Professional, 35 Ga. St. U.L. Rev. 1153, 1160–61 (2019); Gaffney, Part II, supra note 48; Mark Cohen, Getting Beyond the Tech in Legal Tech, Forbes (May 3, 2019),; Simon, supra note 25, at 289; Artificial Intelligence Won’t Replace Lawyers—It Will Free Them, Law Technology Today (Feb. 27, 2018),

[lxxxix] Simon, supra note 25, at 289.

[xc] See Carrel, supra note 88, at 1160–61; Dennis M. Horn & Ira Meislik, How to Ride the Coming Tidal Wave of Technology and Competition, 32 Probate & Property Magazine (Nov. 2018),; Simon, supra note 25, at 289–90; Garcia, supra note 61.

[xci] See articles cited infra notes 46–48.

[xcii] Surden, Artificial Intelligence, supra note 4, at 1327; Edwina L. Rissland, et al., AI and Law: A fruitful synergy, 150 Artificial Intelligence, 1, 6–13 (Nov. 2003),; Buchanan, supra note 77, at 40.

[xciii] See Iansiti, supra note 11; Benjamin McDermott, Is AI Development Moving Too Fast?; Dogtown Media (Jan. 15, 2020),

[xciv] See articles cited infra note 2.

[xcv] See Simshaw supra note 81, at 210; Garcia, supra note 61.

[xcvi] See Johnson, supra note 71; Simshaw, supra note 81, at 177, 206, 209–10; Simon, supra note 25, at 301; Garcia, supra note 61.

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