Brave New World: How AI Tools Are Used in the Legal Sector

Originally published by Haynes and Boone Benefits Group.

From hiring to contracts, AI’s use
in legal departments is increasing. But that also means planning for new types
of risks.

In our previous article, we explored several legal implications that artificial intelligence will have on patent law, and the availability of patent protection for AI inventions. In this article, we explore the impact of AI in the legal industry, including new AI tools for legal departments, and how to plan for risk when using these AI tools.

AI in the Legal Sector

Machine learning is an application of AI in which AI’s algorithms learn from
past experiences and then apply this knowledge to predict future outcomes.

Because there are many similarities between the law and machine learning,
the law is conducive to AI and its machine learning applications. For example, both
the law and AI machine learning infer rules from historical examples to apply
to new situations. Legal rulings involve applying propositions based on prior
precedent to the facts at issue and deriving an appropriate conclusion. AI
machine learning uses the same process. The law and AI machine learning are
both logic-oriented methodologies (e.g., if X happens, then the result should
be Y).

Natural language processing (NLP) is another application of AI in which
the AI’s algorithms automatically process and interpret words based on the
context in which the words are used. For example, rather than processing a word
in isolation, NLP processes the word based on the other words used in the same phrase
or sentence in which the word appears, and the topic or application in which
the word is used. This is similar to law that requires attorneys to analyze
terms in a contract or identify facts of a case that is similar to a case at
issue.

Common Uses for AI Tools

Machine learning and NLP have enabled a number of AI tools to be
developed to help legal departments reduce costs, develop data-driven
strategies, assess risk, and become more productive. Below, we identify some of
the AI tools that are available to legal departments.

Contract Review and Negotiation: Many legal departments spend a significant amount of time on contract review and negotiation. Contracts often have standard terms. These contract terms are often the focus in contract review and negotiation. AI tools using NLP have been developed for legal departments to perform textual analysis of proposed contract terms based on a legal department’s objectives. These AI tools determine which proposed terms of a contract are acceptable, and which are not. At present, contract review AI tools have not replaced review by attorneys. Instead, the AI tools serve as a check that allows for a more efficient review and identification of potential errors before contracts are finalized.

Contract Performance and Analytics: Once parties have a contract in place, it can often be difficult to monitor contract performance to ensure that agreed-upon terms and obligations are being met. Companies with many contracts between many different parties across different divisions of the company often grapple with this challenge. Like contract review AI tools, NLP-powered AI tools extract and conceptualize key terms from contracts and compare those terms with a company’s data metrics to determine whether contract terms and obligations are being met. These AI tools allow legal departments to harness the ever-increasing collection of data by companies to assess contract performance and compile analytics.

Litigation Prediction and
Analytics: Machine-learning AI tools have also been developed to predict
the outcome of cases based on relevant precedent, facts of the case, and prior
outcomes in particular jurisdictions. Likewise, AI tools predict the likelihood
of success for motions or other pleadings based on data-driven assessments. These
litigation prediction models assist legal departments in making decisions on
litigation strategies. In addition, litigation prediction models are also
supercharging the litigation finance industry, where third party investors fund
a plaintiff’s litigation case in return for a share of the award if the
plaintiff is successful. Litigation prediction AI tools enable investors to
develop assessments of which cases to finance, based on the likelihood of
success from the prediction models.

Legal Research: Like litigation funding, other NLP-based AI tools build research platforms that have more sophisticated understandings of legal opinions. These platforms use NLP to uncover relevant law based on the fact pattern of a case, rather than keyword searching. AI helps legal departments to review past matters to assess risk, potential liability, and evaluate legal fee estimates based on analytics. These AI tools take advantage of a knowledge database containing information of interest to the company using the AI tool.

AI Assistance in Hiring

Another way in which companies have begun to use AI tools is to make the
recruitment and hiring process more efficient. AI tools may be used for simple
tasks, such as scheduling interviews and travel arrangements, or for sending
targeted job listings. But companies are also beginning to reap the benefits of
AI in other stages of the recruitment process, including using AI tools to
screen candidates more efficiently. Not only have companies begun using AI tools
to narrow thousands of resumes down to a reasonable number for further review,
companies have also started using AI tools to conduct initial video interviews.

Despite the advantages of using AI in the recruiting processes, AI may also
result in incidental bias and discrimination. One of the most commonly
expressed concerns about using AI focuses on the underlying data the AI tools
uses to analyze and make predictions about successful candidates. Because the
AI algorithms are often trained using data pertaining to past successful and
unsuccessful candidates, the AI may, for example, begin to favor candidates of
a certain age, race, or gender. Similarly, to the extent the AI tool identifies
characteristics shared by successful individuals (e.g., current employees), the
AI tool may begin to favor candidates who also share those characteristics,
which could include anything from educational background to membership in
certain organizations.

These types of questions have prompted important discussions worldwide
and have resulted in calls for transparency in how such AI tools are used in a
hiring process. For instance, at least one proposed class action has been filed
against T-Mobile US, Inc. alleging age discrimination regarding employment
advertisements on online platforms. In another case, a consumer advocacy group,
the Electronic Privacy Information Center (EPIC), filed a complaint with the
Federal Trade Commission regarding AI hiring tools made by HireVue Inc. As
described in the complaint, the technology conducts pre-employment assessments
of job candidates, including video interviews in which thousands of data points
are collected about a candidate and analyzed to predict the candidate’s
employability. The allegations in the complaint highlight ways in which the
hiring algorithms may result in bias, including the possibility that eye
movement tracking captured in video interviews could discriminate against
individuals with neurological differences.

As AI continues to develop in the coming years, and as its use becomes
more prevalent in the hiring process, companies should keep these
considerations in mind. Likewise, businesses should keep abreast of developing
state and federal laws related to using AI. As an example of state law action,
Illinois recently passed the Artificial Intelligence Video Interview Act, which
establishes requirements for companies using AI to analyze video interviews.
The law requires, for instance, that employers inform applicants before the
interview regarding how the AI works and generally what characteristics it uses
to evaluate candidates. At the federal level, various bills have been
introduced regarding the use of AI, such as the Algorithmic Accountability Act
of 2019, which would require businesses using “automated decision systems” (a
term defined in the bill) to conduct impact assessments that evaluate the
automated systems, including the design and training data for the system, to
analyze its impact on “accuracy, fairness, bias, discrimination, privacy, and
security.”

Conclusion

As AI tools become more commonplace, these sorts of requirements
governing the use and testing of AI tools are likely to become more prevalent
as well. For now, however, businesses may benefit from considering these issues
in advance and taking initiative now to ensure AI tools are used fairly, accurately,
and efficiently.

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This article reflects only the
present personal considerations, opinions, and/or views of the authors, which
should not be attributed to any of the authors’ current or prior law firm(s) or
former or present clients.

Eugene Goryunov is a partner in the
Intellectual Property Practice Group in the Chicago office of Haynes and Boone
and an experienced trial lawyer that represents clients in complex patent
matters involving diverse technologies. He has extensive experience and
regularly serves as first-chair trial counsel in post-grant review trials (IPR,
CBMR, PGR) on behalf of both Petitioners and Patent Owners at the USPTO.

David L. McCombs is a partner in
the Intellectual Property Practice Group in the Dallas and Washington, D.C.
offices of Haynes and Boone and is primary counsel for many leading
corporations in inter partes review (IPR) and is regularly identified
as one of the most active attorneys appearing before the Patent Trial and Appeal
Board (PTAB).

Dina Blikshteyn is of counsel in
the Intellectual Property Practice Group in the New York office of Haynes and
Boone. Dina’s practice focuses on post grant proceedings before the U.S. Patent
and Trademark Office, preparing and prosecuting domestic and international
patent applications, as well as handling trademark and other IP disciplines.

Jonathan Bowser is of counsel in
the Intellectual Property Practice Group in the Washington, D.C. office of
Haynes and Boone. He is a registered patent attorney focusing on patent
litigation disputes before the Patent Trial and Appeal Board (PTAB) and federal
district courts.

Raghav Bajaj is a partner in the
Intellectual Property Practice Group in the Austin office of Haynes and Boone.
His practice focuses on patent office trials before the Patent Trial and Appeal
Board (PTAB), including inter partes review (IPR) and covered business method
(CBM) review proceedings, representing both petitioners and patent owners.

Angela Oliver is an associate in
the Intellectual Property Practice Group in the Washington, D.C. office of
Haynes and Boone. She focuses her practice on patent appeals before the U.S.
Court of Appeals for the Federal Circuit and post-grant proceedings before the
U.S. Patent and Trademark Office.

The post Brave New World: How AI Tools Are Used in the Legal Sector appeared first on Haynes and Boone Blogs.

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