Tag: LegalTech

AI in Legal Research and Decision Making

Traditionally, legal research and judicial decisions are performed by legally certified, skilled humans. Artificial intelligence is supporting and enhancing these processes by introducing text analysis tools, case comparison, document review at scale, and accurate case predictions among other things. Where are the technical and ethical boundaries of machine-enhanced judicial decision-making? And, how long until large language models interpret and explain laws and societal norms better than legally certified, skilled humans do? 

tl;dr

This paper examines the evolving role of Artificial Intelligence (AI) technology in the field of law, specifically focusing on legal research and decision-making. AI has emerged as a transformative tool in various industries, and the legal profession is no exception. The paper explores the potential benefits of AI technology in legal research, such as enhanced efficiency and comprehensive results. It also highlights the role of AI in document analysis, predictive analytics, and legal decision-making, emphasizing the need for human oversight. However, the paper also acknowledges the challenges and ethical considerations associated with AI implementation, including transparency, bias, and privacy concerns. By understanding these dynamics, the legal profession can leverage AI technology effectively while ensuring responsible and ethical use.


Make sure to read the full paper titled The Role of AI Technology for Legal Research and Decision Making by Md Shahin Kabir and Mohammad Nazmul Alam at https://www.researchgate.net/publication/372790308_The_Role_of_AI_Technology_for_Legal_Research_and_Decision_Making

I want to limit this post to the most interesting facet of this paper: (1) machine learning as a means to conduct legal research and (2) expert systems to execute judicial decisions.  

The first part refers to the umbrella term machine learning, which in the legal profession comes down to predictive or statistical analysis. In other words, ML is a method to ingest vast amounts of legal and regulatory language, analyze, classify, and label it against a set of signals. For example, think about all laws and court decisions concerning defamation that were ever handed down. Feed the statistical means into your ML system and deploy it against a standard intake of text looking to identify (legally) critical language. Of course, this is an exaggerated example, but perhaps not as far-fetched as it seems. 

The second part refers to the creation of decision support systems, which – as far as we understand the author’s intent here – are designed to be the result of the aforementioned ML engagement that is tailored to the situation and, ideally, executed autonomously. It helps humans to identify potential legal risks. It helps to shorten the time required to overview an entire, complex case. If set and deployed accurately, these decision support systems could become automated ticket systems upholding the rule of law. That is a big if. 

One of the challenges for this legal technology is algorithmic hallucinations or simply put – a rogue response. These appear to take place without warning or noticeable correlation. They are system errors that can magnify identity or cultural biases. This raises ethical questions and liability for machine mistakes. Furthermore, it raises questions of accountability and the longevity of agreed-upon social norms. Will a democratic society allow its norms, judicial review, and decision-making to be delegated to algorithms?  

For some reason, this paper is labeled August 2023 when in fact it was first published in 2018. I only discovered this after I started writing. ROSS Intelligence has been out of business since 2021. Their farewell post “Enough” illustrates another challenging aspect of AI, legal research, and decision-making: access.     

W36Y23 Weekly Review: X Corp. v. California, Maryland v. Instagram/TikTok, and Government Takedown Requests

+++X Corporation Challenges California Law for Transparency in Content Moderation 
+++Maryland School District sues Instagram, TikTok, YouTube and others over Mental Health
+++Appeals Court Limits Government Power to Censor Social Media Content
+++California Lawmakers Wrestle with Social Media Companies over Youth Protection Laws

X Corporation Challenges California Law for Transparency in Content Moderation 

California’s AB 587 law, which demands that social media platforms reveal how they moderate content related to hate speech, racism, extremism, disinformation, harassment, and foreign political interference, is being challenged by X, the company that runs Twitter. X says that the law infringes on its constitutional right to free speech by making it use politically charged terms and express opinions on controversial issues. The lawsuit is part of a larger conflict between California and the tech industry over privacy, consumer protection, and regulation.

Read the full report on techcrunch.
Read the full text of Assembly Bill 587.
Read the case X Corporation v. Robert A. Bonta, Attorney General of California, U.S. District Court, Eastern District of California, No. 2:23-at-00903.

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Maryland School District sues Instagram, TikTok, YouTube and others over Mental Health

A school district in Anne Arundel County, Maryland is taking legal action against major social media companies, such as Meta, Google, Snapchat, YouTube, and TikTok. The school district accuses these companies of causing a mental health crisis among young people by using algorithms that keep them hooked on their platforms. The school district says that these platforms expose young users to harmful content and make them spend too much time on screens. The school district demands that these platforms change their algorithms and practices to safeguard children’s well-being. The school district also wants to recover the money that it has spent on addressing student mental health issues.

Read the full report on WBALTV.
Read the case Board of Education of Anne Arundel County v. Meta Platforms Inc. et alia, U.S. District Court, Maryland, No. 1:23-cv-2327.

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Appeals Court Limits Government Power to Censor Social Media Content

A federal appeals court has narrowed a previous court order that limited the Biden administration’s engagement with social media companies regarding contentious content. The original order, issued by a Louisiana judge on July 4th, prevented various government agencies and officials from communicating with platforms like Facebook and X (formerly Twitter) to encourage the removal of content considered problematic by the government. The appeals court found the initial order too broad and vague, upholding only the part preventing the administration from threatening social media platforms with antitrust action or changes to liability protection for user-generated content. Some agencies were also removed from the order. The Biden administration can seek a Supreme Court review within ten days. 

Read the full report on the associated press.
Read the case Missouri v. Biden, U.S. District Court for the Western District of Louisiana, No. 3:22-CV-1213.

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California Lawmakers Wrestle with Social Media Companies over Youth Protection Laws

A bill to make social media platforms responsible for harmful content died in a California committee. Sen. Nancy Skinner (D-Berkeley) authored SB 680, which targeted content related to eating disorders, self-harm, and drugs. Tech companies, including Meta, Snap, and TikTok, opposed the bill, saying it violated federal law and the First Amendment. Lawmakers said social media platforms could do more to prevent harm. Another bill, AB 1394, which deals with child sexual abuse material, passed to the Senate floor. It would require platforms to let California users report such material, with fines for non-compliance.

Read the full report on losangelestimes. 
Read the full text of Senate Bill 680.
Read the full text of Assembly Bill 1394.

More Headlines

  • Copyright Law: “Sam Smith Beats Copyright Lawsuit Over ‘Dancing With a Stranger’” (by Bloomberg Law)
  • Copyright Law: “Copyright Office Denies Registration to Award-Winning Work Made with Midjourney” (by IP Watchdog)
  • Cryptocurrency: “Who’s Afraid Of (Suing) DeFi Entities?” (by Forbes)
  • Privacy: “Meta Platforms must face medical privacy class action” (by Reuters) 
  • Social Media: “Meta-Backed Diversity Program Accused of Anti-White Hiring Bias” (by Bloomberg) 
  • Personal Injury: “New York man was killed ‘instantly’ by Peloton bike, his family says in lawsuit” (by CNBC)
  • Social Media: “Fired Twitter employee says he’s owed millions in lawsuit” (by SF Examiner)
  • Social Media: “Georgetown County School District joining lawsuit against Meta, TikTok, Big Tech” (by Post and Courier) 
  • Defamation: “Elon Musk to sue ADL for accusing him, X of antisemitism” (by TechCrunch)

In-Depth Reads

  • Surveillance Capitalism: “A Radical Proposal for Protecting Privacy: Halt Industry’s Use of ‘Non-Content’” (via Lawfare)

In Other News (or publications you should read)

This post originated from my publication Codifying Chaos.

Machine Learning from Legal Precedent

When training a machine learning (ML) model with court decisions and judicial opinion, the result of these rulings is the training data needed to optimize the algorithm that determines an outcome. As lawyers, we take the result of these rulings as final. In some cases, however, the law requires change when rulings become antiquated or conflict with a shift in regulations. This cursory report explores the level of detail needed when training an ML model with court decisions and judicial opinions.   

tl;dr

Much of the time, attorneys know that the law is relatively stable and predictable. This makes things easier for all concerned. At the same time, attorneys also know and anticipate that cases will be overturned. What would happen if we trained AI but failed to point out that rulings are at times overruled? That’s the mess that some using machine learning are starting to appreciate.

Make sure to read the full paper titled Overturned Legal Rulings Are Pivotal In Using Machine Learning And The Law by Dr. Lance B. Eliot at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998249

(Source: Mapendo 2022) 

Fail, fail fast, and learn from the failure. That could be an accurate summary of a computational system. In law, judicial principles demand a less frequent change of pace. Under the common law principle of stare decisis, courts are held to a precedent that can either be a vertical or horizontal rule. Vertical stare decisis describes lower courts are bound by higher courts ruling whereas horizontal stare decisis describes an appellate court decision can become a guiding ruling but only for similar or related cases on the same level. In essence, stare decisis is meant to instill respect for prior rulings to ensure legal consistency and predictability. 

In contrast, the judicial process would grind to a halt if prior decisions could never be overturned or judges wouldn’t be able to deviate and interpret a case without the dogma of stare decisis. Needless to say, overturning precedent is the exception rather than the rule. According to a case study of 25,544 rulings of the U.S. Supreme Court of the United States from 1789 to 2020, the court only overturned itself in about 145 instances, or 0.56%. While this number might be considered marginal, it does have a trickle-down effect on future court rulings at lower levels. 

A high-level description of current ML training procedures could include the curation of a dataset comprised of court decisions, analysis, and legal briefs in a particular field that is used to train an algorithm to process the essence of these court decisions against a real-world scenario. On its face, one could argue to exclude overturned, outdated, or dissenting rulings. This becomes increasingly difficult for legal precedent that is no longer fully applicable yet still recognized by some of the judiciary. Exclusion, however, would lead to a patchwork of curated data that would not be robust and capable of reaching legal reasoning of high quality. Without the consideration of an erroneous or overturned decision, a judge or an ML system could not develop a signal around pattern recognition and sufficiently adjudicate cases. On the other hand, mindlessly training an ML model with everything available could lead the algorithm to amplify erroneous aspects while ranking lower current precedents in a controversial case. 

This paper offers a number of insightful takeaways for anyone building an ML legal reasoning model. Most notably there is a need for active curation of legal precedent that includes overturned, historic content. Court decisions and judicial opinions must be analyzed for their intellectual footprint that explains the rationale of the decision. Once this rationale is identified, it must be parsed against possible conflicts and dissent to create a robust and just system.