They effectively “took” this revenue from law firms.16 Law firms still have the upper hand with due diligence. A 2020 survey found that 89.7% of law departments “would appreciate law firms approaching us more frequently with new legal service delivery models.”17 So for those who can figure it out, there is a receptive audience. While it may seem counterintuitive to a law firm to adopt a product that appears to eat into its revenue stream, the firms that figure out how to embrace and use AI most effectively are the ones that will likely come out the furthest ahead.

Split-Up integrated neural nets and rules to determine property divisions in divorce settlements. Also, in the mid 90s, the HELIC-11 project of Katsumi Nitta and Masato Shibasaki, Tokyo Institute of Technology, combined RBR and CBR, and explored defeasible and dialectical reasoning. Increasingly, technological implementations reduce labor hours, which saves lawyers from unnecessary work and saves clients from unnecessary fees. As AI technology becomes more widespread in law, it may become difficult for lawyers to bill their clients a reasonable fee without leveraging artificial intelligence technologies.

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This requires that the creator of the AI collect the legal performance data on various versions of contract language, a process called “labeling.” This labeled data then is used to train the AI about how to generate a good contract. However, the legal performance of a contract is often context-specific, not to mention varying by jurisdiction and an ever-changing body of law. Plus, most contracts are never seen in a courtroom, so their provisions remain untested and private to the parties.

Regardless, using AI in this context may reflect, or even amplify, the inherent bias in the data of the criminal justice system. The data used to train the ML models is based on actual arrests and conviction rates that may be slanted against some populations. Thus it may enshrine past injustices, or worse, falsely cloak them in the vestment of computer-generated objectivity. We at IntelliSoft have vivid experience creating apps from some of these categories.

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Apps like Case Text, for example, can help accelerate the legal research process. Billing is a time-consuming task, but apps like AllocateLegal, BillerAssist, and SmokeBall significantly reduce the time required to complete AI in Law the process manually. Managing the project while evaluating the uploaded documents is crucial for the new technology. Lawyers have to analyze contracts in bulks and give comments and redlines to advise clients.

Conducting due diligence often requires legal professionals to review a large number of documents, such as contracts. As with other document-related challenges, AI can help legal professionals review documents more quickly. An AI-based due diligence solution can pull specific documents required for due diligence, like documents containing a specific clause.

The potential for AI – combining algorithms, predictive models, and data analysis – to support sustainable finance appears ripe. Moreover, progressively more investors consider, in the management of their options, whether, and to what extent, the products and services provided incorporate the adoption of sustainable standards. It is fortunate that profit appears here to push towards the adoption of solutions that help to improve the protection of social and environmental indicators. Thus, investors demand more comprehensive and detailed financial reporting from companies and accountants with an emphasis on those standards, also fulfilling, in a way, a duty of corporate social responsibility.

  • At JPMorgan, an AI-powered program called COIN has been used since June 2017 to interpret commercial loan agreements.
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