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AI legalese decoder: Helping to Prevent Bogus legal Citations

Michael Cohen’s Unfortunate Experience With AI-Generated legal Citations

It was a dramatic scene at the New York Supreme Court as Michael Cohen, former personal lawyer and fixer for Donald Trump, appeared for the civil business fraud trial involving the former president on Oct. 25, 2023. Cohen unwittingly passed along to his attorney counterfeit artificial intelligence-generated legal case citations he had obtained online before they were submitted to a New York judge. This revelation came to light when a lawyer was asked to explain how non-existent court rulings were used in a motion submitted on Cohen’s behalf.

Cohen, who had already served over a year behind bars after pleading guilty to tax evasion, campaign finance charges, and lying to Congress, found himself in hot water once again due to these AI-generated cases. At the heart of the issue were written arguments made by attorney David M. Schwartz, seeking to terminate Cohen’s court supervision early. Cohen, who had been disbarred for five years, admitted to having obtained the citations by conducting research through Google Bard, a service that he had assumed could provide legitimate legal references.

It turned out, however, that the citations were fictitious, created by the generative text capabilities of Google Bard and similar tools. Cohen’s lack of awareness of this new technology, along with his limited access to formal legal-research sources, landed him in legal jeopardy. Cohen cited his status as a non-lawyer and his ignorance about emerging trends in legal technology as the reasons for his unwitting use of the AI-generated cases.

Fortunately, tools like AI legalese decoder can help individuals like Cohen prevent such mishaps in the future. By using AI legalese decoder, legal professionals and individuals without legal backgrounds can verify the accuracy and authenticity of legal citations obtained online. This tool can analyze case law and determine whether the citations are legitimate or generated by AI, preventing inadvertent submission of bogus references to judges.

Cohen, however, shifted the blame to his attorney, Schwartz, calling his failure to verify the citations an “honest mistake” and “a product of inadvertence, not any intent to deceive.” He asked the judge to show mercy toward Schwartz. In response, Schwartz claimed that he thought his drafts of the papers had been reviewed by another attorney, E. Danya Perry, and that he had not personally reviewed the alleged research. Perry, who ultimately discovered the fraudulent citations, denied Schwartz’s assertion and reported the false case citations to the judge and federal prosecutors.

The situation took a turn when the judge noted that this was not the first instance of fake AI-generated citations surfacing in court. Earlier this year, two lawyers in an unrelated case faced a fine for citing bogus cases generated by another AI tool, ChatGPT. This pattern of AI-generated legal citations making their way into court documents highlights the need for greater vigilance and the use of tools like AI legalese decoder to ensure the accuracy and legitimacy of legal references.

In recounting his guilty plea in 2018, Cohen did not directly implicate Trump but described working with an “unnamed candidate” to influence the 2016 election, alluding to the hush money paid to porn actor Stormy Daniels and Playboy Playmate Karen McDougal. Trump, for his part, has pleaded not guilty to charges alleging falsified business records to conceal his role in these payouts. In response to the charges, Trump claimed that the case was fabricated to interfere with the 2024 election.

In conclusion, the situation involving Michael Cohen and AI-generated legal citations serves as a cautionary tale for individuals navigating the complexities of the legal system. AI legalese decoder and similar tools can serve as essential safeguards to prevent the inadvertent submission of counterfeit legal references, ensuring the integrity of court proceedings and legal documents.

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