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Understanding the Impact of Generative AI in legal Practices

Your law firm and its employees are likely employing generative artificial intelligence (GenAI) in various forms, perhaps without even realizing it. The use of AI in the legal sector is not a new phenomenon; it has been gradually integrated into daily operations for some time now. In fact, a recent industry survey indicates that a striking 79% of legal professionals utilized AI by 2025, significantly surpassing adoption rates in many other fields.

The Dual Nature of AI Implementation

The applications of generative AI can range from highly strategic and beneficial to potential pitfalls. For instance, lawyers can use AI to automate mundane but time-consuming tasks, such as document review, contract drafting, billing, and scheduling. This leads to increased efficiency and the prospect of higher profitability. On the flip side, there are numerous cautionary tales, as many attorneys have faced serious repercussions for depending on generative AI outcomes without sufficient verification.

The Root of the Issue

The crux of the problem lies not in the technology itself but rather in its usage. Many legal professionals often turn to off-the-shelf AI models that are trained on general data instead of legal-specific datasets or proprietary firm information. Consequently, these models may inadvertently perpetuate historical biases since they are based on established case law and precedents that might reflect antiquated or discriminatory norms.

In essence, integrating AI into your practice is not as straightforward as merely subscribing to a chatbot service. Leading legal firms and innovators will prioritize training their bespoke models using proprietary data, ensuring that outputs are accurate and resonate with contemporary legal standards while aligning with the firm’s values.

The Limitations of Precedent in AI Performance

Law has a foundational principle known as stare decisis, guiding future cases based on previous rulings. While this is beneficial, feeding raw, unfiltered case law into a language model (LLM) might result in it mimicking the biases of judges from bygone eras, rife with gender exclusion, racial prejudices, and socioeconomic inequities.

If you prompt an AI to generate an illustration of a lawyer presenting before a judge, the outcome will likely default to depicting a young white male attorney arguing before an older white male judge, reinforcing outdated stereotypes.

An extensive study exploring AI’s roles in the courtroom revealed that AI systems used for legal decision-making can manifest racial and gender disparities, reflecting biases existing in the training data. For instance, research from Tulane analyzed over 50,000 convictions in Virginia where judges employed AI risk-assessment tools. Despite AI shortening jail time for low-risk offenders overall, it was found that racial bias persisted—black defendants received less favorable treatment relative to white defendants, even when their risk scores were identical.

Training an LLM is akin to nurturing a child. Just like children learn behaviors, biases are not intrinsic but learned from their environment. If an LLM absorbs bias from historical records, it will reproduce such biases in its responses. Conversely, if trained on the values of a progressive law firm, it can mirror those ideals.

This presents unique legal and ethical dilemmas that have the potential to undermine client outcomes and disrupt the firm’s culture. At McCready Law, we firmly uphold diversity and progressive values, and we reject the idea of letting an unfamiliar algorithm dictate our approach to advocacy. By training our own AI models, we can select and curate historical data to actively promote fairness aligned with our firm’s values, rather than inadvertently perpetuating past inequities.

Strategies for Effective Internal Training

Training your own LLM might seem like an obvious choice, yet if it were that easy, almost every law firm would have implemented it by now. Many managing partners feel paralyzed when it comes to customizing their AI integrations. Commonly, they lack awareness of available options, associated costs, and the potential for ongoing maintenance.

To tackle this challenge, we strategically segment our AI operations into two distinct data categories, each maintained under stringent internal controls.

Bucket 1: The Micro Analysis

The first category focuses on training the AI on a single client file. For instance, we can provide the model with extensive documentation, featuring thousands of pages of medical records, depositions, and police reports relevant to a specific case.

We can then query the AI to locate inconsistencies in depositions or summarize medical timelines. Because the AI operates with data solely from that particular case, the risk of inaccuracies decreases significantly. It doesn’t merely make educated guesses; it retraces information our team can evaluate.

Bucket 2: The Macro Analysis

Our firm carries over 25 years of experience and has amassed rich data, including extensive records of settlements and verdicts. By training an LLM on our historical data, we can pose strategic queries such as, “For a case involving a torn meniscus against Insurance Carrier X, how has this specific adjuster reacted to injury claims of this nature in the past five years?”

While a public LLM may provide legal definitions, only a custom LLM can offer insight into your firm’s unique institutional knowledge, enhancing your impact and client outcomes.

It’s crucial to keep these two buckets distinct. Mixing datasets can be risky. In my early enthusiasm, I contemplated consolidating everything into a singular AI model, but after careful reflection, we wisely decided against it.

Effective data governance becomes essential when developing proprietary tools. Centralizing data without appropriate safeguards can expose sensitive information firm-wide.

In simple terms, if an AI has access to confidential HR files and partner emails, it could inadvertently respond to sensitive queries about employee performance or salary details, perhaps exposing confidential information.

The second critical aspect of successful AI integration is data governance. The first priority? Change management. Without a strategic approach, organizations risk investing in systems that may foster confusion or resistance, rather than delivering genuine value. Alarmingly, only 14% of organizations currently have a change management strategy, yet it’s a vital component in realizing a return on investment that benefits your team and overall operations.

The Concluding Thoughts

Unlike traditional enterprise software solutions, GenAI should not be treated as a mere product to be purchased, set up, and forgotten; it’s more akin to integrating a new team member into your firm.

You wouldn’t hire a promising law graduate and expect them to advocate for a case without mentorship and ethical training. Similarly, we must treat our AI systems as integral members of our team, investing in proper guidance to teach them our values, legal principles, and specific methodologies.

GenAI is currently at its least capable stage. To elevate its effectiveness, we need to enhance our capabilities through technology, developing the skills and procedures necessary to ensure it operates safely and effectively for our firm and clients.

This transformative process commences by tailoring a unique product for your firm, ensuring it aligns with your needs and values while delivering optimal outcomes for your clients.

How AI legalese decoder Can Help

In navigating the complexities of generative AI integration, the AI legalese decoder can serve as an invaluable tool. This platform helps demystify legal jargon and facilitates understanding of AI-generated documents, enhancing your firm’s effectiveness. By leveraging technology to clarify language and meaning, your firm can avoid pitfalls associated with misinterpretation, while reinforcing your commitment to ethical practices. The AI legalese decoder supports your mission to integrate technology safely, aligning it with your firm’s core values while promoting fairness and informed decision-making.

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