The Rising Role of AI in the Legal Sector
Artificial intelligence (AI) has become a significant topic within legal publications, particularly regarding the risks involved and strategies for mitigation. As legal professionals engage with clients, it is crucial for them to understand AI’s operational mechanics and inherent risks when selecting tools for their practice.
A comprehensive grasp of technology underpins an understanding of risk. While detailed textbooks are available, a condensed technical overview can enhance appreciation for the sources of AI risks.
Understanding AI’s Historical Context
Despite its recent prominence, AI technology has long-standing roots. Fundamental mathematical principles originated in the 1950s, and many contemporary techniques emerged during the 1980s and 1990s. Changes in storage capabilities, processing speeds, and data availability over the last 15 years have made these techniques commercially feasible, driving a wave of innovation that shows no signs of slowing.
Diverse AI mathematical techniques have been proposed for various tasks, relying on statistics and probability calculations. The specific mathematics employed (the AI model) receive inputs and produce outputs based on assigned probabilities. However, outputs predicted within the highest ranges may not always represent correctness.
Challenges with AI Accuracy and Reliability
Text generation AIs, for instance, do not access databases for correct answers; they generate sentences that statistically appear plausible based on trained word sequences. Consequently, when such a tool drafts a legal brief, it may suggest citations for fictitious cases, which can be misleading.
This phenomenon, often termed ‘hallucination,’ occurs as the tool generates likely outcomes based on patterns in legal text rather than factual accuracy.
Biases in AI and Their Implications
AI bias is another significant issue. When learning probabilities, AI tools frequently derive relationships between input-output pairs from training data. If particular situations are underrepresented in the training dataset, the AI may not perform reliably for them.
For example, a decade ago, an AI tool trained on resumes demonstrated bias favoring male applicants based on historical hiring data. Such real-world biases may unintentionally become embedded in AI models, compromising outcomes for underrepresented groups.
Best Practices for Utilizing AI
To mitigate risks associated with AI, it is essential for users to ensure clarity and precision in their inputs. Ambiguity can undermine the underlying mathematical operations, thereby affecting results. The optimal approach is to provide simple instructions that can be easily understood.
Moreover, understanding an AI model’s training methods and ensuring human oversight of AI-generated outputs remain critical factors in maintaining accuracy and reliability. The necessity of human verification is particularly vital in high-stakes scenarios.
AI is becoming an invaluable tool in legal and various other fields, but it should be regarded as an assistant rather than a replacement for human expertise.
Andrew “A.J.” Tibbetts, an intellectual property and technology shareholder at Greenberg Traurig, specializes in software-implemented technology across several industries, including AI.
- 0 Comments
- Bias in AI
- Legal Practice