Since the emergence of Chat GPT, the legal industry's response to artificial intelligence (AI) language models has been mixed, ranging from excitement about the potential efficiency gains to concerns about accuracy, privacy and security, and ethical implications.
These were some discussion topics during a webinar presented by Hanzo and ACEDS. Renowned panelists, comprising of data scientists, legal technology, and ediscovery experts, delved deep into the genesis of AI machine learning(ML) and large language models (LLMs). The panelists examined the potential risks and concerns associated with these cutting-edge tools while shedding light on legal teams' ethical and safe utilization of AI-powered solutions.
This blog post will discuss the insights we gathered from the legal industry through polls conducted during our webinar. Below is a summary of our findings and an infographic displaying the poll results.
Legal Perceptions Of Risk Using AI
Privacy and data protection earned the top spot, with 43.5% of poll respondents indicating it as the biggest risk. These concerns stem from legal information's sensitive and confidential nature and the potential risks associated with AI technologies. One of the primary concerns revolves around the issue of data security, as AI systems rely on extensive access to data to operate efficiently. Another concern is the potential for data breaches resulting from cyberattacks. Such breaches could lead to unauthorized access to confidential legal documents and sensitive client information, resulting in legal liabilities and damage to reputation.
Accuracy came in second, with 42% of poll respondents worried about the risk of inaccuracy. Many legal professionals are deeply unsettled by the potential for inaccuracies in AI-generated content, particularly in complex legal matters. The concern lies in the risk of AI misinterpreting information or providing misguided advice, which poses significant repercussions. A particular worry is the concept of "hallucinations," where the AI generates content not based on real data or prompts but rather on patterns and information it has absorbed during its training. This can lead to irrelevant or incorrect outputs, raising serious concerns within the legal industry.
Bias ranked third most concerning at 11.6%. Some legal experts have raised concerns regarding potential biases in AI systems trained on datasets that include racial, ethnic, gender, socioeconomic, or other biases. They worry that such bias may result in discriminatory or unfair outcomes produced by these systems. Furthermore, there is a risk that AI systems may not have access to current information or recent legal developments, which could lead to incomplete or outdated results. These concerns emphasize the need to tackle bias and guarantee that AI language models can access comprehensive and impartial legal information.
AI Adoption By Legal
Over 54% use AI in their legal operations as opposed to the 27% who say they do not and the 18% that don't know. Legal professionals primarily use AI in their legal operations to enhance efficiency, accuracy, and effectiveness in their work. Some common use cases include:
- Text retrieval - searching large bodies of text with natural language (as opposed to standard keywords)
- Sentiment analysis - understanding the origin, meaning, and metadata associated with files (wherever they are stored)
- Text extraction and summarization - creating summaries of relevant or important files
- Document review and analysis - automatic classification and ranking of text for relevance or key issues
Legal Perceptions Of Opportunities Using AI
The poll results revealed that most participants, totaling over 62%, identified speed to insights during data analysis as the most significant advantage. This finding highlights the importance of quickly obtaining valuable insights from large datasets, enabling legal professionals to make informed decisions and enhance work efficiency. In second place, with almost 29% of respondents, were those who recognized the potential of AI as a valuable tool for replacing initial review processes and conducting relevance searches more effectively.