Using Trusted Data for AI Will Ensure Successful Innovation

A Lexis Nexis blog post states that legal teams must rely on using trusted data for AI to ensure that artificial intelligence technology initiatives are successful. The key to this success lies in sourcing data from credible and accurate sources. 

Legal departments should prioritize third-party data providers using up-to-date datasets that cite each source, thus ensuring transparent, ethical data practices. According to the LexisNexis Future of Work 2024 report, over 70% of executives believe that using trusted data boosts the reliability of generative AI outputs.

Incorporating poor-quality data can lead to significant problems, such as inaccurate results, reputational damage, and legal challenges. Unreliable data can also distort AI predictions, leading to costly mistakes. Generative AI models powered by faulty data are particularly prone to “hallucinations” or responses not grounded in accurate information. Using retrieval-augmented generation is a solution that ensures AI responses cite credible sources so users can verify that they are not hallucinations.

Using trusted data for AI also helps companies maintain compliance with legal standards and avoid lawsuits related to data misuse. For instance, technology firms have faced legal challenges over unauthorized data scraping and breaches in data protection. By sourcing data from providers who comply with privacy and intellectual property regulations, companies can minimize these risks and foster greater trust.

By selecting high-quality, credible data collected using ethical standards for AI initiatives, legal departments can gain better insights, ensuring compliance and innovation while reducing legal risks.