How to Ensure Your Legal Team’s Data Is AI-Ready

Generative AI offers legal teams new ways of working to achieve more with fewer resources. But the effectiveness of artificial intelligence tools relies on structured, high-quality data to train machine learning models and run AI applications. To ensure your data is AI-ready, it needs to be suitably sourced, properly trained, and procured, as UnitedLex explores in a recent article.

AI-ready data is essential for successful automation and algorithm training. Without it, even the most promising AI applications can fail, leading to disastrous outcomes, as evidenced by high-profile cases of data misuse and resultant fines. The Federal Trade Commission (FTC) has been proactive in penalizing companies for the illegal use of data and even ordering the destruction of algorithms developed with ill-gotten data.

The quality of the data used to train an AI model is critical to the model’s accuracy. It is important to confirm that the data is accurate, complete, unbiased, and consistent. It’s crucial to understand the data provenance — including its origin, acquisition methodology, ownership, and permissions for use. It helps ensure the validity of the data and how effective it will be for training. Proper data sourcing and updated disclosures are necessary to avoid the risks associated with poor quality or biased training data, which can lead to inaccurate and biased AI outputs.

Legal professionals play a vital role in guiding AI development, ensuring that algorithms are thoroughly tested and refined through a continuous feedback loop. This human expertise, combined with rigorous quality control, optimizes AI models and enhances their performance over time.

The adage “garbage in, garbage out” holds true for generative AI models since they learn based on their input, writes UnitedLex. Data must not only be suitable and adequate to train algorithms, but also properly sourced and procured. Only then can you ensure your data is AI-ready.