Legal Advice

Promises

The legal profession involves handling a large number of existing laws, regulations, case law, factual details, and other information that can also be handled by NLP analysis.
NLP can be expected to alleviate some common faults in human lawyers, like human error (oversight) or subjective bias. However, in the majority of applications, NLP is thought to provide tools for humans to use rather than fully automate human layer services.

Examples of areas of applications include client data preprocessing and legal research. There are commercial chatbot services that conduct initial client interviews to determine the area and the severity of the case.
In more advanced NLP applications, machine learning can be used to make estimations, predictions, and even provide potential decisions. In some cases, human intervention is intentionally removed from the calculation to remove subjective bias.

Bias can also be imported from the training dataset.
Who is responsible for false predictions? Dataset quality and machine learning algorithms seem to contribute to the error. However, human lawyers should be able to explain clearly to their clients the reliability of such predictions.

In case of a false prediction, consequences can be significant. For example, if the machine learning system predicts that the trial should go to the defendant who then refuses to plead but it turns out to be a false prediction, the defendant will likely have a harsher sentence.
Lawyers must be able to consult clients on the ethical and legal issues related to these technologies as well as know how to implement them in their practice.