The emergence and rapid development of Artificial Intelligence (AI) and Machine Learning (ML) within legal services is creating extraordinary opportunities for legal professionals. Many law firms and legal entities are adopting AI/ML technologies to assist with tasks like research, document analysis, and case prediction, significantly revolutionising branches of the industry. However, the plurality of legal professionals harbour fears and doubts about incorporating AI/ML tools in their daily workflow.
According to an article originally published in an ILTA publication, one of the less emphasised reasons for this resistance is the fear of human vulnerability that AI/ML advancements introduce. AI and ML technologies transcend human limitations in several areas, offering increased efficiency that could potentially minimise costs, reduce errors, and eliminate the need for extensive revisions.
AI/ML technologies have a transformative potential for traditionally time-intensive tasks within the legal sector, such as legal research and data analysis. They offer improved accuracy, insights, and precedents that can significantly inform legal strategies and outcomes. Furthermore, AI/ML systems can predict future client needs and legal outcomes, deliver customized services, catch potential inconsistencies in contracts, and automate repetitive tasks – thereby enhancing overall service provision and legal performance.
However, with embracing AI/ML technologies, legal entities also need to tackle the various risks and ethical considerations surrounding their use. Firms need to pay attention to cybersecurity and confidentiality and be aware of specific attacks on AI/ML systems such as Model stealing, Model inversion, Backdoored ML, and Membership inference attacks.
Legal entities employing AI/ML systems should reinforce their cybersecurity to protect against threats that may disrupt services or infrastructure by causing downtime, impacting firm operations, leveraging ransomware, or launching denial-of-service attacks. Legal entities must look into data anonymization, data encryption, limiting model outputs, and employing differential privacy to counter such possibilities.
Acts of data poisoning, input manipulation, adversarial attacks, and compromise to the supply chain are feasible threats to AI/ML system integrity that law firms and other legal entities must lookout for and counteract as they embrace these groundbreaking technologies.
When implementing AI governance, legal firms need to define roles and responsibilities, implement data governance practices, create guidelines for developing and validating AI models, consider ethical and compliance requirements, and update risk management processes and training and awareness programs.
Every organisation planning to embrace AI/ML technologies must perform a comprehensive risk assessment to define security requirements for the system, consider data sensitivity, regulatory requirements, and uphold a firm stance against potential threats.
While there are many challenges with embracing AI/ML technologies, if firms navigate the complexities of AI/ML with a nuanced approach that balances innovation and caution, manage it responsibly, exercise some faith, and implement the proper controls and governance, the legal sector should be able to progress cohesively and tap into the endless possibilities and efficiencies offered by these advancements.