The business world envisions a bright future with the inclusion of the operational contributions of artificial intelligence (AI). There are remarkable benefits expected from medical diagnostics and efficiencies in manufacturing to accelerated computations and vast data culling. Every facet of our productive lives will be impacted.
With the increasing integration in most business practices, the importance of understanding potential legal risks and liabilities has never been higher. There are a lot of unknowns as we proceed with this ‘tool.’ While AI offers significant benefits, it also introduces complex legal, ethical, and operational considerations that organizations must address proactively to mitigate risk and protect the interests of all stakeholders, including patients, customers, and consumers.
Of the variety of issues, the following are noteworthy considerations. Initially, data and the need for its privacy and security is paramount. AI systems often rely on large volumes of data, including sensitive personal information. Organizations should consider whether they are under and in compliance with applicable data protection laws, for example, the General Data Protection Regulation (GDPR) in the European Union, the Health Insurance Portability and Accountability Act (HIPAA) in the US, and other related consumer privacy provisions in states. These laws emphasize consent, data minimization, and individuals’ rights to access and control their data.[1] A companion concern regards implementing robust cybersecurity measures and establishing clear policies for data collection, storage, and sharing.
A concern is the result of inadvertent discrimination and bias from AI algorithms that may perpetuate biases present in training data, leading to discriminatory outcomes. This is particularly concerning when serving vulnerable populations such as patients or marginalized communities.[2] Entities will have to conduct regular audits for fairness,
accuracy, and bias mitigation.
While we are all entering a new data frontier, entities will need to embrace transparency and explainability. Entities’ stakeholders have a right to understand how AI-driven decisions are made, especially in high-stakes sectors like healthcare and finance.[3] As processes become better integrated, maintaining documentation of AI decision-making processes will help entities ensure accountability.
The realm of liability and accountability at this stage of implementation is still not settled. However, clarifying responsibility when AI systems cause harm or errors is critical to an entity’s liability and accountability. Entities should be aware of potential liabilities under product liability laws (e.g., EU Product Liability Directive, US Laws) and establish protocols for addressing adverse outcomes.[4]
Regulatory compliance is a never ending entity task as organizations must stay informed of evolving regulations governing AI use in their industry and jurisdiction. For healthcare, this includes compliance with the FDA’s guidance on AI/ML-based medical devices, while other sectors should monitor relevant national and regional AI regulations.[5]
The ethical use and human oversight of AI should embrace how to implement supporting human judgment rather than phasing out human judgement. Human oversight remains vital as we all are learning how to adapt and maintain productivity, particularly in sensitive areas such as healthcare, legal, or financial decision-making. Regarding service delivery and engagements (contracts and vendor agreements) when an entity engages in the utilization of third-party AI solutions, organizations should review contractual provisions to allocate responsibilities, warranties, and liabilities thoroughly. Vendors should also adhere to relevant standards of compliance and ethics.
Unfortunately, the potential liability for errors does not go away with AI. Over dependence of AI without review of the results and determinations will reveal errors, resulting in legal claims or an entity’s reputational damage. Entities will be expected to employ continuous monitoring and quality control measures to minimize such risks.
With the AI advent of a prosperous future, entities must diligently address the legal and ethical risks associated with their AI implementation in their entity. Proactive risk management, compliance monitoring, transparency, bias mitigation, and accountability—will help safeguard against looming liabilities and to foster trust with clients and consumers in an era of new uncertainties. Engaging legal counsel and technical experts early in the AI integration process to develop comprehensive policies and safeguards tailored to your specific operational context should be kept in mind.
Disclaimer: This memo does not constitute legal advice. For specific legal concerns, consult qualified legal professionals familiar with your jurisdiction and industry.
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[1] GDPR (Regulation (EU) 2016/679), HIPAA (45 CFR Parts 160 and 164).
[2] Equal Credit Opportunity Act (ECOA) (15 U.S.C. §§ 1691-1691f), the Americans with Disabilities Act (ADA) (42 U.S.C. §§ 12101 et seq.)
[3] EU AI Act emphasizes transparency and human oversight for high-risk AI systems; the United States FDA is recommending transparency and validation for AI tools in healthcare.
[4] General tort and product liability principles.
[5] FDA Guidance on AI/ML in Healthcare, forthcoming EU AI Act, and other jurisdiction-specific standards.