Many businesses are now using AI to boost efficiency and gain a competitive edge, incorporating it into basic workflows. Alongside its rapid institutionalization, however, AI governance and regulation must be duly considered.
Policy frameworks are designed to enable technological advancement while keeping public safety and interest in mind. Thus, policymakers need to strike a balance between encouraging innovation and protecting individuals from potential harm. At the moment, there is a distinct lack of consistency in how high-level administrations approach policy frameworks around AI. Inconsistent regulations make it difficult for organizations to ensure compliance.
Legal Implications of AI Usage
The legal implications of AI usage are very complex. For example, an Air Canada chatbot gave a customer inaccurate flight information that led them to purchase a ticket under false pretenses. A lawsuit was filed against the airline, and it was found liable, even though it sought to avoid liability by arguing that the chatbot was solely responsible for its own actions. Assigning liability and preventing harmful AI-driven outcomes are both important considerations moving forward.
Intellectual property is another potential problem area. Generative AI, in particular, is trained by and produces content that’s been scraped from existing datasets. Those datasets may include copyrighted material. Often, the original creators have not consented to their content being used in this manner, creating copyright infringement and fair data use concerns.
Data privacy comes into play, too, especially when AI systems collect and analyze personal information to function. Organizations must adopt proactive risk management strategies, including regular system audits, transparent data practices, and clear governance policies, to mitigate legal issues and build trust with users and stakeholders.
Evolving Regulation
AI is used by individuals buying plane tickets, but also by companies in high-stakes areas such as hiring, lending, healthcare, and criminal justice, where flawed or biased outcomes can have serious real-world consequences. Regulators are likely to introduce stricter requirements around transparency, fairness, and accountability, including mandates for explainable decision-making, regular audits, and clear documentation of how AI systems are used. Increased attention on preventing discrimination and ensuring that AI systems are trained on diverse and representative data is also important.
As these regulations continue to develop and tighten, organizations will have to adapt and comply. Failure to do so could result not only in legal penalties but also in reputational damage and loss of public trust.
Ethical Challenges
Legal and regulatory considerations are important to the development of and protection against AI. So are ethical considerations. IBM published an article describing responsible AI as “the consideration of a broader societal impact of AI systems and the measures required to align these technologies with stakeholder values, legal standards and ethical principles”.
To a similar end, McKinsey laid out 10 principles of responsible AI:
- Accurate & reliable
- Accountable & transparent
- Fair & human-centric
- Safe & ethical
- Secure & resilient
- Interpretable & documented
- Privacy-enhanced & data governed
- Vendor & partner selection
- Ongoing monitoring
- Continuous learning & development
There is only so far AI can, and should, be integrated into the day-to-day life of both business and organizations without strict adherence to these principles. It’s a very exciting time for technological advancement, yes, but it is also a time for caution and responsibility. For professionals navigating this evolving landscape, interdisciplinary knowledge is essential.
As highlighted in Cleveland State University College of Law’s online Master of Legal Studies in Cybersecurity and Data Privacy, understanding AI governance requires more than just legal expertise. The program takes an integrative approach, preparing professionals to engage with the technical and business dimensions of cybersecurity and privacy while also developing a strong foundation in current laws and regulations. This combination of skills is increasingly valuable as organizations seek to manage risk, ensure compliance, and implement responsible AI practices.