
Generative AI has captured public interest at an unprecedented rate, with platforms like ChatGPT reaching one million users in just five days. According to research by McKinsey & Company, the economic potential of gen AI is vast, with estimates suggesting an impact of $2.6 trillion to $4.4 trillion annually. However, this rapid proliferation has also magnified risks related to biases, transparency, intellectual property, privacy, and security. Adding to these risks, organizations are increasingly facing pressure from regulators, globally, who are striving to establish legal frameworks that balance innovation with risk mitigation.
This regulatory pressure is augmented by the fact that (as expected), regulators appear to take vastly disparate approaches to AI regulation!
For example, while the European Union and South Korea have developed comprehensive AI regulations, countries like the United States adopt sector-specific laws, similar to their privacy approach, which is only made more confusing by the fact that states and localities have stepped into the regulatory foray. Also, Brazil and Singapore favor principles-based guidelines, which may evolve into stricter regulations over time. Notwithstanding this confusion, most of these laws tend to focus on similar issues such as transparency, accountability, bias minimization, and compliance.
We believe that a set of principles commonly known as information governance (IG) by design presents a crucial and highly effective framework for managing an organization's compliance, risk, and governance strategies and furthering these general goals.
The essence of IG by design lies in proactively embedding compliance and governance mechanisms into the very fabric of AI development and operational processes from the outset.
These mechanisms and strategies, which are critical to managing the complexities and risks associated with advanced AI technologies, include retention and privacy compliance, the structured removal of redundant, obsolete, and trivial data, version control principles, and the establishment of clear accountability and audit frameworks.
Here are some examples:
Transparency: IG by design promotes transparency and ethical operation by embedding mechanisms that ensure AI systems' decisions can be traced and audited, and by integrating human oversight capabilities that allow for intervention when necessary to correct errors and guide ethical decision-making.
Accountability: IG by design helps organizations to establish and maintain automated accountability structures with clear roles and responsibilities to ensure compliance and facilitate governance.
Biases and Fairness: IG by design helps to promote fairness and bias minimization by embedding rigorous bias detection and mitigation strategies throughout the AI lifecycle such as using diverse and representative training data, implementing fairness-aware algorithms, and conducting regular audits to identify and address biases.
Privacy: IG by design principles help organizations comply with existing regulations and safeguard sensitive information. For example, an organization that proactively builds systems based on these principles can more effectively institute the types of security measures needed to effectively protect against attacks and system failures, enhancing the resilience and reliability of AI systems.
Retention Compliance: Retention compliance is another vital goal for organizations seeking to promote AI compliance. IG by design principles help organizations to ensure that data is managed according to regulatory requirements and organizational policies. IG by design supports retention compliance by embedding mechanisms that automate data lifecycle management, enforce retention schedules, and facilitate secure data disposal.
Vital Records Protection: Vital records programs are also essential, focusing on identifying, protecting, and managing records crucial to the organization's operations. IG by design integrates these programs into the AI development process, ensuring that vital records are consistently maintained and accessible during critical operations.
To effectively navigate the regulatory landscape, organizations should adopt a strategic roadmap for AI compliance. These principles include the continuous monitoring of global regulatory developments, conducting regular risk assessments, fostering cross-functional collaboration among legal, compliance, IT, and operations teams, and deploying strategies such as IG by design to create a strategic roadmap to promote proactive, effective, and adaptable long-term AI compliance.
This approach helps to ensure that AI systems are not only compliant with regulations but also robust, transparent, and accountable, maximizing their potential benefits while minimizing risks.
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