In recent years, a number of international organisations, regulators, governments, academics, and as well businesses have worked on developing principles of Artificial Intelligence (AI).
Alongside the development of these principles, there is an on-going discussion on how to regulate AI in order to best align risk management with optimising potential value creation of these technologies. Risk managing AI systems will likely become a regulatory and social expectations requirement, for all sectors and for both business and government.
Basic principles of artificial intelligence
However, emphasis on how to implement the proposed AI principles and upcoming regulations in practice is more recent, and appropriate tools to achieve this still need to be identified and developed. For example, implementing so-called Responsible AI requires the development of new processes, frameworks and tools, among others. INSEAD databases are currently reviewing the current state and identifying possible gaps.
The Growing Need for AI Regulation and Risk Management
As AI technologies continue to evolve, the necessity of regulatory oversight becomes more apparent. Governments and international organizations are actively working on frameworks that balance innovation with accountability. AI risk management is now a central topic in policy discussions, as both businesses and regulators seek to establish guidelines that ensure ethical and responsible AI deployment. The challenge lies in developing regulations that are both effective and adaptable to the rapid pace of AI advancements. Companies across industries are expected to integrate AI risk management into their operations to meet regulatory and societal expectations. Insead AI for business, for example, is a working paper series related to how to integrate these new tools in a smooth and ethical way that ensures success.
Responsible AI: Challenges and Potential Solutions
While AI principles have been widely discussed, translating them into practical implementation remains a significant hurdle. Responsible AI requires more than just ethical guidelines; it demands concrete frameworks, tools, and processes that ensure transparency, fairness, and accountability. Organizations must invest in compliance strategies, AI auditing mechanisms, and risk assessmentIt refers to the process of identifying, analyzing, and evaluating the severity and probability of risks and threats associated with business or product development, deployment, and maintenance. In the context of the DSA, very large online platforms are required to annually assess the systemic risks stemming from the design, functioning or use of the platforms, including any actual or foreseeable... More models to align with upcoming regulations. However, gaps remain in defining standardized approaches, as different industries have unique AI-related risks. Addressing these challenges will require collaboration between policymakers, businesses, and academia to create adaptable and scalable solutions.
Boza, Pal and Evgeniou, Theodoros, Implementing Ai Principles: Frameworks, Processes, and Tools (February 10, 2021). INSEAD Working Paper No. 2021/04/DSC/TOM, Available at SSRN.
By: Pal Boza and Theodoros Evgeniou
