Are Responsible Artificial Intelligence (RAI) Programs Ready for Generative AI?

Are Responsible Artificial Intelligence (RAI) Programs Ready for Generative AI?

Are Responsible Artificial Intelligence (RAI) Programs Ready for Generative AI?

The majority of Responsible AI (RAI) programs lack the readiness to effectively tackle the risks associated with the utilization of novel generative AI tools. It is evident that RAI programs face challenges in addressing the potential adverse outcomes arising from the use of such tools. An approach that prioritizes the integration of core RAI principles within an organization’s foundation, alongside an adaptable RAI program capable of addressing emerging risks, can provide assistance. This article presents recommendations to help organizations initiate the process of addressing the risks posed by the sudden and rapid adoption of potent generative AI tools.

In practice, Responsible AI (RAI) programs are encountering challenges when it comes to effectively mitigating the risks associated with generative AI, and these challenges can be attributed to at least three factors. Firstly, generative AI tools exhibit qualitative differences compared to other AI tools. Recent advancements have introduced general-purpose AI tools that may not have been previously considered within the context of an organization’s broader responsible AI initiative. Due to the versatile nature of this technology, it is unsuitable for an RAI program to assess the AI technology or algorithm without considering a specific use case. The ethical and social implications of generative AI are contingent upon the particular application it is being used for.

The majority of Responsible AI (RAI) programs primarily tackle the risks associated with traditional AI systems, which revolve around tasks such as pattern detection, decision-making, advanced analytics, data classification, and fraud detection. In contrast, generative AI employs machine learning to process extensive visual or textual datasets, often obtained from unfamiliar sources on the internet. Its purpose is to determine the likelihood of certain elements appearing in proximity to others. Given the unique characteristics of the datasets utilized in generative AI tools and the potential biases inherent in data from unknown sources, most RAI programs are ill-equipped to effectively address the risks posed by these novel generative AI tools.

Moreover, as a consequence of its distinct characteristics, generative AI introduces novel risks that need to be considered. The risks associated with generative AI tools may diverge from those linked to other types of AI tools, such as prebuilt machine learning models or data analysis tools. RAI programs that have not specifically addressed these risks may lack the necessary preparedness to effectively mitigate them. Generative AI presents unique risks with potentially higher implications in the global business landscape. What sets this technology apart is its shift from data classification and prediction to content creation, often utilizing vast amounts of data to train foundational models. Addressing these challenges is not inherently within the purview of conventional RAI programs.

Emerging generative AI tools, including but not limited to ChatGPT, Bing Chat, Bard, and GPT-4, are revolutionizing the creation of original images, sounds, and text through machine learning algorithms trained on extensive datasets. Given that existing Responsible AI (RAI) frameworks were not designed to handle the unprecedented multitude of risks introduced by generative AI tools into society, it is incumbent upon the companies developing these tools to assume responsibility. This entails adopting new AI ethics principles that align with the unique challenges presented by generative AI technology.

Furthermore, the rapid progress in generative AI surpasses the maturation of Responsible AI (RAI) programs. While RAI programs that mandate the implementation of AI risk management frameworks can assist companies in process-oriented risk management related to new generative AI tools, the majority of enterprises have yet to adapt their third-party risk management programs to encompass the AI landscape. Consequently, they do not subject AI vendors or their products to comprehensive risk assessments. As a result, enterprises often remain unaware of the risks they are exposed to when procuring third-party AI applications. With the escalating pace of unregulated development, existing RAI programs may lack the necessary preparedness in terms of specialized tools required to comprehend and mitigate potential harm arising from these new tools.

In parallel with the rapid advancement of AI, Responsible AI (RAI) programs must undergo continual evolution to effectively address emerging risks that may be unforeseen. The evolution of generative tools is occurring at an accelerated pace, and the complete extent of their current and future capabilities remains largely unknown. Consequently, organizations must continuously adapt their programs to comprehensively assess and monitor the risks associated with generative AI tools. It is imperative to ensure that RAI programs align with these assessments and effectively mitigate the identified risks. As new AI technologies continue to emerge and develop, it is vital for RAI programs to remain updated and proactively manage risks, promoting the responsible and ethical use of AI.

Recent developments have witnessed an accelerated pace of technological advancements, resulting in the release of powerful tools with minimal prior public discourse surrounding the associated risks, societal implications, and emerging ethical challenges. The need to navigate these uncharted territories as we progress is evident. Consequently, it is my belief that the majority of responsible AI programs are ill-equipped to effectively address these evolving circumstances.

Fundamental principles of Responsible AI (RAI) establish a solid groundwork for tackling advancements in generative AI and other related technologies. Key principles like trust, transparency, and governance are equally applicable to all AI tools, regardless of whether they are generative or not. The core rules and principles remain consistent: comprehending the potential impact or harm caused by the systems being deployed, identifying suitable mitigation strategies such as adhering to established standards, and establishing robust governance and oversight mechanisms to consistently monitor these systems throughout their entire life cycles.

The existing principles of responsible AI are applicable to recently developed generative AI tools as well. For instance, the importance of striving for transparency and explainability in AI systems remains unchanged. The foundational concepts of responsible AI, including trust, privacy, safe deployment, and transparency, can effectively address and mitigate certain risks associated with new generative AI tools.

Organizations that have well-established Responsible AI (RAI) practices already possess the foundations required to address the challenges posed by generative AI. If your responsible AI program is robust, you will have a certain level of preparedness. The same ethical principles remain relevant, even though they may need to be supplemented with more specific guidance to account for the unique aspects of generative AI.

The majority of Responsible AI (RAI) programs have established the foundational elements that address a wide range of AI risks, including those associated with generative AI. Any perception of “unpreparedness” stems from the extent to which these RAI programs are effectively implemented, operationalized, and enforced in a comprehensive manner.

If an RAI program is incapable of adapting to evolving technologies, it signifies an inherent lack of suitability from its inception. The ethical and legal foundations that form the basis of responsible AI should strive to be future-proof, ensuring their ability to effectively accommodate the dynamic landscape of changing technologies and use cases to the greatest extent possible.

Recommendations for Organizations:


  1. Allocate resources to enhance education and promote awareness. Alongside establishing a robust foundation in Responsible AI (RAI), organizations should prioritize investments in education and awareness initiatives specifically tailored to the distinctive characteristics of generative AI use cases and associated risks. This can be achieved through comprehensive employee training programs. Given the broad applicability of generative AI tools and their potential for diverse use cases, instilling a deep understanding and fostering awareness within the organizational culture is crucial.
  2. Strengthen the foundations of your Responsible AI (RAI) initiatives and commit to continuous evolution. The fundamental concepts, principles, and guidelines of RAI are equally relevant to all AI tools, including generative AI. Thus, the most effective approach to addressing the risks associated with generative AI begins with ensuring that your RAI program, policies, and practices are firmly established. Achieve this by enhancing the maturity of your RAI program to encompass a comprehensive and substantive scope, and by ensuring its application throughout the entire organization, rather than on a piecemeal basis. Additionally, considering the rapid pace of AI’s technological advancements, commit to RAI as an ongoing endeavor and acknowledge that the work is never truly finished.
  3. Maintain oversight of your vendors. Within the ever-growing and intricate AI ecosystem, third-party tools and vendor solutions, including those utilizing generative AI, pose significant risks to organizations. To mitigate these risks, it is vital to integrate robust vendor management practices into the framework and execution of your Responsible AI (RAI) program. This includes leveraging existing technical and legal benchmarks or standards. Consider conducting preliminary risk assessments, evaluating bias measurements, and implementing ongoing risk management practices. Remember, it is unwise to assume that outsourcing liability absolves you of responsibility in the event of any issues or failures.
  4. Allocate resources to enhance education and raise awareness. Alongside establishing a robust foundation for Responsible AI (RAI) programs, organizations should prioritize investments in educational initiatives and awareness-building efforts that specifically address the distinct characteristics of generative AI use cases and associated risks. This can be accomplished through comprehensive employee training programs. Given that generative AI tools have versatile applications across various domains, it is crucial to cultivate a deep understanding and instill awareness of these technologies within the organizational culture.