The fact is that generative AI is here, and there is a lot of interest in figuring out where, how, and when to use it in business. We strongly believe in the applicability of a pathway to ai governance. However, we do not embrace the hype, but rather advocate that its use be made with clear purposes, well-defined objectives, and realistic ROI expectations. We advocate a more cautious approach. Making grandiose claims about LLMs may help sell software, courses, or books in the short term, but in the long term, the thoughtless application of these models throughout the organization can lead to significant productivity losses.

Developing ethical principles and guidelines:

a pathway to ai governance, like all technology, presents both risks and opportunities. Organizations should take a cautious approach to adopting LLMs. Executives should consider where this technology really helps and resist the urge to integrate it into every job and task across the organization. To do so, they need to understand two core issues with LLMs that are critical to their medium- and long-term implications: 1) their persistent ability to produce convincing falsehoods (an LLM is optimized for fluency, not accuracy) and 2) the likely long-term negative effects of LLM use on employees and internal processes. When combined, these issues can create organizational conditions ripe for systemic and hard-to-identify failures that can degrade organizational effectiveness if generative AI use cases are not in a managed context and continuously monitored.

Generative AI can generate software code, text, audio, high-fidelity images and interactive videos. It can help identify new materials and propose molecular models that can serve as the basis for finding drugs for diseases for which there were previously no suitable drugs.

The perception we see among executives is that, on the one hand, they are eager to explore technology, but on the other, they are afraid that risks could get out of control. The question is how to identify and balance risks versus opportunities?

The risks associated with AI generation range from inaccurate results and biases embedded in training data, to the potential for large-scale misinformation and malicious influence on politics and personal well-being. There is also a significant concern about creating legal issues through copyright infringement and inappropriate exposure of private company and customer data, inadvertently creating conditions that allow for fraud, and violating business-critical regulatory, compliance and auditing requirements

.Implementing regulatory frameworks:

These issues can hinder the implementation of generative AI, leading companies to halt experimentation until the risks are better understood. Many companies end up deprioritizing the adoption of the technology due to these concerns.

However, by adapting proven corporate governance and risk management approaches to the use of generative AI, it is possible to move forward responsibly to capture the value of the technology. This will also enable companies to use the technology as the regulatory environment continues to evolve at a rapid pace. Building a governance and risk management policy that is tailored to each company will help protect against these threats. In practical terms, we can think of the following activities:

1. Comprehensively understand the risk of exposures related to the use of generative AI, developing a comprehensive view of the materiality of risks related to the use of the technology across all use cases. Create a range of options (including technical and non-technical measures) to manage these risks.

2. Establish a governance structure that balances expertise and oversight with the ability to support rapid decision-making, adapting existing structures to the use of new technology. Generative AI projects must pass this test.

3. Embed the AI ​​governance framework into an operating model that draws on expertise from across the organization.

The main sources of entry risk arising from the adoption of generative AI are:

1. Security threats resulting from the increased volume and sophistication of GenAI-enabled malware attacks,

2. Third-party risks, such as business partners and customers themselves, resulting from challenges in understanding where and how third parties are implementing generative AI, creating potential exposures,

3. Risks of malicious use, resulting from the possibility of bad actors creating convincing deepfakes that represent company employees or brands that result in significant damage to the business reputation, and

4. Risks of intellectual property (IP) infringement resulting from the inadvertent insertion of IP (such as images, music and text) into training data sets and made accessible to anyone using the technology.

For example

, on the security front, a recent survey found that an overwhelming majority of IT leaders (97%) say protecting AI and systems is essential, but only 61% are confident they will get the budget they need. While the majority of IT leaders surveyed (77%) say they have experienced some form of AI-related breach (not specifically to models), only 30% have deployed effective defenses against attacks on their systems that embed generative AI. And only 14% are planning and testing defenses to counter such attacks.

The first practical action the company should take is to assemble a multidisciplinary team that identifies potential risk exposures, anchored in the organization’s risk profile. This team should also analyze and understand the maturity and readiness of the control environment and the technical and non-technical capabilities that the organization has to prevent, detect and, ultimately, respond to the identified risks. This should include cyber and fraud defenses, third-party due diligence to identify where these third parties may be adopting generative AI in ways that embody high risks, and the ability to limit the extraction of the company’s intellectual property by mechanisms used to train large language models.

The outcome of this analysis should be a clear and unambiguous understanding of where the organization faces the greatest potential risk exposures, as well as the maturity and readiness of its current protection system. After completing this exercise, the organization should have a clear roadmap for where and how to strengthen defenses and what the potential ROI of these efforts would be in mitigating these potential risks.

1. Fostering multi-stakeholder collaboration:

   • Encouraging ongoing dialogue and cooperation between policymakers, industry, academia, civil society, and other relevant parties.
   • Promoting the exchange of knowledge, best practices, and innovative solutions to AI governance challenges.

Given the evolving nature of generative AI and its applications, organizations will need to continually repeat this effort to identify their exposure. For most organizations, it will be important to update this exercise at least semi-annually until the pace of technology evolution moderates and control environments and defenses have matured.

Each use case presents a different risk profile, reflecting both the nature of the technology itself and the specific context of the company in relation to the specifics of the application. For example, a chatbot for internal use has lower risks than a chatbot open to customers. But even internal applications can incur risks. Consider a scenario where a company uses LLMs to write an employee policy manual. While task leaders should carefully review the entire manual, after reading a few pages of coherent and authoritative-sounding text, they will likely skim the rest. If an error is introduced into the manual, it could take years to surface. Imagine if an automatically generated employee handbook omits important details about sexual harassment practices and penalties. This type of risk cannot be adequately quantified at the task level. A holistic, organizational, and longitudinal assessment is required.

1. Investing in AI governance research:

   • Supporting interdisciplinary research to deepen our understanding of the societal impacts of AI.
   • Exploring novel governance approaches, such as a pathway to ai governance auditing, algorithmic impact assessments, and AI-focused regulatory sandboxes.

The starting point for organizations implementing a pathway to ai governance is to map the potential risks associated with each use case across different risk categories to assess the potential severity of the risk. For example, use cases that support customer journeys, such as chatbots for customer service, may raise risks such as bias and unfair treatment between groups (e.g., based on gender and race), privacy concerns for users entering sensitive information, and risks of inaccuracy due to model hallucinations or outdated information. When performing this analysis, it is important to develop a metric to calibrate expectations about what constitutes high risk versus medium or low risk.

So, for example, an application that supports a personalized financial advice advisor for a bank’s customers tends to have a higher privacy risk rating than an application that automates basic contract templates.

It is essential that the use case owner leads the initial assessment of the risks associated with the use case (as part of their role as product manager), based on the processes and metrics defined by AI governance. This promotes awareness of potential risks and accountability for their management once the use case is approved for development. In addition, the cross-functional group should review and validate the risk assessments for all proposed use cases in the organization.

Many risk mitigations

Many risk mitigations are technical in nature and can be implemented and reviewed throughout the project lifecycle. However, there are categories of non-technical mitigations that organizations should consider when developing use cases. For example, because generative AI technology is immature, it is necessary to have a human in the loop monitoring the technology, especially when interacting directly with end customers. This will help to address the challenges of “explainability” and increase overall confidence in the results.

The use of generative AI will place new demands on most organizations to adapt governance structures to meet the requirements for approvals and oversight. The CEO and senior leadership are ultimately responsible for ensuring that their organization implements strong governance throughout the entire lifecycle of an AI-enabled application. The CEO and senior leadership are responsible for setting the overall tone and culture of the organization. Prioritizing responsiblea pathway to ai governance sends a clear message to all employees that everyone should use AI responsibly and ethically.

The essential factors for good AI governance are:

1. A cross-functional team responsible for reviewing generative AI projects for risk and adherence to governance practices. This group should include C-level business and technology executives, as well as members from data governance, privacy, legal, and compliance. It should have a mandate to make critical decisions about managing the risks of proposed use cases and review strategic decisions such as the selection of foundation models and their adherence to the organization’s risk posture. Ideally, this group should have a single individual empowered to handle coordination and agenda-setting.

2. Responsible AI guidelines and policies. Organizations should develop a set of guiding principles agreed upon by the executive team and board that will guide AI adoption and serve as a guardrail for acceptable use cases.

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1. Strengthening public awareness and engagement:

   • Implement public education campaigns to improve understanding of AI capabilities, limitations, and potential risks.
   • Engage with diverse communities to ensure AI governance reflects the needs and concerns of all stakeholders, including underrepresented and marginalized groups.
   • Promote transparency in the development and use of AI systems, enabling public scrutiny and accountability.
   • Empower citizens to participate in thea pathway to ai governance process, such as through public consultations, citizen assemblies, or advocacy initiatives

   • Conclusion:

   • The development of a comprehensive AI governance framework is a complex and ongoing challenge, but one that is essential to ensuring the responsible advancement of this transformative technology. By establishing ethical principles, implementing regulatory structures, fostering multi-stakeholder collaboration, building capacity, promoting international cooperation, and adopting an adaptive approach, policymakers and stakeholders can work together to chart a pathway that harnesses the benefits of AI while mitigating its potential risks. Continued efforts in these areas will be crucial in shaping a future where AI is deployed in a manner that aligns with societal values and promotes the greater good.
     

     FAQs:

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     1. Why is AI governance important?
        • AI governance is important because as AI systems become more advanced and ubiquitous, there is a growing need to ensure their safe, ethical, and accountable deployment. Effective governance frameworks can help mitigate risks, promote societal benefits, and ensure AI aligns with human values.
     2. What are the key elements of a pathway to AI governance?
        • The key elements include establishing ethical principles and guidelines, developing regulatory frameworks and oversight mechanisms, fostering multi-stakeholder collaboration, building capacity and education, promoting international cooperation, and adopting an adaptive and iterative approach to governance.
     3. How can different stakeholders contribute to AI governance?
        • Policymakers can enact legislation and regulations, technologists and AI developers can implement ethical design principles, ethicists and civil society can provide input on societal values, and international bodies can work towards harmonized global standards.
     4. How can we ensure AI governance remains adaptable and effective?
        • AI governance frameworks need to be designed with flexibility in mind, allowing for continuous monitoring, evaluation, and refinement as the technology evolves. Regular stakeholder engagement and the incorporation of emerging research and best practices will be crucial.
     5. What are the main challenges in developing a comprehensive AI governance system?
        • Key challenges include balancing innovation and risk mitigation, addressing cross-border issues, aligning diverse stakeholder interests, building the necessary technical and regulatory expertise, and keeping pace with the rapid advancement of AI technology.
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