3. Emphasize data governance as an important part of AI governance
Just as AI relies on data to perform its work, government agencies using AI must implement clear data governance standards, policies, and protocols aimed at employees. Its data governance programme should cover cybersecurity measures and provisions to ensure that citizen data is protected from misuse. This involves establishing clear standards that specify how sensitive data is processed. For example, which AI models are more accessible to? Use systems that treat data like valuable assets and protect against misuse, leakage and cyberattacks.
Create guidelines and requirements for curating your dataset. This includes rules and responsibilities for anonymous data. As part of our governance programme, we communicate and strengthen the practices and protocols that govern AI use in our organization to ensure that employees recognize and follow them. Holding people accountable by making AI compliance a part of their training program and performance management. Employees understand that employees are evaluated for how closely they follow AI governance programs.
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4. Promote AI and data governance to C-level issues
The responsibility lies with the institutions that protect themselves and their components from the risks associated with AI, while maximizing profits. These responsibilities need to begin at the very top of the organization, not left to the organization or security team. Simply put, we deal with AI at a level worthy of AI. Don't speculate about it and don't be a blase about its governance.
5. Establish an AI governance agency
When it comes to mitigating the risks associated with AI, it should stop being human. Specifically, a centralized, representative group of people across the organization is responsible for establishing governance programs and ensuring that it continues while overseeing and monitoring agency AI activities. Part of that job is to ensure the auditability of models and apps with the ability to monitor output to prevent bias, drift, or degradation. Model behavior can change quickly and unexpectedly, and humans need to monitor them consistently in case they do so. Agents need to develop a process in which humans validate the output generated by AI.
AI governing bodies must take the reins to address risks that arise outside the organization through vendors, suppliers or partners. For example, a data leak or cyber attack can arise from third-party cloud service providers. Make sure your security team is creating and enforcing well-defined cybersecurity standards and requirements that apply to entities within the agency's business ecosystem. Choose a software vendor whose AI development standards and policies address security, privacy and ethical concerns. If your vendor is unable or does not meet your security standards, be prepared to take your business elsewhere.
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6. Align the governance guard rails with the regulatory requirements of the policy.
Keep a close eye on new laws, policies and regulations that emerge from state legislatures and regulatory bodies. AI is still in its early days, and so is the high-level policy around it. Hope it changes and evolves over the next few years.
7. Prioritize Whitebox AI deployment
Transparency and accountability help to make your organization's AI tools, the LLMS you rely on, and output easy to explain and understand.
As the Public Sector Network whitepaper points out, “By developing AI capabilities, public institutions can cultivate a culture of innovation and revolutionize the efficiency and flexibility of public services.”
Governance and monitoring are important to exploiting its huge potential.
