Who is responsible for the secure and responsible usage of AI within a modern organization? In the rapidly evolving landscape of AI and LLMs, this question is more critical than ever. Not only that, but AI’s reach continues to expand beyond tech hubs like software development into customer support, finance, and HR. This becomes a truly consequential question for an organization. In this post, we’ll explore how to build accountability and stakeholder support as an organization’s AI governance leader.
Whose Job Is AI Governance?
AI Governance is a new responsibility that doesn’t yet fit cleanly into a well-defined job. Just like the AI landscape itself, the job of governance itself is a bit messy and evolving on the fly.
There are 3 fundamental requirements for your AI governance leader:
- Technical Credibility: The usage of AI and LLMs within an organization is still primarily a technical implementation. To govern and provide guardrails around this, the responsible person must have the technical credibility to understand the trade-offs around security, privacy, and employee productivity.
- Strategic Mindset: In addition to technical understanding, the responsible person must also be able to help propel the vision and decisions that enable AI across the organization. This vision and strategy isn’t entirely their job, but governance efforts can fall flat if they’re without an overarching aim.
- Cross-Functional Leader: The responsible person must be able to lead this initiative across functional groups within an organization, communicating clearly and effectively while having the credibility to make your governance efforts stick.
When factoring in both requirements, an organization’s AI governance leader tends to be a senior technical employee. This can be the CTO or CIO, or it can fall to a more senior technical leader who reports regularly to the senior leadership team.
AI Accountability
AI accountability means holding individuals and groups responsible for the decisions made, actions taken, and outcomes produced by AI systems. AI accountability ensures that organizations can effectively address risks, mitigate harms, and uphold ethical standards in their AI initiatives. This level of accountability is a benefit of a strong AI governance leader within an organization.
In the context of AI governance, accountability extends beyond technical implementation. It encompasses strategic decision-making, policy development, and oversight of AI systems throughout their lifecycle. Key stakeholders, including senior leadership, board members, legal experts, compliance officers, and AI practitioners, play pivotal roles in driving accountability within organizations.
Stakeholder Management
Effective AI governance requires collaboration and coordination among diverse stakeholders. This includes:
- Senior Leadership: Executives and senior leaders set the tone for AI governance efforts, establishing values, priorities, and strategic objectives.
- Data Scientists and AI Practitioners: Data scientists and AI practitioners are responsible for developing, deploying, and maintaining AI systems.
- Legal and Compliance Teams: Legal and compliance experts provide guidance on regulatory requirements, legal risks, and ethical considerations related to AI governance. They ensure that AI initiatives comply with relevant laws, regulations, and industry standards, mitigating legal liabilities and reputational risks.
- External Stakeholders: Organizations must consider the perspectives and interests of external stakeholders, including customers, partners, regulators, and civil society. Engaging with these stakeholders fosters transparency, builds trust, and enhances the legitimacy of AI governance efforts.
While many stakeholders will be involved, it’s the AI governance lead who is responsible for driving this consensus and ensuring the usage of AI is secure and responsible within an organization.