Implementing strategies for RAI

1. Implementing strategies for RAI

Welcome back! This video outlines a step-by-step guide to navigating the complexities of implementing responsible AI within any organization. AI governance is all about making sure the business benefits from developing or deploying an AI, then checking what rules and regulations apply, checking ethical and responsible guidelines and frameworks, and finally making sure it is implemented well, has full organizational support and external stakeholders are considered. This calls for an 8-Step journey to Responsible AI.

2. 8-Step journey to Responsible AI

So let’s look at these 8 steps before taking a deep dive: Step 1: Embrace AI governance Step 2: Build an AI Playbook Step 3: Identify key internal and external stakeholders Step 4: Leverage internal support mechanisms Step 5: Embrace a multi-stakeholder approach for external stakeholders Step 6: Explore additional responsible behavior indicators Step 7: Implement governance tools Step 8: Monitor, audit, and evaluate AI governance

3. Step 1: Embrace AI governance

Start by getting organizational support for AI governance, emphasizing its critical role in responsible AI development. Highlight the necessity of aligning AI with business goals and ethical standards, incorporating existing compliance frameworks like GDPR, and creating strategies to track AI usage.

4. Step 2: AI playbook

Let’s talk about putting it all down on paper, Step 2. Integrating AI governance is strategic, aligning AI with business goals and ethics, ensuring regulatory compliance, and building trust. It involves a plan that integrates AI into existing governance frameworks. This is all documented in an AI playbook and remember: this is not a static document.

5. Step 3: Identify key internal and external stakeholders

Step 3 emphasizes the importance of identifying and engaging both internal and external stakeholders, including employees, unions, customers, regulators, and community organizations. Understanding their perspectives enhances AI solutions' inclusivity and effectiveness.

6. Step 4: Leverage internal support mechanisms

The next step leverages internal support through Codes of Conduct, Ethics Boards, and AI Squads to guide and review ethical AI development. Examples include IBM's AI Ethics Board, Meta's Oversight Board, Telefonica’s AI Champions and Microsoft's AETHER Committee. Additionally, investing in AI literacy through education and training is crucial for awareness of AI risks and responsible practices. This effort is vital as research shows that 4 in 5 people want to learn more about how to use AI in their profession.

7. Step 5: Embrace a multi-stakeholder approach

Step five focuses on engaging external stakeholders through a multi-stakeholder approach, including users, community members, industry experts, and policymakers, to ensure AI's societal relevance and acceptance. Emphasizing participatory development, it involves collaborating with communities to integrate their insights and feedback, making AI development community-driven and inclusive.

8. Step 6: Explore additional responsible behavior indicators

In this next step organizations can further demonstrate their commitment to responsible AI through initiatives like becoming a BCorp, a member of the Responsible AI Institute, adhering to Corporate Social Responsibility (CSR) standards, and transparent Environmental, Social, and Governance (ESG) reporting. These actions reflect a broader commitment to ethical business practices and societal well-being.

9. Step 7: Implement governance tools

Step 7 outlines AI governance implementation as a pyramid: Foundational principles reflecting the organization's core middle-tier guidelines and policies based on these principles, and top-level procedures for compliance. It emphasizes ensuring mechanisms effectively uphold responsible AI. And finally to double-check readiness, an organization can use the AI Readiness Quotient (AI-RQ), a diagnostic tool designed by Wharton.

10. Step 8: Monitor, audit, and evaluate AI governance

Lastly, organizations must continuously monitor, audit, and update their AI governance to match ethical standards, technological progress, and regulations. This includes reviewing AI projects for policy compliance, evaluating their impact, and refining practices based on feedback. Leadership plays a crucial role in ensuring clear task assignments within this iterative process, promoting ethical integrity and trust in AI systems. The concept of a Chief AI Governance Officer and the AI playbook embody these efforts, guiding responsible AI development that benefits society and the economy.

11. Let's practice!

Implementing AI is a step-by-step exercise that any organization needs to execute. Let’s do our own exercises to see if this material resonates with you.

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