1. Staying on track
Welcome back. In this video, we explore the continuous practices of monitoring, auditing, and ensuring accountability within your organization to stay on track with responsible AI deployment.
2. The comprehensive approach to Responsible AI
Responsible AI requires a commitment not just to creating AI systems but also to continuously monitoring, evaluating, and auditing them to maintain ethical integrity. Let me take you through these three key elements.
Monitoring is the ongoing observation of AI systems to ensure that they perform as intended and adhere to ethical standards. It is about being vigilant and responsive to how the AI operates in real-world scenarios.
3. Evaluating
Evaluating is a critical step that involves assessing the effectiveness and ethical performance of AI systems against established criteria. This includes looking at the system's impact on various stakeholders, checking for fairness and bias, and ensuring that the AI's actions align with the organization's values. Evaluating is about measuring outcomes and processes against the goals of responsible AI, which necessitates qualitative judgments, like the user experience and societal impact, beyond quantitative metrics.
4. Auditing
Lastly, Auditing is a more formal, often third-party, review process that ensures compliance with legal and regulatory requirements, as well as organizational policies.
It is a process resembling a financial audit, which involves an external auditor thoroughly examining your organization's financial records, accounting methods, and internal safeguards.
AI auditing is a critical process designed to evaluate an AI system comprehensively, identifying potential risks related to its technical capabilities and governance structure. It involves a detailed analysis of the system's operations to uncover vulnerabilities, and proposes measures to mitigate these risks.
Additionally, AI auditing underscores the importance of accountability, ensuring that when flaws are identified, there is a clear framework to determine who is responsible for addressing these issues. This accountability is crucial for maintaining trust in AI technologies, ensuring that they operate fairly, safely, and transparently, and that responsible parties are held accountable for rectifying any identified shortcomings.
5. Red and blue teaming
Incorporating red and blue teaming is a dynamic method to evaluate and improve AI systems continuously. But what do these teams do?
Red team: They challenge the AI, simulating ethical and operational breaches to evaluate the system's resilience and identify vulnerabilities.
Blue team: They respond to the red team’s challenges, reinforcing the system's defenses and ensuring that the AI’s ethical integrity is maintained.
This active evaluation process through adversarial simulations is essential for staying on track with AI in your organization. Google Red Team is a well known example, OpenAI has a Red Teaming Network and more and more organizations are even introducing Purple teams, to ensure and maximize the effectiveness of the Red and Blue teams.
6. Continuous evaluation
Evaluating responsible AI means integrating ethical reflections into every stage of the AI lifecycle.
In the realm of AI, it's vital to maintain the quality and reliability of the data and the systems that use them.
Constantly check to make sure everything is in top shape and that your AI is sturdy and dependable.
Just as important is how understandable the AI's decisions are; you want to ensure that they're clear enough for everyone to follow, like a story that makes sense from beginning to end. This is what we previously referred to as explainability and interpretability.
Also keep a close watch to make sure that responsibilities are well-defined and that you can pinpoint exactly who did what within your AI projects and document this.
It is like knowing who brings what ingredients to a shared kitchen, it keeps things smooth and accountable.
When it comes to regulation and the people using your AI, you're always on guard to ensure their rights are protected and that they're not being taken advantage of in any way.
Make sure you check the principles you handle have not changed, check if your risk tolerance remains the same and your jurisdiction is still what it was.
Continuously scan for any unfairness in how the AI behaves, always tweaking and adjusting to stamp out biases like fixing a lopsided table so everyone can sit at it comfortably.
Lastly, you regularly put your AI through its paces to make sure it's tough enough to handle any curve balls thrown its way, ensuring it stays reliable and fair, come what may.
It's about keeping your AI systems ready and resilient!
7. Let's practice!
Let’s finish with a final set of exercises.