AI GOVERNANCE

Let’s get down to business and meet your instructor Anna Bethke. She’s the Director of Data Science and Ethical AI at Included AI. Now she’s partnering with ELVTR to teach the blueprint to AI governance!

• Instructor introduction
• Course objectives & flow
• Final project overview
• Brief overview, key moments, and rapid growth of the AI industry
• Q & A

With great AI power, comes greater AI responsibility. It has the ability to create solutions, protect wildlife, and invade users’ personal privacy – all at once. It’s important to consider the unintended consequences of AI to assess how responsible you’re being and to find areas where you could be more effective.

• Evolution of AI and its impact on society
• Potential positive and negative consequences of AI
• Case Study: Positive & negative examples of AI from Meta, Google, WeWalk, etc.
• Workshop: Analyze an AI system to explore user & societal impact

Assignment 1: AI System Impacts
Select an AI system, and fill out a pre-formatted slide outlining its potential positive and negative consequences.

AI, like a newborn, has the potential to become anything. As responsible adults, it can be a challenge to keep up with how fast AI is growing. It can quickly head down the wrong path and jump into an even worse path. We’re going to cover diverse frameworks and tools to help you guide your AI intelligently.

• Overview of ethical AI
• Key principles in ethical AI
• Development of AI standards
• Case Study: Global initiatives & collaborative standards (NIST & GDPR) 

Assignment 2: Applicable AI Governance themes
Identify relevant AI Governance themes for the chosen AI system in assignment 1 with concise 1-2 bullet points explaining their applicability.

Ethical prevention is better than an algorithmic cure. So, how can we make AI systems better for everyone? Now that the groundwork is in place, let’s build upon our risk assessment skills and stay ahead of the tech game.

• Ethical considerations in AI development
• Identifying, assessing & mitigating AI risks
• Implementing ethical AI practices
• Demo: Deon
• Workshop: Mitigating risks in AI systems

Assignment 3: Risk Mitigation
Propose risk mitigation techniques for potential negative consequences identified in assignment one, as part of final project building blocks.

Oops…something went wrong! AI is guaranteed to make mistakes. Turn your “oops” into feedback loops while taking accountability. Some mistakes are minor, other mistakes can majorly impact your users and business. Today you’ll practice handling undesirable content.

• Regulatory & organizational oversight
• AI accountability, transparency & legal frameworks
• Accountability across levels: leaders & teams
• Workshop: The process of reporting inappropriate & inaccurate content

Humans have biases, and so do our creations. Intentional or not bias also exists within AI. How can we proactively reduce them? Let’s find out!

• Algorithmic bias & fairness
• Types of bias
• Case Study: Impact of fairness decisions
• Demo: Google's What-If tool

Assignment 4: Fairness definition understanding
Match fairness names with their definitions and potential use cases. Use the What-If tool, IBM’s 360 toolkit, Aquitas, and the lecture notes for help.

Transparency builds trust. When it comes to AI every user wants to trust the brands they engage with. But what does that actually look like in practice? Get ready to practice creating model cards, like nutrition labels for AI systems, for some hands-on experience.

• Transparency overview
• AI transparency methods
• Model cards - nutrition labels for AI systems
• Workshop: Complete a model card for an AI system

Assignment 5: Model Card Creation
Fill out a model card for the system of your final project AI system following the templates provided.

Laws are being established to protect users’ data. The proposed Artificial Intelligence Act and other location-based regulations have set standards on how we collect, process, store, and leverage personal data. You’ll learn how these systems impact AI and how to create better systems that champion privacy.

• Data privacy regulations and compliance
• Differential privacy, how it works, and how to implement it
• Balancing innovation and privacy concerns
• What can users do to protect their data
• Workshop: Create strategies to prevent sensitive information leaks in AI systems

Ethics are subjective. With an international community able to access the same technologies, we may all have a different perspective on how ethical they are. How can you keep the whole world satisfied with your AI governance? Learn how to overcome these challenges when building AI systems.

• Comparative analysis of AI governance models
• Challenges and opportunities in global AI cooperation
• Cross-border data governance
• Guest Speaker

Your AI might be ethical, but how can you prove this to your users? After all, the way you handle user data is the way you treat your customers. Dive into how big businesses develop ethical AI practices, avoid “ethics washing”, and navigate pitfalls.

• Corporate ethics in AI development
• Dangers of “ethics washing”
• Balancing profit & social impact
• Workshop: CSR in the age of AI

Assignment 6: AI Governance Pitch
Create 1-2 slides on a new AI policy which should exist including the governance theme, regulation points, etc.

It is hard to not find examples of problematic generative AI. These often take the form of inappropriate or inaccurate images, videos or text. And problematically the generated content can often be really hard to discern from real content. How can we utilize this type of technology ethically and where can regulation help?

• Generative AI overview
• Particular AI governance concerns
• How can generative AI be used ethically?
• Case Study: Mainstream generative AI
• Guest Speaker 

We are creating new ways to utilize AI at an unprecedented pace. In this class we will explore how regulation is trying to keep up, and how existing policies help reduce the potential risks of these systems. We will also get to practice creating governance on a new technology.

• AI and autonomous systems
• Case Study: Regulating AI in Emerging Industries
• Anticipating future governance challenges
• Workshop: Creating governance on a new tech

Now that you know much more about AI Governance let's chat about where you can go from here. We will talk about careers there are in the field, and how to get started. Also, you will get a chance to present some of your final projects!

• Types of careers
• AI governance work ranges
• Advancing your skills
• Demo: Job boards
• Presenting model card (optional)

Final Project: Create ~10 slides showcasing how to apply AI Governance Principles to a Real-World Scenario.