What up AI homies?! Welcome to your dose of AI legal news. I’m keeping it light this time around because “ain’t nobody got time for…” writing a whole newsletter when you’re lawyering full time. 🙄⚖️🤖

On the docket today:

• EU Lawmakers Ratify Political Deal on Artificial Intelligence Rules

• FCC Goes One Step Further by Making AI-Generated Voices in Robocalls Illegal

• Northern District of California grants unfair competition claim again OpenAI

• EU AI Act: Prohibited Practices Acts Cheat Sheet

• Depressing Attorney Meme

• $800+ Worth of AI Governance Material

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Depressing Attorney Meme

$800+ Worth of AI Governance Notes PLUS: IAPP’s AI Governance Course Notes

Moving on to Module 4 of the IAPP’s AI Governance course (notes). Alrighty, it’s time to geek out and keep learning about AI Governance!!! 🤓📚

Module 4: Interoperability of AI Risk Management

Introduction

• Importance of reviewing and harmonizing existing risk management with new AI risk strategies.

• Understanding existing programs and their objectives.

• Creating new processes for unique AI risks and ensuring efficiency and protection.

Security Risk

• Risks include hallucinations, deepfakes, training data poisoning, data leakage, and filter bubbles.

• Organizations respond by banning or limiting AI use.

• AI algorithms can concentrate power, leading to overreliance and security holes.

• Risks of adversarial machine learning attacks.

• Misuse of AI and transfer learning attacks.

• Storing training data in less secure environments.

Operational Risk

• High costs in hardware and environmental impact.

• Data corruption and poisoning risks.

• Need for good identity and access management.

Privacy Risk

• Data persistence, repurposing, spillover, and collection from AI.

• Challenges in informed consent and opting out.

• Limiting data collection and deletion complexities.

Regulation and Legal Risk

• Compliance with laws and regulations.

• Liability, intellectual property, human rights, and reputational risks.

• Importance of aligning different risk management strategies to prevent gaps.

Privacy Harms

• Categories include physical, reputational, relationship, economic, discrimination, psychological, and autonomy harms.

• Use of AI in autonomous weapons, healthcare decisions, and labor displacement.

• Effects on justice system and accountability.

Summary

• Balancing AI benefits with potential harms.

• Evolving risk management strategies to include AI.

• Regular assessment of vulnerabilities and potential harms.

Module 4: Principles of AI Risk Management

Introduction

• Incorporating risk management and AI governance principles.

• Understanding stakeholders, AI programs, and associated risks.

Principles

• Pro-Innovation Mindset: Preparedness for changes and alignment with principles.

• Risk-Centric Governance: Considering risk factors in governance.

• Consensus-Driven Planning and Design: Involving all stakeholders and ensuring understanding of needs vs. risks.

• Outcome-Focused Team: Clarity on desired outcomes and exploring better achievement methods.

• Non-Prescriptive Approach: Context-specific risk approaches for adjustment and evolution.

• Law-, Industry-, and Technology-Agnostic Framework: Interoperability and flexibility without bias.

• Third-Party Risk Management: Ensuring end-to-end accountability.

Risk Management

• Treating risks case-by-case.

• Involving business and technical stakeholders.

• Enumerating potential harms and assessing data used in AI.

• Technical tools to assess AI for bias and risks.

• Categorizing AI risks according to the EU model.

Summary

• Regular risk assessments in the context of regulatory requirements, risk tolerance, and industry standards.

Module 4: Establishing AI Governance and Strategy

Introduction

• Understanding the organizational operations and incentive structures.

• Tailoring AI governance to the organization's type.

AI Governance Stakeholders

• Identifying key stakeholders and their roles.

• Engaging leadership for AI governance support.

• Addressing pressures on tech teams and influencing behavioral change.

AI Governance Structure

• Leveraging existing structures and ensuring company-wide buy-in.

• Transparency about the state of AI governance maturity.

• Defining roles and responsibilities clearly.

• Incentivizing effective and safe AI products.

• Engaging HR for role identification and success measures.

Summary

• Building AI governance starts with understanding organizational structure and culture.

• Engaging stakeholders early and fostering a culture of responsible AI.

• Iterating the governance program from conception to completion, with clear roles and incentives.

That’s all for today!

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