AI governance consulting

If you’re integrating AI into your organization, you may fall under the purview of any or all of the following regulatory frameworks:

• GDPR (General Data Protection Regulation): while not AI-specific, it heavily impacts AI systems that process personal data of EU residents.
• CCPA/CPRA (California Consumer Privacy Act/California Privacy Rights Act): similar to GDPR, but for California residents.
• NIST AI Risk Management Framework: voluntary guidelines for managing risks in AI systems.
• EU AI Act (proposed): would classify AI systems based on risk levels and set requirements accordingly.
• Industry-specific regulations:
  • Healthcare: HIPAA, FDA regulations on AI/ML-based medical devices
  • Finance: SEC guidelines on AI use in trading, FINRA regulations
  • Human resources: EEOC guidelines on AI in hiring
• Sector-agnostic AI guidelines:
  • OECD AI Principles
  • UNESCO Recommendation on the Ethics of AI
• Local AI regulations: some states and cities have enacted or proposed AI-specific laws.
• ISO/IEC standards such as ISO/IEC 23894 for AI risk management.

When deploying or developing AI systems and processes, there are a wide range of ethical considerations to keep in mind:

1. Fairness and non-discrimination: ensuring AI systems do not perpetuate or amplify biases based on race, gender, age, or other protected characteristics.
2. Transparency and explainability: making AI decision-making processes understandable and interpretable, especially for high-stakes decisions.
3. Privacy and data protection: safeguarding personal information and ensuring responsible data collection and use.
4. Accountability: establishing clear lines of responsibility for AI system outcomes and decisions.
5. Safety and reliability: ensuring AI systems are robust, secure, and perform as intended without causing harm.
6. Human oversight and control: maintaining appropriate human involvement in AI-driven processes and decisions.
7. Beneficence: ensuring AI is developed and used for the benefit of humanity and individual well-being.
8. Autonomy: respecting human agency and freedom of choice in AI-human interactions.
9. Justice and fairness: ensuring equitable distribution of AI benefits and risks across society.
10. Environmental sustainability: considering the ecological impact of AI systems, including energy consumption.
11. Informed consent: ensuring individuals understand when they're interacting with AI and how their data is being used.
12. Long-term impacts: considering potential societal and economic effects of AI deployment, such as job displacement.

These principles often intersect and can sometimes conflict, requiring careful consideration and balancing in the responsible development of AI.

The Talbot West corporate governance program is comprehensive and tailored to your organizational challenges and objectives. Here are some components that we typically include; your corporate governance instance may require different modules. 

1. Guide the establishment of a board oversight committee on AI and technology. This committee serves the following functions:
1. Dedicated to AI governance and emerging tech risks
2. Includes members with AI expertise and ethics backgrounds
3. Quarterly reviews of AI projects and their ethical implications
4. Tasked with oversight of AI and its responsible development and deployment
2. Guide the establishment of an AI ethics review board to serve the following functions:
   1. Cross-functional team including legal, tech, and business units
   2. Reviews all major AI initiatives before implementation
   3. Ensures alignment with ethical AI principles and legal frameworks
   4. Assesses the organizational and societal impact of AI initiatives
3. Draft an AI risk management framework specific to your organization, based on that of the National Institute of Standards and Technology (NIST)
   1. Includes AI-specific risk assessments and mitigation strategies
   2. Regular audits and continuous monitoring of AI systems
4. Draft ethical AI guidelines
   1. Clear principles for fairness, transparency, and accountability
   2. Mandatory training for all employees involved in AI projects
   3. Annual recertification process
5. Propose data governance and privacy protection protocols
   1. Strict protocols for data collection, use, and storage
   2. Compliance with GDPR, CCPA, and other relevant regulations
   3. Regular privacy impact assessments for AI systems
6. Draft transparency and explainability protocols:
   1. Requirements for documenting AI decision-making processes
   2. Tools and methodologies for explaining AI outputs to stakeholders
   3. Clear communication channels for AI-related inquiries and concerns
7. Propose a bias detection and mitigation program:
   1. Regular audits of AI systems for potential biases
   2. Diverse data collection and curation practices
   3. Ongoing monitoring and adjustment of AI models
8. Draft an AI incident response plan:
   1. Clear procedures for addressing AI-related failures or ethical breaches
   2. Defined roles and responsibilities for rapid response
   3. Regular drills and scenario planning to mitigate reputational damage
9. Draft a stakeholder engagement strategy:
   1. Regular communication with employees, customers, and shareholders about AI use
   2. Feedback mechanisms for addressing concerns
   3. Participation in industry AI governance initiatives
10. Propose a continuous improvement process:
    1. Regular reviews and updates of the governance program
    2. Benchmarking against industry best practices
    3. Integration of lessons learned from AI deployments
11. Propose a schedule of regulatory compliance monitoring:
    1. Dedicated team tracking AI-related regulations
    2. Proactive adaptation of governance practices to emerging laws
    3. Regular compliance audits and reporting
12. Guide the creation of an ethical AI innovation pipeline:
    Encourages development of AI solutions that align with corporate values
    1. Provides resources for ethical AI research and development
    2. Rewards initiatives that demonstrate exceptional ethical considerations
13. Governance training for all relevant stakeholders
    1. Workshops and scenario planning
    2. Education on AI-related risks

This program is designed to be flexible and adaptable, allowing you to stay at the forefront of responsible AI use while managing associated risks effectively. Our robust governance frameworks will guide the ethical development of your AI program and set you on a solid foundation for digital transformation within our organization.

If you’re implementing generative AI or other forms of AI, and are debating whether to engage an AI governance consulting service, ask yourself the following questions:

1. Do you fully understand the ethical implications, legal requirements, and potential risks associated with your AI projects?
2. Are you confident that your AI systems meet all relevant compliance requirements, regulations, and standards?
3. Have you established clear policies and procedures for the development, deployment, and use of AI in your organization, including managing ethics and bias?
4. Is your team equipped to handle the complexities of AI risk management and mitigation?
5. Can you explain how your AI systems make decisions, especially for high-stakes applications, to all stakeholders?
6. Are you prepared to handle potential AI-related incidents or failures?
7. Do you have a strategy for communicating your AI use to stakeholders, including customers and employees?
8. Is your board of directors equipped to provide oversight on AI-related risks and opportunities?
9. Are you keeping up with the rapidly evolving landscape of AI regulations and industry best practices?
10. Have you conducted a comprehensive AI risk assessment across your organization?
11. Do you have mechanisms in place to ensure the privacy and security of data used in your AI systems?
12. Are you confident in your ability to monitor, audit, and explain your AI systems if required by regulators?
13. Do you have a clear understanding of the long-term societal and economic impacts of your AI deployments?
14. Have you established metrics to measure the ethical performance and impact of your AI systems?
15. Do you have a dedicated team with expertise in AI governance, or are you relying on general IT or data science staff?
16. Are you aware of the potential reputational risks associated with improper AI governance?
17. Can you ensure that your AI initiatives align with your overall business goals and ethical standards?
18. Are you prepared to continuously update and adapt your AI governance practices as technology and regulations evolve?

If you answered "no" or "unsure" to any of these questions, you might benefit from the expertise of an AI governance consultant. They can help you navigate these complex issues and establish a robust governance framework for responsible AI use in your organization.