AI: What is Your Policy?

So we need a policy on artificial intelligence (AI)? Really? On top of everything else? Well, only if you’re concerned about good governance, transparency, privacy – oh, and risk management.

Here’s the nitty gritty: a governance policy covers rules of use. Are you using a well-known tool? Or is the organization open to any tool? What information is being shared? Do you know? Is it proprietary? Is there a governance framework? Do you provide training in the how and what but also what it is not used for?

AI provides quick and easy access to information. It allows for rapid creation of content for marketing, communication, training. It allows a small organization to scale far beyond its staff. But it needs to be used safely and wisely.

Here are a few benefits of a policy:

• Ethical and Responsible Development and Use;
• Compliance with Laws and Regulations;
• Risk Identification and Mitigation;
• Stakeholder Trust and Confidence;
• Alignment with Organizational Values;
• Continuous Improvement.

An effective policy should cover for most organizations:

Ethical Principles and Values -- the ethical principles and values that will guide the development, deployment, and use of AI systems within the organization -- fairness, transparency, accountability, privacy protection, and respect for human rights.

Roles and Responsibilities -- of various stakeholders, including developers, legal and compliance teams, and executive leadership\ with clear lines of accountability and oversight mechanisms.

Data Governance -- practices including guidelines for data collection, storage, processing, and usage. It should ensure data quality, security, and privacy compliance, as well as address issues related to data bias and representativeness.

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 Risk Assessment and Mitigation-- mandate a comprehensive risk assessment process to identify potential risks, such as algorithmic bias, privacy violations, and unintended consequences. It should also establish strategies and procedures to mitigate these risks.

Transparency and Explainability

The policy should mandate transparency and explainability, ensuring that stakeholders can understand the basis for AI-driven decisions and raise concerns if necessary.

Legal and Regulatory Compliance

The policy should ensure compliance with relevant laws, regulations, and industry standards related to AI, such as data protection regulations, consumer protection laws, and sector-specific guidelines.

Stakeholder Engagement

The policy should establish mechanisms for engaging with relevant stakeholders, including employees, customers, and community members, to gather feedback and address concerns related to the development and use of AI systems.

Continuous Improvement

The policy should include provisions for regular review and updating to keep pace with technological advancements, emerging risks, and evolving best practices in AI governance.

And for some more involved in AI:

Model Development and Validation

The policy should outline best practices for developing and validating AI models, including requirements for model documentation, testing, and continuous monitoring. It should also address model interpretability and explainability.

By addressing these key areas, an AI governance policy can help organizations develop and deploy AI systems in a responsible, ethical, and trustworthy manner while mitigating potential risks and fostering innovation.

What are your main “pain points” dealing with risk management and AI systems?

What advice would be most helpful to you?
And we always assume that you are asking for a friend!

Get in touch. We’ll address your questions and concerns in an upcoming blog post.

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