Intro
Developing an AI Governance Strategy and Framework is an essential step for organizations looking to harness the power of AI responsibly. A comprehensive framework outlines best practices, guidelines, and procedures for the ethical and safe development, deployment, and management of AI technologies. A well-defined strategy will help ensure that your organization successfully applies your initial framework and stays up to date on industry developments, regulatory standards, and best practices.
Before developing your strategy, it is essential to understand the general landscape of AI governance frameworks. Set by governments and standards-setting bodies, these frameworks will serve as the foundation for your internal AI governance strategy and framework.
Some existing frameworks that aim to regulate AI, such as the Organization for Economic Cooperation and Development (OECD), the United Kingdom’s (UK) pro-innovation approach to AI regulation, and the National Institute for Standards and Technology (NIST) Risk Management Framework (RMF) tout a flexible, pro-innovation approach that emphasizes the rapidly evolving capabilities of AI systems and highlights existing legal tools to address potential harms and warns of the tradeoffs created by more stringent regulation.
Frameworks proposed by the European Union (EU AI Act) and Brazil (Brazilian National Strategy for Artificial Intelligence, EBIA) emphasize compliance with existing state laws related to privacy, discrimination, and acceptable content, require licenses to develop and make high-risk AI systems publicly available, and embrace a statutory and legislative approach for compliance and enforcement.
The White House AI Bill of Rights heavily emphasizes AI’s potentially harmful impact on protected classes and democracy. It outlines how developers, deployers, and regulators should work to mitigate risks before a system is adopted while recognizing the vital role the private sector will play in driving innovation and access to these technological capabilities.
Congress has held a handful of hearings focused on AI since May of 2023. Members of both chambers have introduced several pieces of legislation that would respectively create a federal AI task force, provide AI training for federal employees, deny AI firms Section 230 immunity regarding generative AI, and secure the software supply chain for the Department of Defense.
Steps to Consider When Developing an AI Governance Framework
Preliminary Steps:
Conduct a Risk Assessment: Evaluate the potential risks associated with the AI technologies you plan to develop or deploy, including ethical, safety, and legal risks. This article from eWeek focuses on 6 AI risk management tips.
Stakeholder Engagement: Involve stakeholders like employees, customers, industry experts, ethicists, and regulators to provide multiple perspectives on governance.
Regulatory Compliance: Understand and list the legal requirements applicable to your industry or jurisdiction, such as GDPR for data protection in the EU or healthcare. A proposed rule from HHS would require electronic health record systems using AI and algorithms to inform users about how those technologies work. Check out this guide to AI compliance and regulation.
Core Elements: Ethical Guidelines: Develop a set of ethical principles that align with broader societal values and norms, such as fairness, non-discrimination, and respect for human autonomy. AI development is often tied to ingesting large data sets to build algorithms that produce output that assists with intelligent decision-making.
Whenever that input for an AI model involves personal data or any output is used to make decisions that affect the rights or interests of individuals, the AI model and its applications are most likely already directly subject to various data privacy laws.
Transparency Protocols: Establish guidelines for making the AI’s decision-making process transparent. This may include documentation and interpretability of AI models in use. Recently, experts told the Senate Commerce Subcommittee on Consumer Protection Congress Should Mandate AI Guidelines for Transparency and Labeling.
Data Governance: Include data acquisition, storage, and handling policies to ensure data quality and compliance with privacy laws.
Safety Measures: Develop standard operating procedures for ensuring the safety and reliability of AI systems, especially for applications in critical areas like healthcare, transportation, and security.
Accountability and Oversight: Assign roles and responsibilities for the governance of AI, possibly creating a specific committee or appointing an AI Ethics Officer. Include a mechanism for internal and external audits. Does your board have a plan yet for AI oversight?
User Consent and Autonomy: Outline procedures for obtaining informed consent from users whose data will be processed or impacted by AI decisions.
Monitoring and Auditing: Create mechanisms for ongoing monitoring and periodic auditing of AI systems to ensure compliance with the framework and to identify any emerging risks or issues.
Redress Mechanisms: Develop methods for rectifying mistakes or harms caused by AI and outline dispute resolution mechanisms.
External Collaboration: Set up guidelines for collaborations or data sharing with external organizations, ensuring alignment with your governance framework.
Implementation: Train team members and stakeholders on the guidelines and policies outlined in the framework.
Documentation: Maintain extensive documentation of use cases, algorithms, data sets, and decision-making processes for auditing purposes.
Leverage Tools: Develop or adopt AI workflow tools that assist in monitoring compliance with the framework, such as dashboards or automated AI auditing systems.
Pilot Testing: Before full-scale deployment, pilot-test the framework to ensure it’s practical and effective.
Continuous Improvement on your AI Governance Framework
Regular Reviews: Periodically review and update the framework to adapt to new technologies, regulations, or societal values.
Public Reporting: Share performance metrics and auditing results with stakeholders and, where appropriate, the public to maintain transparency.
Feedback Loop: Implement a mechanism to gather feedback on the framework’s effectiveness and identify areas for improvement.
Overview
Building an effective AI Governance Framework is an ongoing process that requires the engagement of multiple stakeholders, constant monitoring, and adaptation to new developments in AI technologies and regulations.
Done right, a framework should make it impossible to separate the AI project from its governance. If you start with governance and use it to drive the process, you’ll eliminate the need for a complex and lengthy “bolt it on in the end” solution. Scrutinize your policies using cutting-edge AI Governance tools to provide a centralized view while engaging the entire organization and all stakeholders. It will take considerable time and many iterations, but ultimately, the framework will be sound and protect the business and your users down the road.
How Can Fairo Help?
Fairo is a SaaS platform focused on standards, simplicity, and governance to give organizations and their users the confidence to successfully and rapidly consume AI at scale. Fairo is committed to being the industry-standard platform for helping your organization implement its AI governance framework and strategy. Fairo seamlessly integrates into your existing ecosystem and is easy to consume. AI is a disruptive technology that will change how people work and live. We envision a world where AI is universally built responsibly, trusted, and not feared. We aim to provide an easy-to-use solution that helps organizations procure, develop, and deploy trustworthy AI solutions with confidence.
