Key Elements of a Robust AI Governance Framework

By Morgan Sullivan

Senior Content Marketing Manager

January 30, 20247 min read

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At a glance

  • AI governance is essential for managing the societal and individual impacts of AI—focusing on ethical considerations, legal compliance, and balancing innovation with potential risks.
  • The evolution of AI governance, from an informal academic discipline to a structured technical discipline, highlights the need for comprehensive frameworks that address data privacy, algorithmic bias, and accountability.
  • Key components of an effective AI governance framework include technical guardrails, policies, and training around privacy and data protection, transparency and explainability, appropriate oversight, and more.

Understanding and defining AI governance

AI governance refers to the strategies, policies, and technical guardrails that guide and regulate the development and use of artificial intelligence.

It includes ethical considerations, such as fairness and privacy, legal aspects like regulatory compliance, and the technical structures that govern the flow of data into and out of large-language models (LLMs).

The goal is to ensure AI is used responsibly, safely, and for the public good—balancing innovation with societal needs.

One example of an AI governance policy could be guidelines implemented by a healthcare organization for the ethical use of AI in patient care. This policy might include principles such as:

  • Ensuring patient data privacy
  • Requiring informed consent to use patient data in AI models
  • Conducting regular audits for AI-driven diagnostic tools to prevent biases
  • Establishing clear lines of accountability for decisions made by AI systems

This approach aims to safeguard patient rights and ensure that AI tools enhance, rather than compromise, the quality of healthcare.

Throughout this post, we'll explore other real-world examples of AI governance, as well as some of the ways world governments are looking to regulate AI.

Historical evolution of AI governance

Before the advent of technologies like ChatGPT, Dall-E, and Stable Diffusion, AI was a niche academic field with limited real-world applications.

Now, there are dozens of LLMs that, as of this writing, have become the norm in our everyday lives.

Initially, AI governance was informal, mainly guided by the ethical norms of researchers. As AI started impacting various sectors like healthcare, finance, and transportation, the need for formal governance structures became apparent.

In recent years, the focus has shifted to developing comprehensive frameworks that address AI's expansive reach and impact, including data privacy and algorithmic bias.

Major developments include the establishment of ethical guidelines by organizations like the EU and IEEE, and national strategies by countries like the U.S., China, and members of the EU.

These changes reflect a growing understanding of AI's societal impact and the need for a coordinated approach to ensure its benefits are maximized while minimizing risks.

Why is AI governance so important for modern organizations?

Just like any new technology, democratized AI platforms like ChatGPT are still largely in the "Wild West" phase of their maturity. As a global society, we're learning the boundaries of what AI can and cannot (and should not) do, as well as the risks involved with using AI. These include, but aren't limited to:

  • Security vulnerabilities
  • Bias and discrimination issues
  • Privacy invasion
  • Ethical concerns

For example, most large language models like ChatGPT use user input as training data. This means if a user inputs proprietary internal source code or sensitive data, that information is vulnerable in the event of a cyber-attack.

This is just one example of why AI governance is so important and why it will continue to be an essential part of every organization's policies and procedures.

Examples of real-world AI governance scenarios

Fraud detection in financial services

Many financial institutions use AI for fraud detection and prevention. The governance challenges here are ensuring the accuracy and fairness of the AI models, protecting customer data privacy, and complying with financial regulations.

Recruitment and hiring in human resources

Over the past few years, there's been a massive uptick in using AI systems to screen job applicants. Organizations must govern these systems to prevent biases, ensure fairness, and comply with labor laws.

Supply chain management

AI plays a huge role in optimizing supply chain logistics. Governance in this industry needs to focus on data-sharing agreements, operational safety, and mitigating the risk of system failures.

Key components of an AI governance framework

Countries across the globe are pouring resources into developing robust AI governance frameworks. As varied as they are, most contain common key components.

Whatever framework you implement at your organization should at least include these fundamental tenets.

1. Transparency and explainability

This means AI systems should be understandable and their actions should be able to be explained. Users should know how and why an AI system makes decisions or recommendations. AI systems should never be a black box.

2. Fairness and non-discrimination

AI should be designed to avoid unfair biases. It should treat all users equitably and not discriminate based on race, gender, or other personal characteristics.

3. Privacy and data protection

This involves ensuring personal data used by AI is handled securely and privately, complying with data protection laws, and respecting user consent.

4. Accountability and oversight

This refers to having clear responsibilities and control mechanisms for AI systems. It ensures that if something goes wrong, there are ways to identify the problem, correct it, and hold the right people or entities responsible.

AI governance frameworks in practice today

The AIGA AI governance framework

The AIGA AI Governance Framework is a comprehensive framework designed for the responsible implementation of AI in organizations.

It aligns with the OECD's AI system lifecycle framework and is geared towards compliance with European AI regulations.

It argues that implementing AI responsibly must follow a three-layered framework:

  1. The environmental layer covers legal requirements and stakeholder pressures
  2. The organizational layer focuses on strategic and value alignment within the organization
  3. The AI system layer addresses operational governance for AI system development and management

It emphasizes transparency, accountability, fairness, and ethical AI system deployment. This framework is a result of academic-industry collaboration and is value-agnostic, making it adaptable for various organizational needs.

For detailed information, you can refer to the original AIGA documentation.

EU ethics guidelines for trustworthy AI

The Ethics Guidelines for Trustworthy AI, presented by the High-Level Expert Group on AI in 2019, outline key principles to help responsible organizations put AI ethics into practice.

The guidelines propose seven key requirements for AI systems:

  1. Agency and human Oversight: AI should empower humans, ensuring they can make informed decisions and have fundamental rights protected, with oversight mechanisms like human-in-the-loop systems.
  2. Technical Robustness and Safety: AI must be secure, reliable, and resilient, with fallback plans to prevent unintentional harm.
  3. Privacy and Data Governance: AI should respect privacy and data protection, with mechanisms ensuring data quality, integrity, and legitimate access.
  4. Transparency: AI operations and decisions should be transparent and understandable to users, with traceability of data and systems.
  5. Diversity, Non-discrimination, and Fairness: AI technologies should be free from bias, promoting diversity and accessibility for all.
  6. Societal and Environmental Well-being: AI technology should benefit society and the environment, considering its broader social and ecological impact.
  7. Accountability: There should be mechanisms for AI accountability and auditability, ensuring responsible outcomes and accessible redress.

Singapore Model AI Governance Framework

The Singapore Model AI Governance Framework is designed to foster a trusted digital ecosystem where AI is used responsibly.

It emphasizes a balanced approach to facilitate innovation while safeguarding consumer interests. Key aspects include:

  • Transparency
  • Fairness
  • Safety of AI systems
  • A focus on ethical principles

The framework provides practical guidance for organizations to implement responsible AI solutions, with a sector-agnostic approach for broad applicability. It also includes tools like AI Verify for testing AI systems against ethical principles, and resources for understanding AI's impact on jobs.

Final thoughts

AI technologies and AI-augmented decision-making aren't going anywhere anytime soon.

ChatGPT has only been public since November of 2022 and it's already represented a massive shift in the global workforce, as well as how organizations safeguard their privacy and mitigate risk.

Which framework you choose for your organization isn't as relevant as the underlying key components of safety, transparency, security, and impartiality.

At Transcend, we build software that helps you maintain trust with your users, avoid privacy violations, and safeguard your data. Learn more about how Transcend is leading the charge in data privacy, or click here to see a live demo of our software.

About Transcend Pathfinder

With Pathfinder, Transcend is building the new frontier of AI governance software—giving your company the technical guardrails to adopt new AI technologies with confidence.

As AI becomes integral to modern business, companies face two distinct challenges: maintaining auditability and control, while managing the inherent risks. Without the right systems in place, businesses are slow to adopt AI, and risk losing their competitive edge.

Pathfinder helps address these issues, providing a scalable AI governance platform that empowers companies to accelerate AI adoption while minimizing risk.

By Morgan Sullivan

Senior Content Marketing Manager

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