An Open Letter to Boards: A Practical Guide to AI Fairness Governance for Boards

As AI technologies become increasingly integral to business operations, there is a growing imperative for companies to implement rigorous governance mechanisms around AI fairness. The ethical dimensions, especially bias mitigation, have ascended from being just good-to-have values to mission-critical components of corporate risk management and brand reputation.

While AI promises efficiency and insights, it also risks perpetuating societal biases if deployed without oversight. As fiduciaries, we must put processes in place to monitor our AI systems.

This guide aims to serve as a navigational tool for board members and C-suite executives, offering a road map through various available tools and frameworks designed to monitor, evaluate, and rectify biases in AI models.

The Business Case for AI Fairness 

Before diving into the tools, it is essential to understand why AI fairness is a non-negotiable aspect of corporate governance. The importance of AI fairness isn’t solely a question of social responsibility but is critically tied to corporate sustainability, brand integrity, and legal compliance.

Inequitable AI models not only risk reputational damage but can also lead to legal consequences. Hence, the mandate for fairness is as much about social responsibility as it is about corporate survival.

Artificial intelligence (AI) systems are increasingly being used to automate decisions that impact people’s lives, such as approving loans, making hiring decisions, and diagnosing medical conditions. However, there is a risk that AI models can perpetuate or even amplify existing societal biases if not built responsibly. Developers, policymakers, and the public need tools and frameworks to audit AI systems for discrimination and bias. 

Available Tools for the Board and C-Suite

Several open-source libraries provide algorithms, metrics, and visualizations to detect and mitigate unfairness in AI models:

1. NIST AI Risk Management Framework: Guiding Principles

What It Does

The National Institute of Standards and Technology (NIST) has proposed a structured approach to reduce AI bias risks through four guiding principles: understanding your use case, testing your system, monitoring your system, and engaging with stakeholders. It goes beyond mere bias detection, emphasizing a holistic risk management approach. NIST’s risk assessment guide advises that boards engage stakeholders early, monitor outputs post-deployment, and iteratively improve to address identified biases. While technical teams should embed fairness through the AI pipeline, auditing and governance cannot be solely their responsibility.

Who Can Use It

This framework is designed for broad organizational adoption and doesn’t require technical expertise, making it ideal for board members, policymakers, and compliance officers. It is best used as an overarching guide by board members and compliance officers, often in collaboration with technical teams for execution.

2. Pymetric’s Audit-AI: Discrimination Detection

What It Does

Audit-AI is an open-source Python library designed for statistical tests that identify biases in AI models, particularly those used in hiring, loan approvals, and medical diagnoses. Its strength lies in its rigorous statistical tests, making it useful for audits.

Who Can Use It

This tool best fits organizations with in-house software engineering capabilities. The software engineering team, in collaboration with legal and compliance units, would run the tool to produce reports for the board’s review.

3. Microsoft’s Fairlearn: AI Fairness Dashboard

What It Does

Developed by Microsoft, Fairlearn is an open source python package that focuses on assessing and mitigating unfairness through visualization dashboards and algorithmic adjustments.  Fairlearn provides a two-pronged approach: it allows you to visualize unfairness metrics and then offers algorithmic solutions to adjust these imbalances. It’s particularly useful in scenarios where the board needs to see impact assessments.

Who Can Use It

Fairlearn is designed to be user-friendly but requires some technical know-how for proper implementation. Business leaders and technical teams can collaborate to integrate Fairlearn into existing AI pipelines.

4. IBM’s AI Fairness 360: IBM’s Holistic Approach

What It Does

IBM’s toolkit offers a full suite of metrics and algorithms for assessing and reducing bias. Its unique feature is its capacity to intervene at multiple stages of the AI model life cycle. IBM AI Fairness 360 has over 70 built-in algorithms and tutorials using real case studies to demonstrate bias mitigation techniques.

Who Can Use It

Due to its comprehensive nature, this tool requires a level of technical expertise, making it ideal for software engineers. While software engineers will be the primary users, interactive demos and tutorials can help board members to grasp its utility.

5. Aequitas: Fairness Auditing

What It Does

Aequitas is an open-source toolkit that audits machine learning models for discrimination and bias, targeting developers, analysts, and policymakers.  It is designed for transparency, providing clear explanations of bias and equity issues in accessible language.  Aequitas gives you an auditing framework designed for non-technical evaluators. Its flexibility makes it ideal for monitoring different types of AI systems across various industries.

Who Can Use It

Aequitas provides non-technical auditing tailored for policymakers. The tool is flexible and can be used by both technical and non-technical teams to make informed decisions regarding the equitable deployment of AI systems.  


In addition to many of the open source options above, some companies offer proprietary bias monitoring tools. Though these often requires budget allocation, they simplify bias detection for non-technical business leaders.

6. PWC’s Bias Analyzer: AI Monitoring AI

What It Does

Bias Analyzer offers an enterprise-grade, web-based solution for bias detection and mitigation. It provides a more streamlined, turnkey option for boards that prefer a managed solution, offering recommendations for mitigation strategies.  Bias Analyzer provides an accessible web dashboard to analyze and simulate mitigation strategies. 

Who Can Use It

Due to its web-based interface, this tool is accessible to non-technical board members and business leaders interested in compliance and risk management.

7. Fiddler AI: AI Observability

What It Does

Fiddler AI offers an AI observability platform providing model monitoring, bias detection, error analysis, drift detection, and counterfactual explanations in accessible reports. This enables technical and non-technical teams to collaborate on improving model transparency, fairness, and trustworthiness.  Fiddler AI helps users to understand the logic, behavior, and impact of their AI models, as well as to monitor and improve them over time.

Who Can Use It

Fiddler AI is designed for both developers and business leaders to work together monitoring and acting on AI risks throughout the model lifecycle. The platform bridges communication gaps around model behavior. Fiddler AI is ideal for organizations that want to increase the transparency, accountability, and trustworthiness of their AI models.

8. TruEra: Enterprise Grade ML monitoring

What It Does

TruEra offers an enterprise-scale machine learning quality management platform with model testing, monitoring, analytics, and explainability. This drives model transparency, accuracy, and performance. TruEra provides comprehensive model evaluation and testing that drives quality and transparency, fast. 

Who Can Use It

TruEra integrates into workflows enabling developers to validate models. Dashboard visualizations also provide business leaders visibility into model quality.


The aforementioned list of tools is not meant to be fully comprehensive. Given the rapid pace of development in this space, innovative solutions are frequently emerging. Rather, the options presented aim to provide boards with a practical launching point to gain familiarity with capabilities in AI bias detection and mitigation. This introduction can kickstart informed conversations and exploration of how best to approach AI ethics within your specific organizational context.

Implementing Fairness Tools – Budget Needed

If AI is a growing part of your company’s business, boards should allocate resources to procure technology, train audit teams, and compensate marginalized stakeholders for participating.  Beyond the procurement of technology, this involves training audit teams and potentially compensating marginalized communities involved in bias impact assessments. 

Boards should formalize processes for bias detection, model risk management, and impact assessments. Responsible AI requires a commitment to transparency, accountability, and corrective action.

Establishing Robust Accountability Mechanisms

In addition to auditing tools, boards should consider implementing structural accountability mechanisms as part of the AI governance regime.

  • Ethics Review Board: We recommend establishing an ethics review board consisting of diverse internal and external perspectives. This oversight body can assess high-risk AI systems before deployment and on an ongoing basis, acting as an independent check on the organization. The board should have the authority to halt or modify AI if harmful biases or impacts are detected.
  • Public Fairness Reports: Making regular fairness reports publicly available demonstrates accountability to stakeholders. This transparency into the steps being taken to assess and mitigate algorithmic harms upholds trust and reputation.
  • Executive Compensation Tied to Ethics KPIs: Boards can incentivize accountability by incorporating AI ethics goals into executive compensation packages. A portion of incentives can be tied to key performance indicators like running fair ML audits, completing bias training, and achieving diversity metrics.
  • Contractor Ethics Requirements: External vendors and contractors involved in building AI systems should undergo mandatory bias training and commit in writing to abide by ethical AI principles. This ensures responsibility across the supply chain.

Adding structural oversight mechanisms builds on the foundation of auditing tools, embedding fairness as an ongoing priority. With a multifaceted approach, boards can comprehensively govern AI ethics.

The Missing Puzzle Pieces 

While these tools provide a strong starting point, they should not be viewed as exhaustive solutions.  There are still gaps in available compliance tools, such as benchmarking model outputs to human decisions and monitoring real-world impacts. We should invest in emerging standards and collaborate with researchers, lawmakers, and civil rights groups to advance responsible AI.

Organizations should also consider conducting third-party audits and incorporating additional data quality metrics and diversity standards into their AI governance strategies.


With vigilance and moral courage, boardrooms can harness AI side by side with the C-Suite to expand opportunity while safeguarding those vulnerable to harm. Board members play a pivotal role in establishing responsible AI practices. A comprehensive fairness strategy involves not just the technical implementation of fairness tools but also a culture of ongoing vigilance, learning, and adaptation. Given the multifaceted nature of fairness in AI, a multi-stakeholder, multi-disciplinary approach is advised.

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