Part 5: Fairness Audit Framework

1. Introduction

In Sprint 1, you learned about and built components for historical context assessment, fairness definitions, bias source identification, and fairness metrics. Now it's time to integrate these components into a cohesive Fairness Audit Playbook that systematically evaluates AI systems for bias and fairness issues. This practical implementation will test your ability to transform theoretical concepts into an actionable framework applicable in real-world organizational contexts.

2. Context

Imagine you are a staff engineer at a technology company that uses numerous AI systems across multiple domains. Your company has faced increasing concerns about the fairness of its AI applications, with several incidents raising questions about potential systemic bias. Currently, there are no centralized fairness tools or guidelines. Thus, fairness assessments and interventions happen inconsistently, with different teams using their own ad hoc approaches.

You have raised this issue with your VP of Engineering. Together, you determined that understanding how fair the models are should be the first priority. To address this, you decided to develop a new tool - "Fairness Audit Playbook." Luckily, as a staff engineer, you have accumulated some first hand experience by supporting various engineering teams with their fairness requests and developed several tools that will serve as components of the Fairness Audit Playbook.

The Fairness Audit Playbook should help standardize how fairness is evaluated by engineering teams across existing and future AI systems built by or used by the company (e.g., in the case of 3rd party AI APIs). In most cases, teams should be able to use your playbook without external support, only requiring fairness experts for the most complex cases.

3. Objectives

By completing this project, you will practice:

• Developing adaptable workflows that preserve methodological rigor while accommodating domain-specific requirements.
• Communicating complex technical proposals to both leadership and technical audiences.
• Distilling complex technical changes into actionable implementation guides.
• Balancing comprehensiveness with ease of adoption and usability in an organizational context.
• Designing validation approaches to verify that your proposals achieve the desired outcomes.

4. Requirements

Your Fairness Audit Playbook must include:

1. Integration of all four components (Historical Context Assessment, Fairness Definition Selection, Bias Source Identification, and Comprehensive Metrics) with clear workflows showing how outputs from each component feed into subsequent ones.
2. An implementation guide explaining how to use your playbook, with commentary on key decision points, supporting evidence, and identified risks.
3. A case study demonstrating the application of your playbook to a typical fairness problem.
4. A validation framework providing guidance on how implementing teams can verify the effectiveness of their audit process.
5. Explicit consideration of intersectional fairness in each component of the playbook.
6. Adaptability guidelines for using the playbook across different domains (healthcare, finance, etc.) and problem types (classification, regression, etc.).
7. Implementation guidelines addressing practical organizational considerations like time requirements, necessary expertise, and integration with existing development processes.
8. Insights on how your playbook could be improved.

There are no rigid format requirements for this Sprint Project. Choose the structure that you believe best supports the intended outcomes. If you're uncertain where to start, try the following default layout:

• One Markdown file for each Project Component
• One Markdown file for the Case Study
• One Markdown file that serves as the introduction and entry point to the other files.

5. Evaluation Criteria

Your project will be evaluated on:

1. Integration coherence: How effectively you connect historical context, fairness definitions, bias sources, and metrics into a logical workflow with clear information flows between components.
2. Practicality and usability: How realistically your framework can be adopted within organizations and integrated with existing engineering and AI development processes.
3. Documentation quality: How clearly your guides and templates facilitate consistent fairness assessments and establish accountability.
4. Scientific and technical soundness: The degree to which your framework integrates established scientific consensus on fairness assessment methodologies and applies rigorous technical principles to ensure validity and reliability.
5. Communication effectiveness: The degree to which you translate complex technical concepts into business-relevant terms that resonate with leadership and support informed decision-making through clear, compelling explanations.

6. Project Review

During your project review, present the Fairness Audit Playbook as if you were presenting to the VP of Engineering, striking a balance between foundational fairness concepts and concrete implementation details. Your presentation should cover:

• Problem statement: What challenge you're solving and how the playbook addresses it at a high level.
• Playbook overview: The main components and how they interact.
• Practical demonstration: A case study showing the playbook in action.
• Implementation considerations: Required resources and integration with existing workflows.
• Key insights: Fairness findings uncovered during playbook development.

Your VP has a strong technical background but is particularly interested in practical implementation and business impact. Be prepared to discuss how your playbook balances scientific rigor with usability, how it scales across different AI applications, and how it creates accountability for fairness outcomes.