Part 5: Fairness Intervention Playbook

1. Introduction

Sprint 2 shifts from diagnosis to treatment. You've identified bias in your Fairness Audit Playbook. Now create a Fairness Intervention Playbook that integrates causal analysis, data transformations, model constraints, and threshold adjustments to fix it.

2. Context

Imagine you're a staff engineer at a mid-sized bank that uses numerous AI systems across multiple domains. Your bank has faced increasing concerns about the fairness of its AI applications, with several incidents raising questions about potential systemic bias. The loan approval system shows troubling gender disparities. Currently, the existing centralized fairness tools or guidelines focus only on assessments, but not interventions. Thus, interventions happen inconsistently, with different teams using their own ad hoc approaches.

You have raised this issue with your VP of Engineering. Together, you decided to develop a new tool - "Fairness Intervention Playbook." Luckily, as a staff engineer, you have accumulated some first hand experience by supporting various engineering teams with their fairness intervention requests and developed several tools that will serve as components of the Fairness Intervention Playbook:

• Your causal fairness toolkit traced how gender affects employment history and income.
• Your pre-processing toolkit fixed biased data representations.
• Your in-processing toolkit embedded fairness into model training.
• Your post-processing toolkit adjusted prediction thresholds.

The Fairness Intervention Playbook should help standardize how fairness interventions are implemented 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 orchestrate causal analysis, data transformations, model constraints, and threshold adjustments into coherent intervention strategies across different ML systems.
• Communicating technical fairness trade-offs to both executives concerned with business impact and engineers implementing the solutions.
• Distilling mathematical fairness concepts into clear, actionable guides that help teams select and sequence appropriate interventions.
• Balancing thorough fairness assessments with practical implementation needs in real-world environments where resources and time have limits.
• Designing validation mechanisms that measure intervention success across multiple dimensions—from fairness improvements to model performance to business outcomes.

4. Requirements

Your Fairness Intervention Playbook must include:

1. Integration of all four components (Causal Fairness Toolkit, Pre-Processing Fairness Toolkit, In-Processing Fairness Toolkit, and Post-processing Fairness Toolkit) 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 Intervention Playbook as if you were presenting to the VP of Engineering. Find 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.