Caba

𝗖𝗔𝗕𝗔 Curriculum — 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗕𝗶𝗮𝘀 & 𝗔𝗜 𝗔𝘂𝗱𝗶𝘁𝗶𝗻𝗴

25+ Bias, Fairness & Governance Techniques

CABA is aimed at detecting, assessing, and mitigating fairness risks in AI systems, explicitly framing fairness as a socio-technical problem rather than a purely algorithmic one. Bias is treated as an emergent property of data generation, modeling choices, deployment context, and institutional decision-making—not as a defect solvable by a single metric or technique.

Core Perspective

Fairness is shaped by societal context, historical data, proxy attributes, model objectives, and downstream use, not merely by algorithmic constraints or post-hoc corrections. As a result, fairness evaluation requires both quantitative tooling and human judgment informed by domain, regulation, and impact.

Toolkits and Capabilities (Fairlearn)

The CABA curriculum incorporates the Fairlearn ecosystem, including:

Fairness Metrics Toolkit

Supports group-based evaluation using metrics such as demographic parity, equalized odds, equal opportunity, selection rate, false-positive/negative rates, and predictive parity—computed across protected and proxy groups.

Visualization & Diagnostics Toolkit

Interactive dashboards and plots to surface group-wise disparities, trade-offs between accuracy and fairness, and sensitivity to threshold changes

Mitigation Algorithms Toolkit

Implements mitigation strategies across:

Pre-processing (reweighting, sampling adjustments)

In-processing (constraint-based optimization, reduction approaches)

Post-processing (threshold optimization, output adjustment)

Workflow Integration

Designed to integrate with standard Python ML pipelines (scikit-learn compatible), enabling fairness assessment without disrupting existing model development and evaluation workflows.

Guidance and Governance Emphasis

Documentation and practice emphasize:

Selecting fairness definitions appropriate to the use case, rather than defaulting to generic metrics.

Understanding trade-offs between competing fairness criteria and model performance.

Explicitly documenting residual risk, mitigation limits, and decision rationales for audit and regulatory review.

Illustrative Use Case

Credit-card loan decisioning is used as a canonical example:

Demonstrates how prediction errors and approval rates can differ across protected attributes (e.g., sex).

Shows how disparities are quantified using multiple fairness metrics.

Explores mitigation strategies that adjust outcomes while explicitly acknowledging business constraints, risk appetite, and regulatory obligations.

Reinforces that mitigation does not eliminate responsibility—trade-offs must be disclosed and justified. 

Bottom Line

It provides a practical audit framework that combines metrics, diagnostics, mitigation tools, and disciplined judgment. This makes it suitable for professionals who must measure disparities, evaluate interventions, and reason about fairness within real-world, regulated AI systems—not merely discuss ethics in the abstract.