Build Fair AI with Confidence
The Svrus Fairness Intervention Playbook helps turn fairness audit into clear, repeatable action — with less effort and more confidence.
Actionable frameworks for addressing bias across real-world AI systems.
Actionable frameworks for addressing bias across real-world AI systems.
Actionable frameworks for addressing bias across real-world AI systems.
The Fairness Intervention Playbook helps teams go beyond audits. It provides a repeatable, tool-driven workflow to detect, diagnose, and fix algorithmic bias across the AI lifecycle.
Identify Sources of Bias using Causal Analysis
Transform Data to Mitigate Bias
Embed Fairness Constraints in Model Training
Adjust Model Outputs through Post-processing Techniques
Monitor Performance and Iterate
From credit decisions to hiring algorithms, biased AI systems can reinforce existing inequalities, erode trust, and trigger regulatory scrutiny. Most organizations assess fairness too late — and intervene too inconsistently.
We created the Fairness Intervention Playbook to change that.
Contextual insights from data to guide smarter, impact-driven investments. We help you identify where to act, why it matters, and what’s at stake — responsibly.
Contextual insights from data to guide smarter, impact-driven investments. We help you identify where to act, why it matters, and what’s at stake — responsibly.
Contextual insights from data to guide smarter, impact-driven investments. We help you identify where to act, why it matters, and what’s at stake — responsibly.
Strategic partnerships between institutions, biotech companies, and investors.
Strategic partnerships between institutions, biotech companies, and investors.
Strategic partnerships between institutions, biotech companies, and investors.
Advice on compliance, regulatory processes, and market entry strategies
Advice on compliance, regulatory processes, and market entry strategies
Advice on compliance, regulatory processes, and market entry strategies
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Features & Benefits
Features & Benefits
Features & Benefits
01
Causal Fairness Toolkit
Trace the true origins of bias using structural causal models, DAGs, and counterfactual reasoning. This toolkit helps teams identify whether observed disparities arise from legitimate factors or hidden unfair pathways — allowing for more targeted, defensible interventions that go beyond surface-level correlations.
01
Causal Fairness Toolkit
Trace the true origins of bias using structural causal models, DAGs, and counterfactual reasoning. This toolkit helps teams identify whether observed disparities arise from legitimate factors or hidden unfair pathways — allowing for more targeted, defensible interventions that go beyond surface-level correlations.
01
Causal Fairness Toolkit
Trace the true origins of bias using structural causal models, DAGs, and counterfactual reasoning. This toolkit helps teams identify whether observed disparities arise from legitimate factors or hidden unfair pathways — allowing for more targeted, defensible interventions that go beyond surface-level correlations.
02
Preprocessing Toolkit
Transform your data before it enters the model. This suite includes reweighting, distribution alignment, and counterfactual data augmentation — making biased patterns less likely to influence outcomes. Ideal for early-stage fixes that are model-agnostic and deployment-safe.
02
Preprocessing Toolkit
Transform your data before it enters the model. This suite includes reweighting, distribution alignment, and counterfactual data augmentation — making biased patterns less likely to influence outcomes. Ideal for early-stage fixes that are model-agnostic and deployment-safe.
02
Preprocessing Toolkit
Transform your data before it enters the model. This suite includes reweighting, distribution alignment, and counterfactual data augmentation — making biased patterns less likely to influence outcomes. Ideal for early-stage fixes that are model-agnostic and deployment-safe.
03
In-Processing Toolkit
Embed fairness directly into model training. Techniques like adversarial debiasing, fairness-aware regularization, and constrained optimization ensure that your models learn to balance performance with equity — right from the start. Perfect for teams retraining or building new systems.Embed fairness directly into model training. Techniques like adversarial debiasing, fairness-aware regularization, and constrained optimization ensure that your models learn to balance performance with equity — right from the start. Perfect for teams retraining or building new systems.
03
In-Processing Toolkit
Embed fairness directly into model training. Techniques like adversarial debiasing, fairness-aware regularization, and constrained optimization ensure that your models learn to balance performance with equity — right from the start. Perfect for teams retraining or building new systems.Embed fairness directly into model training. Techniques like adversarial debiasing, fairness-aware regularization, and constrained optimization ensure that your models learn to balance performance with equity — right from the start. Perfect for teams retraining or building new systems.
03
In-Processing Toolkit
Embed fairness directly into model training. Techniques like adversarial debiasing, fairness-aware regularization, and constrained optimization ensure that your models learn to balance performance with equity — right from the start. Perfect for teams retraining or building new systems.Embed fairness directly into model training. Techniques like adversarial debiasing, fairness-aware regularization, and constrained optimization ensure that your models learn to balance performance with equity — right from the start. Perfect for teams retraining or building new systems.
04
Post-Processing Toolkit
When retraining isn’t feasible, use post-processing tools to close fairness gaps. Apply group-specific thresholding, score transformations, and calibration adjustments to achieve regulatory compliance and parity — without touching the model internals.
04
Post-Processing Toolkit
When retraining isn’t feasible, use post-processing tools to close fairness gaps. Apply group-specific thresholding, score transformations, and calibration adjustments to achieve regulatory compliance and parity — without touching the model internals.
04
Post-Processing Toolkit
When retraining isn’t feasible, use post-processing tools to close fairness gaps. Apply group-specific thresholding, score transformations, and calibration adjustments to achieve regulatory compliance and parity — without touching the model internals.
05
Human-in-the-Loop Protocols
Structure expert oversight into every stage of your fairness workflow. From causal assumption reviews to calibration overrides and rejection handling, this protocol ensures critical human judgment complements automation — boosting accountability, transparency, and trust.
05
Human-in-the-Loop Protocols
Structure expert oversight into every stage of your fairness workflow. From causal assumption reviews to calibration overrides and rejection handling, this protocol ensures critical human judgment complements automation — boosting accountability, transparency, and trust.
05
Human-in-the-Loop Protocols
Structure expert oversight into every stage of your fairness workflow. From causal assumption reviews to calibration overrides and rejection handling, this protocol ensures critical human judgment complements automation — boosting accountability, transparency, and trust.
Strategic Partnership for Fair AI You Can Trust
Strategic Partnership for Fair AI You Can Trust
Strategic Partnership for Fair AI You Can Trust
We recognize that building fair AI is not just a technical ambition—it’s a structural commitment. One where even the best-intentioned teams struggle to move beyond audits into lasting interventions.
The Fairness Intervention Playbook is more than a framework. It’s a bridge between intention and implementation, designed to equip organizations with the tools, evidence, and human-in-the-loop protocols needed to act decisively.
We believe fairness isn’t an add-on—it’s foundational to resilient, future-ready AI.
We believe fairness isn’t an add-on—it’s foundational to resilient, future-ready AI.
We believe fairness isn’t an add-on—it’s foundational to resilient, future-ready AI.
We’re committed to walking alongside teams—not ahead of them—with grounded tools and expert support.
We’re committed to walking alongside teams—not ahead of them—with grounded tools and expert support.
We’re committed to walking alongside teams—not ahead of them—with grounded tools and expert support.
Our work is guided by a simple principle: equity that endures must be built into the system, not around it.
Our work is guided by a simple principle: equity that endures must be built into the system, not around it.
Our work is guided by a simple principle: equity that endures must be built into the system, not around it.
