Applied AI Solutions

Responsible AI Practice

We help organizations navigate ethical challenges presented by AI technologies.

Turning Responsible AI Principles into Practice

Our Responsible AI Practice helps organizations move from awareness to action. We combine evaluation, mitigation, and governance frameworks to strengthen systems, reduce risk, and ensure AI works responsibly across every stage of development.

Evaluation. Mitigation. Governance.

These three pillars represent how our Responsible AI Practice helps organizations assess and strengthen their AI systems—from identifying bias and risk, to building safeguards, to establishing lasting governance and oversight models.

Evaluation

Bias, robustness, privacy, and safety testing.

We deliver

Fairness & drift testing
Explainability reports
Security & privacy reviews

Mitigation

Controls, guardrails, and policy-to-practice.

We deliver

Prompt & output filters
Human-in-the-loop workflows
Risk-based deployment gates

Governance

MVG operating model and continuous monitoring.

We deliver

Model inventory & ownership
KPIs / KRIs & dashboards
Audit-ready documentation

Responsible AI in Practice

Explore our core pillars: Risk & Impact Assessment, Governance, Training, and Strategy. Click a pill to expand quick details.

Product / Risk & Impact Assessment

Comprehensive evaluation of AI systems across fairness, explainability, privacy, safety, and organizational risk to inform design, deployment, and oversight.

Decision-ready artifact covering intended use, data lineage, evaluation results, harms & mitigations, and open risks.

Bias/robustness/privacy testing, content safety reviews, counterfactual probes, and stress testing to identify gaps and harm potential.

Workshops and templates that help internal teams maintain assessments and evolve controls as systems change.

Bias Testing, Transparency, Safety & Security

These controls define the Responsible AI evaluation layer for transparency, fairness, and reliability in every deployment.

Bias Testing

Screen for unfair outcomes, performance and representational disparities, and harmful content across sensitive attributes.

Evaluate metrics such as demographic parity and equalized odds; report disparate impact and recommend mitigations.

Evaluate metrics such as counterfactual fairness and similarity-based consistency; report outcome disparities between similar individuals.

Evaluate a model with “what-if” scenarios by altering inputs to detect unintended dependencies or biases.

Privacy

Minimize PII exposure and leakage. Support data subject rights, retention limits, and secure redaction workflows.

Scan datasets and prompts for sensitive entities; map flows where PII could surface in logs or outputs.

Apply masking/redaction strategies (pre-, in-, or post-processing) to reduce re-identification risk while preserving model utility.

Techniques to reduce risk of privacy leakage using membership or attribute inference attacks.

Explainability

Understand and interpret how a model makes its decisions or predictions. It’s about making the model’s internal logic, reasoning, or output transparent.

Measures how much each feature or input contributes to the model’s prediction using SHAP, LIME, or feature importance scores.

Evaluates how changes in inputs affect the model’s outputs using methods such as perturbation sensitivity testing.

Provides visual insights into the model’s internal workings or representations using approaches like attention maps and NLP explanations.

Safety & Security

Model’s ability to perform reliably and safely under noisy, unexpected, or adversarial inputs.

Detecting data issues that may reduce model robustness or reliability.

Ensuring the model maintains correct predictions when faced with maliciously crafted inputs.

Monitoring changes in input data distributions over time to detect performance degradation or unexpected behavior.

Transparency

The practice of making AI models open and understandable to stakeholders by documenting their capabilities, limitations, intended use, and potential risks.

Structured documentation of model performance, purpose, and limitations.

Detailed documentation of dataset composition, collection process, and potential biases.

Systematic evaluation and reporting of the AI development and deployment pipeline to ensure accountability and traceability.

Diagram of comprehensive Responsible AI engagements

Proof in Practice

Verizon Communications — Responsible AI in the Wild

See how our Responsible AI team partnered with Verizon to operationalize AI governance — from evaluation and mitigation to audit-ready documentation.

Bias, robustness, and privacy evaluation mapped to business risk
Human-in-the-loop and prompt control guardrails
Minimal Viable Governance (MVG) model and documentation

Read the Verizon Case Study (PDF) Explore All Resources

Ready to Deploy AI Responsibly?

The time to ensure a comprehensive RAI implementation is now. We help organizations fully harness the power of Responsible AI, meet emerging regulatory obligations, enhance technology by integrating ethics into the innovation process and more.

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Responsible AI Education Programs

Responsible AI Executive Education

This cutting-edge course provides an actionable blueprint for leaders in driving their organizations' RAI strategy and in taking their businesses to the next level in the digital world. The courses are particularly fitting for those engaging with company strategy and digital transformation, and those organizing digital innovation initiatives within their organizations. Attendees will learn how to:

Transform organizations with a comprehensive RAI strategy

Build a culture to foster responsible innovation

Create efficient, practical, and seamless operationalization of ethical decision-making

Upskill & empower employees for better implementation of RAI strategies

Differentiate organizations and create customer value

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Ethics and Governance in the Age of Generative AI

This course is designed for individuals eager to deepen their knowledge of generative AI and best practices for responsible, ethical incorporation of genAI tools. Gain an understanding of the ethical and technical aspects shaping AI and genAI model development and deployment. Course modules include:

Ethics of Emerging Technologies

Generative AI: An Emerging Challenge

How Does Generative AI Work?

RAI & Generative AI Bias & Fairness Metrics

RAI & Generative AI: Workflows

RAI Strategy & Governance

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Responsible AI Readiness Quiz

Take our 2-minute quiz to assess your company’s AI governance maturity—from bias mitigation and privacy safeguards to ethics, oversight, and transparency.
Get a snapshot of where you stand and what steps to take next.

Take the Quiz