Assess an AI system or initiative
Understand risks related to fairness, privacy, explainability, safety, robustness, intended use, and organizational impact before or after deployment.
Explore risk & impact assessment
Our approach
From principles to operating practice.
Responsible AI requires more than principles or policies. We help organizations identify risk, put practical safeguards in place, establish accountability, and continue evaluating AI as systems and uses evolve.
Evaluate
Identify technical, organizational, and human risks across AI systems, use cases, data, and workflows.
Mitigate
Design safeguards, controls, human review, and other interventions appropriate to the risks identified.
Govern
Establish ownership, decision rights, documentation, review processes, and organizational accountability.
Monitor
Continue assessing performance, risk, drift, incidents, and changing requirements after deployment.
Responsible AI in practice
What does it take to move from principles to practice?
Hear Cansu Canca, Director of Responsible AI Practice, discuss how organizations can approach Responsible AI in ways that work in real-world development and deployment.
Cansu Canca
Director of Responsible AI Practice
Responsible AI services
Support for the decisions that matter.
Our multidisciplinary teams work with organizations at different stages of Responsible AI maturity, from evaluating individual AI systems to designing governance structures and organization-wide strategies.
Responsible AI Services Catalog
Download the catalog
Compare every engagement side by side and see where most organizations start.
01 / ASSESS
Risk & Impact Assessment
Evaluate an AI system, product, project, or use case to understand potential risks and determine what should be addressed before or after deployment.
RAI Product Scorecard
A decision-ready artifact covering intended use, data lineage, evaluation results, potential harms, mitigations, and unresolved risks.
Deep-Dive Analysis
Technical evaluation across fairness, robustness, privacy, explainability, safety, security, and other areas relevant to the system.
Knowledge Transfer
Workshops, tools, and templates that help internal teams understand findings and maintain responsible practices as systems evolve.
What you leave with
A clearer picture of where risk exists, what requires attention, and what evidence or safeguards are needed to make a more informed deployment decision.
Technical evaluation capabilities
What we can evaluate.
The right evaluation depends on the AI system, its intended use, who may be affected, and the risks involved. Our teams draw from a range of technical methods to examine the areas most relevant to each engagement.
01 Fairness & Bias Examine whether AI systems create or reinforce unfair differences in performance, treatment, or outcomes.
Group fairness
Evaluate performance and outcome disparities across relevant groups using appropriate fairness measures.
Individual fairness
Examine whether similarly situated individuals receive meaningfully different outcomes.
Counterfactual analysis
Test “what-if” scenarios by changing inputs to identify unintended dependencies or disparities.
02 Privacy Identify potential exposure, leakage, or inappropriate use of sensitive information.
PII detection
Examine datasets, prompts, workflows, and outputs for sensitive or personally identifiable information.
Redaction
Assess masking and redaction approaches designed to reduce exposure while maintaining appropriate utility.
Model & output hardening
Evaluate approaches for reducing risks such as privacy leakage and unintended information exposure.
03 Explainability Investigate how inputs influence predictions, decisions, recommendations, or generated outputs.
Input importance
Examine how features or inputs contribute to a model's predictions or outputs.
Model sensitivity
Evaluate how changes to inputs affect model behavior and resulting outputs.
Model interpretation
Apply appropriate methods to better understand model behavior, representations, and decision patterns.
04 Safety & Robustness Test whether systems continue to behave reliably under changing, unexpected, noisy, or adversarial conditions.
Dataset vulnerabilities
Identify data issues that may reduce model reliability, performance, or robustness.
Adversarial robustness
Examine system behavior when confronted with intentionally challenging or manipulated inputs.
Drift detection
Monitor changes in data or system behavior that may contribute to degraded or unexpected performance.
05 Transparency Create documentation and traceability around intended use, limitations, data, performance, and system decisions.
Model cards
Document model purpose, intended use, performance, limitations, and other information stakeholders need.
Dataset documentation
Document dataset composition, collection processes, limitations, and potential sources of bias.
Pipeline review & reporting
Examine the development and deployment pipeline to strengthen accountability, traceability, and reporting.
02 / GOVERN
AI Governance
Move from high-level Responsible AI principles to a practical operating model with clear ownership, review processes, controls, documentation, and oversight.
Governance Strategy & Structure
Define organizational values, roles, decision rights, review bodies, ownership, escalation paths, and governance structures appropriate to AI risk.
Tools, Guidelines & Workflow Integration
Turn Responsible AI principles into usable guidelines, controls, review processes, safeguards, and workflows that teams can apply in practice.
Scorecards, Evidence & Accountability
Create evaluation frameworks, inventories, documentation, evidence requirements, and reporting that help leadership understand and oversee Responsible AI performance.
Minimal Viable Governance
Build enough governance to create meaningful accountability and oversight without creating unnecessary process that prevents teams from using AI effectively.
03 / Responsible AI Advisory
Independent expertise when you need it.
Bring independent Responsible AI expertise into the decisions your organization is making as AI systems, risks, regulations, and organizational needs evolve.
Meet our experts
The people behind our Responsible AI Practice.
Our team brings together expertise in AI ethics, technical evaluation, governance, and the real-world implementation of Responsible AI.
Cansu Canca
Director of Responsible AI Practice Read Cansu's bio →
Thulasi Tholeti
Research Scientist Read Thulasi's bio →
Meredith McFadden
Research Scientist Read Meredith's bio →
Daniel Tigard
AI Ethicist Read Daniel's bio →
Backed by Northeastern's broader AI expertise.
Engagements can draw on researchers and practitioners across AI, data science, cybersecurity, law, policy, ethics, and domain-specific disciplines based on the needs of the work.
Continue exploring
Responsible AI resources.
Explore examples, education, and perspectives from Northeastern's Responsible AI work.
Case Study
Read the case study →
Responsible AI at Verizon
See how Northeastern worked with Verizon to connect technical evaluation, risk mitigation, governance, and documentation.
Education
View the catalog →
2026 Responsible AI Education Catalog
Explore Responsible AI education designed for leaders, governance teams, technical teams, and employees.
Services
Download the catalog →
Responsible AI Services Catalog
Governance, assessment, advisory, and training services, plus the Navigator Maturity Assessment as a starting point.
Start a conversation
Put Responsible AI into practice.
Tell us what you're building, evaluating, governing, or preparing for. We can help determine the right starting point and what your organization should address next.
What are you working through?
An AI initiative, governance challenge, risk assessment, workforce need, or Responsible AI strategy is enough to start the conversation.