Catalog
concept#Artificial Intelligence#Governance#Data#Security

Responsible AI

Principles for the safe, fair and responsible design of AI systems.

Responsible AI provides principles and practices to ensure AI and machine learning systems are fair, transparent, accountable, and aligned with societal values.
Emerging
High

Classification

  • High
  • Organizational
  • Organizational
  • Intermediate

Technical context

MLOps platforms (e.g. Kubeflow, MLflow)Data catalogs and data governance toolsIncident and compliance management systems

Principles & goals

Transparency: Document models and decisions for traceability.Fairness: Detect and mitigate discrimination.Accountability: Define responsibilities and escalation paths.
Discovery
Enterprise, Domain

Use cases & scenarios

Compromises

  • Incomplete governance leads to inconsistent decisions.
  • False sense of security due to superficial explanations.
  • Data errors or overfitting can amplify harms.
  • Involve legal and ethics expertise early.
  • Integrate automated tests and monitoring into CI/CD.
  • Consider user perspective and impact analyses in design decisions.

I/O & resources

  • Clean, documented training and test datasets
  • Model artifacts and version history
  • Organizational policies and compliance requirements
  • Governance policies and role definitions
  • Audit and assessment reports
  • Technical mitigation measures for bias

Description

Responsible AI provides principles and practices to ensure AI and machine learning systems are fair, transparent, accountable, and aligned with societal values. It guides governance, risk management, and lifecycle practices across development and deployment. Organizations use it to reduce harms and increase trust in AI-driven products.

✔

  • Reduced legal and reputational risks through compliance.
  • Increased user trust and acceptance of AI products.
  • Improved model quality via structured reviews and monitoring.

✖

  • Not all sources of bias can be detected fully automatically.
  • Implementation is time- and resource-intensive.
  • Conflicts between fairness goals and business requirements may arise.

  • Fairness index

    Metric for group-based performance disparities of the model.

  • Explainability score

    Quantitative assessment of the traceability of model decisions.

  • Number of reported incidents

    Number of reported problems or harms attributed to AI functions.

EU Ethics Guidelines for Trustworthy AI

A framework defining transparency, fairness and accountability requirements for AI systems in the EU.

Fairlearn for fairness analysis

Open-source toolkit for assessing and mitigating bias in ML models, used in product projects.

Internal model governance at a financial provider

Example of a board that evaluates, approves and schedules regular audits for models.

1

Conduct initial risk assessment and stakeholder workshop.

2

Define governance roles, policies and review processes.

3

Implement technical controls (monitoring, explainability, fairness checks).

4

Establish regular audits, training and continuous improvement.

⚠️ Technical debt & bottlenecks

  • Lack of version control and metadata for models.
  • No standardized audit and reporting mechanisms.
  • Incomplete test coverage for fairness and robustness tests.
Poor data qualitySkill gaps in ethics and ML engineeringScalability of audits and tests
  • Providing cosmetic explanations that offer no real traceability.
  • Running fairness checks once before release and then disabling them.
  • Using governance tools without assigning responsibilities.
  • Focusing too narrowly on single metrics instead of systemic assessment.
  • Overreliance on automated bias-detection tools.
  • Insufficient stakeholder communication regarding measures.
Data science and ML modelingEthics, legal and compliance expertiseSoftware engineering and MLOps skills
Regulatory requirements and complianceUser and stakeholder trustData quality and integrity
  • • Data protection regulations (e.g. GDPR)
  • • Limited resources for additional review processes
  • • Unclear or changing regulatory requirements