Intermediate

Ethical Review Process

Design structured, efficient ethical review workflows that evaluate AI projects for potential risks and ethical concerns while maintaining development velocity.

Review Process Steps

  1. Risk-Based Intake Triage

    Use a standardized intake questionnaire to classify AI projects by risk level. Low-risk projects proceed with self-certification. Medium-risk projects get expedited review. High-risk projects receive full committee review.

  2. Ethics Impact Assessment

    Project teams complete a detailed ethics impact assessment covering data usage, affected populations, potential harms, mitigation strategies, and human oversight mechanisms.

  3. Committee Review

    The committee evaluates the assessment, asks clarifying questions, and deliberates using established ethical frameworks. Members bring their diverse expertise to identify blind spots.

  4. Decision and Conditions

    The committee issues one of four outcomes: approved, approved with conditions, returned for revision, or rejected. Most reviews result in approval with specific conditions or mitigations.

  5. Post-Deployment Monitoring

    Approved projects include monitoring requirements. The committee reviews post-deployment data on bias metrics, user complaints, and outcome fairness at defined intervals.

Efficiency Tip: Target a 2-week turnaround for standard reviews. If reviews take too long, teams will find ways to avoid them. Build efficiency through clear templates, pre-read materials, and structured discussion formats.

Assessment Criteria

CriteriaKey QuestionsEvidence Required
FairnessDoes the system treat all groups equitably?Bias testing results, demographic analysis
TransparencyCan affected people understand how decisions are made?Explainability documentation, user disclosures
PrivacyIs personal data minimized and protected?Data inventory, privacy impact assessment
SafetyWhat are the failure modes and safeguards?Testing results, fallback procedures, human oversight
AccountabilityWho is responsible for outcomes?RACI matrix, escalation procedures

Making Reviews Effective

Standardized Templates

Provide clear, structured templates for ethics impact assessments. Templates guide project teams to consider the right questions and provide the right evidence.

Pre-Review Consultation

Offer informal consultations before formal review. Teams can address potential issues early, making the formal review faster and more likely to succeed.

Decision Documentation

Document every review decision with rationale, conditions, and follow-up requirements. This creates precedent that guides future reviews and ensures consistency.

Appeals Process

Provide a formal appeals mechanism for teams that disagree with committee decisions. Fair processes build trust even when decisions are unfavorable.

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Looking Ahead: In the next lesson, we will develop comprehensive AI ethics guidelines that provide clear, actionable guidance for development teams building AI systems.