Description

This curriculum spans the breadth of an enterprise-wide AI ethics initiative, engaging learners in the same granular decision-making required in multi-jurisdictional compliance programs, incident response frameworks, and organizational governance structures across technology, legal, and operational teams.

Module 1: Defining Ethical Boundaries in Data Systems Design

Select whether to implement differential privacy in customer analytics when it reduces model accuracy by 12–15% on high-stakes fraud detection.
Decide whether to log raw user inputs in AI training pipelines when those inputs may contain personally identifiable information (PII) from voice assistants.
Evaluate whether to allow third-party SDKs in mobile apps that enhance functionality but introduce uncontrolled data exfiltration risks.
Choose between open-sourcing a data anonymization tool or keeping it proprietary to maintain competitive advantage in compliance automation.
Assess whether to retain biometric data for facial recognition systems when local regulations permit but ethical guidelines advise against it.
Implement role-based access controls (RBAC) for training data repositories, balancing developer access needs with privacy safeguards.
Determine whether to collect inferred demographic data (e.g., race, gender) from user behavior for personalization, despite lack of explicit consent.
Design data minimization protocols that conflict with data science team demands for expansive feature sets in predictive models.

Module 2: Legal Frameworks and Cross-Jurisdictional Compliance

Map GDPR data subject rights to AI model retraining workflows, including procedures for right to erasure in embedded vector representations.
Configure data residency settings in cloud infrastructure when training models on EU data that must not leave the region.
Implement data transfer impact assessments (DTIAs) for AI training data moving from the EU to the U.S. under the EU-U.S. DPF.
Decide whether to deploy separate model instances per jurisdiction to comply with local AI regulations, increasing operational costs by 40%.
Respond to a data protection authority (DPA) inquiry about automated decision-making in loan approval systems, including model explainability demands.
Classify AI outputs under CCPA as personal information when they reveal behavioral patterns traceable to individuals.
Design audit trails for AI inference decisions to satisfy Brazil’s LGPD requirements for algorithmic transparency.
Handle conflicting data retention mandates: financial regulations require seven-year logs, while privacy policies recommend 90-day deletion.

Module 3: Incident Response and Ethical Disclosure Protocols

Determine whether to classify a model inversion attack that reconstructs training data as a reportable data breach under HIPAA.
Coordinate disclosure timelines when legal counsel advises delay but ethical guidelines recommend immediate user notification.
Activate incident response playbooks for a compromised model API that exposed user prompts containing sensitive health queries.
Decide whether to publicly attribute a breach to a nation-state actor when intelligence is inconclusive but public concern is high.
Engage third-party forensic firms under NDAs while preserving internal investigation independence and chain of custody.
Balance transparency with liability by drafting breach notifications that disclose technical root causes without admitting negligence.
Implement temporary model rollback after discovering training data included improperly sourced medical records.
Manage executive pressure to downplay breach impact when affected datasets include minors’ information.

Module 4: Bias Auditing and Fairness in Production Systems

Choose fairness metrics (e.g., equalized odds, demographic parity) for a hiring recommendation engine when they produce conflicting results.
Decide whether to retrain a credit scoring model when post-deployment audit reveals 18% higher false rejection rates for rural applicants.
Implement bias mitigation techniques like reweighting or adversarial debiasing, knowing they may reduce overall model precision.
Respond to internal whistleblower claims about exclusion of minority groups from training data collection in voice recognition systems.
Design ongoing monitoring for proxy discrimination when protected attributes are removed but correlated features remain.
Handle pushback from product teams when fairness constraints reduce conversion rates in targeted advertising models.
Disclose known bias limitations in model documentation when required by EU AI Act but not by current U.S. regulations.
Integrate fairness testing into CI/CD pipelines, requiring model performance parity checks before deployment.

Module 5: Third-Party Risk and Supply Chain Accountability

Audit data labeling vendors for compliance with ethical labor standards when using gig workers in low-regulation countries.
Assess whether synthetic data providers have introduced bias through over-sampling techniques in training datasets.
Negotiate data usage clauses in contracts with AI API providers to prevent downstream model training on customer inputs.
Terminate a data partnership after discovering a vendor sold aggregated behavioral data to hedge funds without consent.
Implement software bill of materials (SBOM) for AI systems to track open-source model components with known security flaws.
Respond to a breach at a cloud provider that exposed model weights and training metadata from a customer support chatbot.
Verify ethical sourcing claims of training data from a startup offering "consent-cleared" social media archives.
Enforce right-to-audit clauses in vendor agreements when automated systems make high-risk decisions in healthcare triage.

Module 6: Organizational Governance and Ethical Oversight

Structure an AI ethics review board with voting rights over model deployment, including non-technical stakeholders.
Define escalation paths for data scientists who refuse to deploy models they believe cause societal harm.
Implement model cards and data sheets for all production AI systems, despite resistance from engineering teams citing overhead.
Allocate budget for independent ethics audits when internal reviews are deemed insufficient by regulators.
Respond to employee walkouts over military contract work involving facial recognition in surveillance drones.
Design whistleblower protections for staff reporting unethical data practices without fear of retaliation.
Balance innovation velocity with ethical review timelines, where approval processes add 3–6 weeks to deployment cycles.
Document ethical trade-offs in model design decisions for potential regulatory scrutiny or litigation.

Module 7: User Autonomy and Informed Consent Mechanisms

Design just-in-time consent prompts for AI features that adapt in real time, avoiding notification fatigue.
Implement opt-out mechanisms for personalized recommendation engines that rely on sensitive inferred data.
Decide whether to allow users to delete their data from retraining cycles, knowing it may degrade service quality.
Handle implied consent in voice assistants when ambient listening captures private conversations unintentionally.
Create dynamic consent interfaces that explain AI decision logic at levels appropriate for non-technical users.
Respond to user demands for model explainability when proprietary algorithms prevent full disclosure.
Manage consent inheritance when user data is transferred during corporate acquisitions involving AI platforms.
Design data donation programs for medical AI research with granular control over data usage scope and duration.

Module 8: Long-Term Societal Impact and Externalities

Assess environmental costs of large-scale model training against ethical commitments to sustainability.
Monitor downstream misuse of released language models in disinformation campaigns targeting elections.
Decide whether to restrict API access to developers in countries with documented surveillance abuses.
Engage with civil society groups after a facial recognition system is adopted by authoritarian regimes.
Quantify opportunity costs when AI development resources are allocated to high-margin vs. public-interest applications.
Respond to academic studies showing AI hiring tools reinforce gender stereotypes in job recommendations.
Manage intellectual property rights when open-sourced models are used in unethical applications beyond original intent.
Design sunset clauses for AI systems that automatically deactivate when societal norms or risks evolve.

Module 9: Post-Breach Recovery and Ethical Remediation

Design restitution programs for users affected by a data breach involving AI-generated deepfake identities.
Implement model retraining protocols that exclude compromised data without introducing new biases.
Decide whether to offer free credit monitoring when exposed data includes behavioral biometrics.
Revise data governance policies after a breach reveals inadequate oversight of shadow AI models.
Conduct root cause analysis that assigns responsibility across data, model, and deployment layers.
Restore user trust through transparency reports detailing technical failures and corrective actions.
Engage independent ethics auditors to validate remediation efforts before resuming AI services.
Negotiate with regulators on required changes to AI system design as a condition for continued operation.

Data Breaches in The Ethics of Technology - Navigating Moral Dilemmas