This curriculum spans the breadth of ethical decision-making in technology through detailed, scenario-driven modules comparable to those encountered in multi-workshop organizational ethics programs, addressing real-world trade-offs in algorithmic fairness, data governance, automation impacts, and stakeholder conflict resolution.
Module 1: Defining Ethical Boundaries in Technology Development
- Selecting whether to implement facial recognition in a public safety application, weighing accuracy disparities across demographic groups against operational urgency.
- Deciding whether to collect granular user behavior data during beta testing when informed consent mechanisms are incomplete.
- Choosing between open-sourcing a privacy-preserving algorithm or retaining it as a competitive advantage despite public interest in transparency.
- Implementing age verification systems in social platforms and determining acceptable false positive rates that may restrict access for legitimate minors.
- Establishing whether to proceed with deploying an AI-driven hiring tool known to reflect historical hiring biases in certain job categories.
- Integrating third-party SDKs with opaque data practices into a mobile application when contractual obligations limit audit rights.
Module 2: Data Governance and Privacy by Design
- Designing data retention policies that balance regulatory compliance with business analytics needs, particularly in multinational deployments.
- Implementing differential privacy in customer analytics when performance degradation affects decision-making accuracy.
- Choosing whether to anonymize or pseudonymize user data in internal reporting systems when re-identification risks remain high.
- Enforcing data minimization principles during product development when stakeholders demand expansive data collection for future use cases.
- Responding to legitimate law enforcement data requests in jurisdictions with weak human rights protections while honoring user trust.
- Configuring consent management platforms to handle granular opt-in/opt-out options without degrading user experience or tracking reliability.
Module 4: Algorithmic Accountability and Bias Mitigation
- Selecting fairness metrics (e.g., demographic parity vs. equalized odds) for credit scoring models when trade-offs between groups are unavoidable.
- Conducting bias audits on legacy systems when training data is no longer available or poorly documented.
- Deciding whether to override algorithmic recommendations in high-stakes domains like healthcare triage when human oversight contradicts model output.
- Implementing real-time bias monitoring in recommendation engines when performance overhead impacts system scalability.
- Disclosing known model limitations to clients when contractual terms discourage transparency about algorithmic shortcomings.
- Allocating engineering resources to retrain models for underrepresented user segments when ROI projections are unfavorable.
Module 5: Ethical Implications of Automation and Job Displacement
- Designing workforce transition programs when deploying robotic process automation that eliminates 30% of back-office roles.
- Choosing whether to disclose automation roadmaps to employees during labor union negotiations involving productivity benchmarks.
- Integrating AI co-pilots into customer service workflows while measuring impacts on employee skill atrophy and job satisfaction.
- Setting performance thresholds for automated systems that trigger human escalation, balancing cost savings with service quality.
- Responding to community backlash when a manufacturing plant replaces human inspectors with computer vision systems after safety incidents.
- Evaluating vendor claims about “augmentation not replacement” when deployment plans include headcount reduction targets.
Module 6: Dual-Use Technologies and Responsible Innovation
Module 7: Stakeholder Engagement and Ethical Decision Frameworks
- Convening external ethics advisory boards when internal review processes lack diversity in lived experience or technical expertise.
- Choosing between Delphi methods and consensus workshops to resolve disagreements among executives on AI ethics guidelines.
- Documenting dissenting opinions in ethics committee decisions when majority rulings approve controversial product features.
- Integrating community impact assessments into product roadmaps when affected populations are not direct customers.
- Responding to employee walkouts over participation in government surveillance contracts when financial penalties for withdrawal are significant.
- Structuring cross-functional escalation paths for engineers who identify ethical risks not addressed in standard risk registers.
Module 3: Transparency, Explainability, and User Agency
- Designing model explanation interfaces for non-technical users that avoid oversimplification while remaining actionable.
- Implementing right-to-explanation workflows under GDPR when model complexity prevents human-interpretable justifications.
- Choosing whether to expose confidence scores to end users when low-confidence predictions may erode trust in high-accuracy systems.
- Developing opt-out mechanisms for algorithmic decision-making in loan applications when manual review capacity is limited.
- Disclosing training data sources in model cards when doing so risks exposing proprietary data partnerships.
- Managing user expectations about system autonomy when marketing materials emphasize “full automation” but fallback protocols require human intervention.
