Upon successful completion of this program, participants will be able to:

• Identify and analyze core ethical issues (e.g., bias, fairness, deepfakes) across various digital technologies.
• Apply formal ethical frameworks (e.g., deontology, utilitarianism, virtue ethics) to real-world tech dilemmas.
• Design AI systems with principles of transparency, explainability (XAI), and auditability.
• Implement privacy-by-design and data minimization techniques in development projects.
• Understand the impact of digital technology on social equity, democratic processes, and human autonomy.
• Develop organizational governance structures, like AI ethics review boards, for oversight.
• Navigate and comply with evolving global digital and AI regulations and guidelines.
• Foster a culture of ethical awareness and responsible innovation within their teams.

• Software Developers and Data Scientists
• Digital Product Managers and Owners
• Executives responsible for technology strategy
• Compliance, Risk, and Legal Professionals
• UX/UI Designers and Researchers
• Academics and Policy Analysts focused on technology
• Journalists and Public Relations Specialists

• **Scenarios:** Resolving a conflict where a development deadline requires compromising on explainability features.
• **Case Studies:** In-depth examination of the Cambridge Analytica scandal and the design flaws in predictive policing algorithms.
• **Group Activities:** Applying a decision-making matrix (e.g., Markkula Center framework) to a complex AI dilemma.
• **Individual Exercises:** Auditing a simulated dataset for potential sources of bias and proposing mitigation steps.
• **Mini-Case Studies:** Rapid review of a company's public AI ethics statement and critiquing its substance.
• **Syndicate Discussions:** Debating who should be legally responsible when an autonomous system causes harm.
• **Role-Play:** Simulating an ethical review board meeting reviewing a new AI product proposal.

Unit 1: Foundations of Digital Ethics

• Introduction to classical ethical theories (Utilitarianism, Deontology).
• Defining algorithmic bias, discrimination, and disparate impact.
• The concept of accountability and responsibility in autonomous systems.
• The problem of opacity and the need for explainability (XAI).
• The relationship between digital ethics and social justice.
• Examining the "moving target" of digital ethical standards.

Unit 2: Bias, Fairness, and Data Ethics

• Sources of algorithmic bias (data, feature selection, model training).
• Metrics for measuring fairness (e.g., demographic parity, equalized odds).
• Techniques for bias detection and mitigation in machine learning pipelines.
• Ethical standards for data collection, labeling, and governance.
• The trade-off between privacy, accuracy, and fairness.
• Developing inclusive design principles for digital products.

Unit 3: Transparency, Explainability, and Trust

• The ethical imperative for transparency in automated decision-making.
• Introduction to Explainable AI (XAI) methods and tools (e.g., LIME, SHAP).
• Determining the appropriate level of explanation for different stakeholders.
• Designing user interfaces that convey trust and system uncertainty.
• Audit trails and logging for post-hoc analysis and accountability.
• Communicating AI capabilities and limitations honestly.

Unit 4: Societal Impact and Autonomy

• The ethical implications of deepfakes and generative content.
• Surveillance capitalism and the ethics of digital attention economies.
• Digital wellbeing, addiction, and the responsibility of platform designers.
• The impact of automation and AI on the future of work and labor rights.
• Digital divide, access equity, and technological justice.
• The ethics of persuasive technologies and manipulation.

Unit 5: Operationalizing Ethical Technology

• Integrating ethical checklists and review points into the development lifecycle.
• Establishing an AI Ethics Review Board and standard operating procedures.
• The role of the Chief Ethics Officer and cross-functional teams.
• Reviewing and interpreting major regulatory frameworks (e.g., GDPR, EU AI Act principles).
• Developing a crisis management plan for ethical failures.
• Ethical procurement guidelines for third-party digital solutions.