Data and AI Ethics Foundations

Explore the foundational concepts of law, ethics, and data ethics, distinguish law from ethics, and engage with MIT Moral Machine dilemmas to evaluate data practices.

Learn data sources and classifications, data structures such as structured, unstructured, and semi-structured, the data lifecycle and practices, and how data enables AI and industry four technologies.

Explore data classification across confidentiality levels (restricted, confidential, internal only, public), data sensitivity (high, medium, low), and the open, shared, and closed data spectrum with practical examples.

Trace the data life cycle from creation to deletion, including storage, archival, and processing, with a focus on data quality, storage decisions, and ethical use for AI.

Delve into the data life cycle—creation, storage, use, archival, deletion—and how data use informs analytics, AI, research, operations, and decision making, including data sharing and marketplaces.

Data primer shows how data enables ai and industry 4.0, outlines ai branches and technologies like iot, cloud computing, ar/vr, robotics, and highlights data concepts, lifecycle, and analytics use cases.

Explore how data ethics drives positive social outcomes through case studies on financial inclusion, environmental reporting, and healthcare, while examining risks of unethical data practices.

Introduce data ethics principles and an ethical framework to evaluate data practices and automated decision making, ensuring accuracy, reliability, fairness, accountability, and sound judgments.

Explore the three data ethics types: guiding, qualitative, and governing principles, and learn how fairness, privacy, safety, transparency, explainability, and accountability shape responsible data practices and governance.

Apply data ethics principles to evaluate data practices across the data lifecycle and ai-driven decision making, using checklists, established frameworks, or a custom framework to ensure legal and ethical outcomes.

Apply a starter data ethics checklist to evaluate data practices, fairness, transparency, and security across business processes, data collection, processing, and governance within data science projects.

Develop a custom data ethics framework using a five-step process—identify, define, evaluate, mitigate and monitor, review and refine—integrating ethics into people, process, technology, and governance alongside legal compliance.

Develop and embed a custom data ethics framework across your organization using a five-step process that aligns people, process, and technology with ethical principles, governance, training, and change management.