Learning Objectives

Unit	Week	Learning Objectives
Unit 1: Making the Datafied World	Week 1: Foundations	Use a classic example (bridges) to see how mundane technologies are not just neutral tools, but organize social relationships and exercise power. Learn the basics of the HCE toolkit. This is a set of analytical concepts that make sense of how science and technology interact with society. Practice applying the HCE toolkit in a contemporary, real-world context: price-fixing rent algorithms. See how the toolkit helps identify stakes and ethical issues.
	Week 2: Making Data	Understand that data is made by people for specific purposes and reflects selective choices. Use classification and representation as tools to think about what data includes, what it says, and what is left out. Build a classification system and think critically about how it can reinforce inequality or harmful assumptions. Explore how identity can shift over time in response to how people are categorized and known through datafied technologies. Discuss how data often travels beyond and obscures its original context.
	Week 3: Making Knowledge	Explain how scientific knowledge is produced by a body of researchers and other participants through social processes rather than simply discovered. Describe the role that scientific communities play in shaping knowledge. Apply the concept of scientific paradigm to complicate simplified timelines that distort the collective and contextual nature of scientific change. Analyze how science comes to be viewed as authoritative within specific historical and social contexts. See how the concept of objectivity serves as a method for generating social trust in democratic societies.
	Week 4: Narratives about Human-Technology Futures	Analyze dominant cultural narratives about data and their influence on society. Interpret stories about data from a humanistic perspective, including dystopian and utopian themes. Examine how visions of the future shape present-day behaviors and technologies. Critically evaluate who benefits from prevailing narratives about data and its role in the world.
Unit 2: Histories of the Datafied World	Week 5: States	Unpack the historical origins of statistics and their connection to state power and governance. Recognize how statistical practices emerged from efforts to manage populations and resources. Critically reflect on the ethical implications of using statistical methods and population data shaped by this history.
	Week 6: Capitalism	Analyze the historical and cultural development of Silicon Valley and its global influence on data and technology. Examine the roles of state, military, and venture capital funding in shaping Silicon Valley. Critically evaluate dominant narratives about Silicon Valley’s origins and success. Explore how cultural ideals like disruption and founder mythology influence Silicon Valley's political and economic power.
Unit 3: Governance	Week 7: Surveillance and Privacy	Understand how surveillance operates as a form of power in a datafied world. Analyze the unequal distribution of visibility and its impact on different social groups. Analyze the limitations of contemporary privacy laws by examining their historical origins. Understand more collective and contextual approaches to data, privacy, and protection.
	Week 8: Governing with Algorithms	Understand the most important ethical stakes of automated risk-assessment and decision-making systems. Analyze the limitations of “bias” and “fairness” as frameworks for addressing the ethical stakes of these systems. Examine the legal, political, and social contexts that shape algorithmic governance. Survey the deeper histories and values embedded in algorithmic systems and their impacts.
	Week 9: Data From Above, Data From Below	Understand how global health data is made, circulated, and used to guide interventions. Analyze the role of major institutions in shaping definitions of health and well-being. See how grassroots movements, like the AIDS movement, challenge and reshape data practices. Reflect on the power dynamics behind who defines, collects, and is represented by data on health and well-being.
Unit 4: Industry, Capitalism, and Labor	Week 10: Industrial Revolutions, Automation, and Labor	Survey the diverse forms of labor that support data systems and the digital economy. Analyze how common automation narratives obscure the human work behind data and technology. Examine power dynamics and hierarchies in gig work, content moderation, and tech workplaces. Explore how workers respond to and resist exploitation through activism, legal action, and protest.
	Week 11: Environmental Externalities and the Tech Workplace	Analyze the culture of the tech workplace, including expectations about work-life balance, role models, and gender norms. Identify the historical and structural barriers to diversity and inclusion in the tech industry. Bring into view the experiences and contributions of marginalized tech workers, including Latinx, African American, and South Asian communities. Analyze the invisible labor and environmental costs behind digital technologies and global supply chains.
Unit 5: Sites of Data Ethics Today	Week 12: Ethics as Institutions	Understand the origins and purposes of institutional research ethics frameworks like the Common Rule and IRBs. Practice analyzing the assumptions and limitations of traditional ethical guidelines in today’s data-driven research landscape. Examine how professional codes of conduct intersect with broader ethical challenges in data work. Imagine alternative approaches to ethical practice beyond compliance-based models.
	Week 13: Ethics as Philosophy, Ethics as World-Making	Get familiar with the traditional Western moral philosophical frameworks and analyze their assumptions about agency and the good life. Evaluate the strengths and weaknesses of these frameworks’ embrace of abstraction. Critically assess the ethical frameworks informing dominant approaches to AI Ethics, such as value alignment. Begin to think about ethics as a situated, collective, and ongoing practice.
	Thanksgiving Break	Thanksgiving Break
	Week 14: Ethics as Cultures of Data Practice	Survey the various appeals to “openness” in contemporary science (open source, open data, open methods) and understand the problems they aim to solve. Examine the limits and challenges of open science, and consider who openness serves in practice. Survey contemporary discussions about reproducibility and the replication crisis, and examine competing approaches to framing what’s at stake in them. Understand robust scientific practice as more than simply methodological, but as a situated and collective effort.