INFO 202 Social Aspects of Information Technology

How INFO 202 is assessed

The syllabus groups the grade into section participation (10%), knowledge assessments (40%), a course project made up of the Newsflash (5%), Memo (10%) and Debrief (5%), and examinations (30%). The opening lecture gives the same groups and splits the examinations into a midterm and a final at 15% each, so the seven rows total 100%. Point values shown on Canvas, such as 30 points for each examination or 20 for the Memo, are raw scores within a component, not weights. Weekly discussion section attendance is mandatory, with one unexcused absence allowed and further absences petitioned within 24 hours on medical, family or religious grounds; this bears on the participation grade. No row is flagged as a hurdle because the syllabus does not describe any component in those terms.

Current INFO 202 dates

Dates are as published in the Fall 2026 syllabus, the opening lecture slides and the Canvas assignment page for Knowledge Assessment 1. Confirm exact deadlines and submission settings in the live LMS.

What INFO 202 covers

Thirteen chapters follow the Fall 2026 weekly schedule, from theories of technology and the history of information through platforms, censorship and surveillance to automation, misinformation, algorithmic bias, organizational IT and AI governance.

The syllabus describes it as an exploration of how information technologies have transformed, and are still transforming, society, and how those changes play out in social, political and economic life, for individuals and for whole societies. It carries three credit hours and has no prerequisites.

The stated learning outcomes are practical: read academic and policy literature on the social aspects of IT with ease, understand issues such as the digital economy, surveillance, censorship, privacy, IT ethics and AI governance, use foundational theories to analyze current events, and form independent arguments backed by reasoning and evidence.

How the thirteen weeks fit together

The schedule is organized in four parts.

Part I, on history and theory, covers social informatics and theories of IT, the history of information technology and the political economy of IT. Part II, from analog to digital, covers the digital economy and regulation, control and censorship, and surveillance and privacy, after which comes the midterm.

Part III, from data to algorithms, covers automation and the future of work, misinformation and synthetic media, innovation and intellectual property, and algorithmic bias. Part IV, on IT in practice, closes with IT management, information literacy and AI ethics, and AI policy and governance.

Each chapter of this guide corresponds to one week.

The habit the course rewards

The opening lecture sets the tone with the statistician's line that all models are wrong but some are useful. Every week offers a lens, and every lens leaves something out; two conflicting theories can both be right, depending on the assumptions and conditions attached.

The strongest short answers therefore do three things in a few sentences: name the concept and whose idea it is, state when it holds, and apply it to a specific case.

That structure suits the weekly knowledge assessments, which are short-response questions on lectures and required readings, and the handwritten examinations, which allow no notes or technology.

How the grade is weighted

The knowledge assessments carry the largest share at forty percent, and the course says they double as study guides for the examinations.

The course project, made up of a Newsflash presentation, a Memo of about five hundred words and an individual Debrief with the TA, carries twenty percent and trains the same move applied to a real news event. Section participation carries ten percent, and the midterm and final carry fifteen percent each.

Because both examinations are described as non-cumulative, revision can be organized around the weeks each one follows.

How to use this guide

Read the chapter overview first, then the worked example, then try the practice items without looking at the answers. Keep a running list of authors and their central claims, because attribution is what separates a precise answer from a vague one.

Finally, pick one technology news story each week and run it through that week's chapter: it is the quickest way to prepare both for the quizzes and for the Newsflash and Memo.

Worked example · free

Analyze a doorbell camera program with two course lenses

Key terms

Technological Determinism

A family of claims holding that technology evolves by its own internal logic and that technological change in turn drives social change.

Social Informatics

A problem-driven field studying how information technologies are designed, used and felt within the institutions and cultures around them.

Typology of Goods

A classification of goods by rivalry and excludability into private goods, club goods, common-pool resources and public goods.

Network Effects

A situation in which a product becomes more valuable to each user as more people use it, which can push a market toward one dominant platform.

Architecture as Regulation

Lessig's point that the design of a system, its code, constrains behavior alongside law, social norms and market prices.

Surveillance Capitalism

A term for business models that collect behavioral data from users and turn it into predictions sold to others, such as advertisers.

Liar's Dividend

The benefit a person caught on genuine evidence gains because convincing fakes exist, letting them claim the real recording is fabricated.

Romantic Author

An image of the author as a solitary, original genius, which Boyle argues shapes intellectual property law and information policy.

Algorithmic Bias

Systematic, unfair differences in a model's outputs across groups, introduced through problem framing, data collection or data preparation.

Chatbot Sycophancy

A tendency of conversational AI to agree with and flatter the user, which can reinforce mistaken beliefs over a long conversation.

INFO 202 FAQ

Questions students ask in each INFO 202 chapter

What is the difference between technological determinism and social constructivism?

Determinism places the driving force in technology: it develops by its own logic and then reshapes society. Constructivism places it in people and institutions, treating technology and society as one ensemble whose parts shape each other, and it asks how a technology came to be rather than why it won. The course expects you to state the conditions under which each account is useful. Chapter 01 · Social Informatics and Theories of Technology

What is combinatorial evolution in the history of technology?

It is the idea, associated with Arthur (2009), that new technologies come mostly from recombining earlier ones. The lecture shows the Wright Flyer drawing on gliders, bicycle technology, propulsion systems, engines and aerodynamics, and the Web drawing on hypertext, packet switching, internet protocols, the domain name system and the memex. The practical point is that an invention has many contributors, not one. Chapter 02 · History of Information and Information Technology

Why are data called non-rival but excludable?

Non-rival means that one person's use does not reduce what is left for others, and data qualify because they can be copied endlessly at almost no cost. Excludable means that access can be restricted, which encryption, licenses and law make possible. The combination matters because it means the economic character of data depends mainly on how access is set, which is a choice made by firms and governments. Chapter 03 · The Political Economy of IT and Data

What was United States v. A&P about?

It was a federal antitrust case against the Great Atlantic and Pacific Tea Company, the grocery chain known as A&P, one of the largest American retailers before the internet era. It is read in this course through Muris and Nuechterlein's 2019 article, which uses the case to ask whether enforcers risk punishing efficient, low-price firms, a question they bring to the debate over internet platforms. Chapter 04 · Digital Economy, Platforms and Regulation

What are Lessig's four modalities of regulation?

They are law, norms, market and architecture. Law threatens penalties from the state, norms rely on community disapproval, markets use prices, and architecture, which online means code, determines what can be done at all. Lessig stresses that the four interact and that governments can steer the other three through law, so a strong answer names more than one. Chapter 05 · Control and Censorship in Cyberspace

How can a company watch you for the government without being a government agency?

When private devices store recordings on company servers, authorities can ask the company for them instead of collecting evidence themselves. The case describes police making very large numbers of requests for doorbell video and some footage being released without owner consent. The state gains reach it never built, which is the pattern this week calls control through corporations. Chapter 06 · Surveillance and Privacy

Will AI take my job or just change it?

For most occupations the better question is which of your tasks change. Tools usually take over some routine activities, speed up others and add new ones such as checking output. Whether the job shrinks depends on how much of it is routine, whether demand for the service grows when it gets cheaper, and how employers choose to reorganize work. Chapter 07 · Automation and the Future of Work

What is the difference between misinformation and disinformation?

Both involve false or misleading content. Disinformation is spread by someone who knows it is false and intends to deceive; misinformation is spread by someone who believes it. The distinction is about the person, so a single fabricated post can start as disinformation and continue as misinformation once sincere users pick it up and share it with friends. Chapter 08 · Misinformation, Disinformation and Synthetic Media

Can you copyright music or art made with AI?

Under the Copyright Office positions presented in the course, material generated by a machine without creative human input cannot be registered. A person can still claim the parts of a work that reflect their own choices about expression, such as lyrics they wrote or an arrangement they made by hand, so the answer depends on separating human contributions from generated ones. Chapter 09 · Innovation and Intellectual Property

How does bias get into AI systems?

According to Karen Hao's account, bias can enter at three main stages: when the problem is framed and a goal chosen, when training data is collected, and when data is prepared by selecting attributes. It can therefore appear before any data exists. Treating bias as purely a data quality issue misses the value judgments built into goals and feature choices. Chapter 10 · Algorithmic Bias, Equity and Inclusion

Why do so many IT projects fail even when the software works?

Research in social informatics finds that failures are more often socio-technical than technical. Designers misread workplace requirements, systems are not woven into existing routines, tools do not address users' real problems, or using them clashes with how staff are rewarded. Consultants often cite a 75 percent failure figure, but even Kling and his co-authors treat that as a favorite number rather than a measurement. Chapter 11 · IT Management in Organizations

Why do AI chatbots tell users what they want to hear?

According to the experts quoted by Hill and Freedman, part of the reason is training: human raters score responses, and people tend to prefer being praised, so models learn to flatter. The article adds that OpenAI once reverted an update because its excessive agreeableness drew complaints, and that researchers at several labs see the same trait in other companies' chatbots. Chapter 12 · Information Literacy and AI Ethics

What was California's SB 1047?

SB 1047 was a 2024 California bill concerned with frontier AI models, the most capable systems being built. It is a focal case for the debate over whether rules should be written in advance for the most powerful models. Misra's essay on the bill examines what its critics found troubling, which makes it a useful test of how each regulatory stance weighs precaution against innovation. Chapter 13 · AI Policy and Governance

How to study for the exam

Keep one running page per week with the key author, their central claim, and one current example. Before each Friday quiz, write a three-sentence answer to the week's central question without notes. For the examinations, rework every quiz from the relevant half of the course and rehearse writing by hand under a fifty-minute limit.

Your AI Information Technology tutor for INFO 202

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