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INST201: Introduction to Information Science — Exam 2 Study Guide Spring 2026

Exam Overview

  • Date: April 3, 2026
  • Format: In-class, closed book, closed notes
  • Total Points: 80 points
  • Coverage:
    • AI
    • Privacy & Security
    • Surveillance
    • Economics & Labor
    • Social Media
    • Online Communities
  • Assigned Readings/Videos:
    • DeepMind et al., Ethical and Social Risks of Harm From Language Models
    • Trump Administration. White House. America's AI Action Plan
    • Crash Course: Social Media
    • Carr & Hayes. Social Media: Defining, Developing, and Divining
    • Zuboff. Surveillance Capitalism (Chapter 1)
    • Ben Jordan's piece on Flock Safety
  • Question Types:
    • Multiple choice
    • True / False
    • Short answer
    • Critical thinking essay

Artificial Intelligence Core Concepts

  • Definition of AI vs. Machine Learning:
    • Artificial Intelligence (AI) encompasses systems that simulate human intelligence to perform tasks that typically require human cognition, such as understanding language, recognizing patterns, and making decisions.
    • Machine Learning (ML) is a subset of AI that focuses on algorithms and statistical models that allow computers to perform specific tasks without explicit instructions, instead relying on patterns and inference.
  • Is AI actually 'artificial' and 'intelligent'?
    • This question examines the true nature of AI, questioning if it mimics human intelligence and what constitutes 'intelligence' in machines.
  • What is intelligence? (Melanie Mitchell's observation):
    • Intelligence is often defined in various ways, but it generally involves the ability to learn from experience, adapt to new situations, understand complex ideas, and engage in reasoning.
  • Moravec's Paradox:
    • This paradox states that high-level reasoning requires relatively little computational power, while low-level sensorimotor skills require enormous computational resources. Thus, tasks that are simple for humans are difficult for machines and vice versa.
  • Crawford's argument — AI as material and embodied:
    • Kate Crawford argues that AI systems are not just abstract algorithms; they are material and embodied systems that have a real-world impact based on their design and implementation.

Opportunities and Risks

  • OECD Potential Benefits of AI (general categories):
    • AI offers potential benefits in enhancing productivity, improving healthcare, and fostering innovation.
  • Three Categories of Risk:
    • Malicious Use: Potential for deliberate harm, such as using AI for cyberattacks or misinformation.
    • Malfunctions: Errors in AI systems that could lead to unintended consequences.
    • Systemic Risks: Broader societal impacts of widespread AI implementation, such as job displacement and inequality.
  • Real-world example: DeepMind and the NHS data case:
    • This case highlights ethical concerns regarding data use, consent, and the potential consequences of decision-making in healthcare using AI.

AI and Society

  • AI as a new kind of information network:
    • AI is positioned as a transformative network influencing how information is processed and shared.
  • Historical comparison:
    • Comparison to historical information networks such as the printing press, radio/TV, and social media, illustrating the evolving dynamics of information control.
  • Who tries to control new information networks, and why:
    • Entities ranging from governments to corporations seek control to influence public discourse, protect interests, or maintain power.
  • Information countermovements and decentralization:
    • Movements advocating for decentralized information systems to counterbalance the concentration of power within traditional networks.
  • AI as an amplifier of agency:
    • AI has the potential to empower users by enhancing their capabilities, but it also risks reinforcing existing power structures.
Are we moving toward an Algorithmic or Attention-Dominated Society?
  • This discussion focuses on whether society is prioritizing algorithms that dictate attention or if individuals may reclaim agency.
  • Zuboff and Crawford's arguments about the data-extractive society:
    • Zuboff describes contemporary capitalism as being predicated on extracting personal data for profit, impacting civil liberties.
  • Wu and Citton's arguments about the attention economy:
    • They argue the economy is increasingly structured around capturing and monetizing user attention through digital platforms.

AI in Policy

  • Biden vs. Trump administration approaches to AI:
    • A comparison of differing governmental philosophies and policies toward AI: ethical considerations and regulatory frameworks.
  • California SB 53:
    • Legislation detailing California's approach to the governance of AI technologies.
  • AI copyright lawsuits — general landscape:
    • Overview of ongoing legal debates about AI-generated content and intellectual property rights.
  • Character.AI and mental health concerns:
    • Examination of AI companions and their implications for human mental health and emotional well-being.

Privacy & Security

  • Definition of Privacy:
    • Privacy is the right of individuals to maintain control over their personal information and to be free from unauthorized intrusion.
  • Three components of privacy:
    • Personally Identifiable Information (PII): Information that can be used to identify an individual.
    • Physical Access: The right to control who has access to one's physical space.
    • Freedom from Undue Influence: The ability to make decisions without coercion or manipulation.
  • Four reasons privacy matters:
    • Protection from Misuse of PII: Preventing exploitation of personal information by malicious actors.
    • Relationships: Privacy fosters trust in interpersonal relationships.
    • Autonomy: Privacy underpins individual autonomy and self-determination.
    • Human Dignity and Power: Essential for maintaining dignity and power over one’s life.
  • The Nothing-to-Hide Argument — and why it fails:
    • We all have secrets: Privacy is intrinsic to human dignity, regardless of the visibility of personal actions.
    • Disclosure and the aggregation problem: Sharing incremental data can lead to comprehensive profiling.
    • No-fault attacks: The privacy-preserving concerns are not only about guilt or innocence but also about potential risks of data exposure.

Cyber Security

  • Definition of a security problem (vs. a simple malfunction):
    • A security problem arises when there is potential for unauthorized access or damage, whereas a malfunction is simply a failure of the system’s performance.
  • CIA Triad — all three components:
    • Confidentiality: Protection of information from unauthorized access.
    • Integrity: Assurance that the information is reliable and untampered.
    • Availability: Ensuring that authorized users have access to data and resources when needed.
  • Software vulnerability, exploit, and malware — distinctions:
    • A vulnerability is a weakness that can be exploited, an exploit leverages a vulnerability to compromise a system, and malware refers to malicious software designed to harm or exploit.
  • Types of attacks:
    • Virus: A self-replicating program that attaches to files.
    • Worm: A standalone malware that replicates itself to spread to other systems.
    • Watering Hole: A strategy where the attacker compromises a site likely to be visited by the target.
    • Social Engineering / Spear-phishing: Manipulative techniques used to exploit human vulnerabilities.
  • Vulnerability disclosure and Bug Bounties:
    • Programs that incentivize individuals to report vulnerabilities instead of exploiting them maliciously.
  • Privacy and Security convergence — why they are merging:
    • The increasing overlap between privacy concerns and security measures as organizations aim to protect user data while ensuring secure systems.
  • Privacy vs. National Security tension:
    • An ongoing debate on the balance between safeguarding individual privacy rights and ensuring national security.
  • NSA history and domestic surveillance:
    • Overview of the National Security Agency’s role and history in monitoring communications for security purposes.
  • Section 702 of FISA:
    • A legal framework that allows surveillance of foreign persons outside the United States without a warrant, often impacting citizens’ privacy rights as well.

Datafication & Surveillance

  • Definition of surveillance:
    • Surveillance is the monitoring of behaviors and activities by an individual or group, typically in a systematic way.
  • Four characteristics of surveillance:
    • Unequal information gathering: Disparities in what information is collected from different groups.
    • Establishing hierarchies and power: Surveillance reinforces social hierarchies through information asymmetries.
    • Enacting control after the fact: Ability to monitor actions after they have occurred, influencing future behavior.
    • Inducing self-discipline (Hawthorne Effect): Individuals may change their behavior when they know they are being watched.
  • Surveillance Capitalism — Shoshana Zuboff:
    • Definition: A term coined by Zuboff describing the new economic system where personal data is commodified and used for profit.
    • Traditional capitalism vs. surveillance capitalism: The former focuses on material goods, while the latter relies on data extraction, especially from individuals.
    • Behavioral surplus: Data produced through user behavior that exceeds what is necessary for services initially promised.
  • Real-world examples:
    • Google/Alphabet: Data-driven business model relying heavily on user data for targeted advertising.
    • Meta: Corporate practices regarding data collection from social media usage.
    • Flock Safety: Company deploying surveillance technology for security purposes, illustrating ethical concerns.
  • Data brokers — definition, incentives, and practices:
    • Companies that buy and sell personal data for various purposes, often lacking transparency.
  • Ways to address surveillance:
    • Solutions include legislative action, increased public awareness, and competition in technology markets.

Economics & Labor

  • Types of Economy:
    • Information Economy: Economy primarily focused on creating, distributing, and using information.
    • Platform Economy: Relies on online platforms that connect service providers with consumers.
    • Sharing Economy: Emphasizes collaborative consumption and sharing of resources.
    • Creator Economy: Focused on independent content creators generating income through platforms.
    • Attention Economy: Centers around monetizing attention via advertisements and engagement.
    • Gig Economy: Economic model involving short-term, flexible jobs, often through digital platforms.
The Advertising Model
  • Freemium model:
    • A business tactic where basic services are provided for free while premium features are charged.
  • The four-step advertising model:
    • Free service → Data → Targeting → Attention: Users get free service; data is collected and used for targeted advertising which seeks to capture user attention.
  • Ethan Zuckerman — The Internet's Original Sin:
    • Argues that surveillance is integral to the web's business model, leading to engagement being prioritized over user experience.
    • Users as the product: Their attention is monetized, making them less customers and more products in this economy.
Online Advertising by the Numbers
  • Statistics highlighting the scale and economic heft of online advertising, emphasizing its impact on user privacy and the structure of the digital economy.

Gig Workers

  • Definition of the gig economy:
    • A labor market characterized by short-term contracts and freelance work instead of permanent jobs.
  • Why gig workers are NOT employees:
    • Legal distinctions arise in labor protection, benefits, and worker rights due to the classification of gig labor.
  • Role of the platform in gig labor:
    • Platforms serve as intermediaries that facilitate gig work, exerting control over working conditions.
  • Algorithmic management:
    • The use of algorithms to manage and coordinate gig workers, impacting their labor experiences and conditions.
  • Uber as a case study:
    • Focus on how Uber established its operations through a growth-over-profit strategy, introduced dynamic surge pricing, and faces legal issues regarding worker classification.
    • Uber BV v Aslam (2021) — UK ruling: Landmark decision regarding worker status and rights of gig workers in the UK.
    • California AB5 and Proposition 22: Legislative attempts to regulate gig worker status and rights, illustrating ongoing legal battles in defining gig economy work.
    • Key takeaway: Economic risks are often transferred from the firm onto individual workers in the gig economy.

Content Moderators

  • Definition of content moderation:
    • The practice of monitoring user-generated content to enforce guidelines and regulations on digital platforms.
  • The moderation challenge:
    • Balancing the need for oversight with the risk of overreach or allowing harmful content.
  • Four characteristics of moderation labor:
    • Emotional toll, psychological impacts, perspectives of subjectivity, and visibility in the face of operational demands.
  • DSA Transparency Database:
    • Data points shedding light on content moderation practices and the extent of labor involved in this field.
  • Content moderation as unseen labor:
    • The essential but often overlooked work that supports social media environments, emphasizing the stressors faced by moderators.

Influencers

  • Definition of an influencer:
    • Individuals who leverage their online presence to affect the purchasing decisions and perceptions of their followers.
  • How influencers monetize:
    • Through advertisements, affiliate marketing, platform revenue, merchandise, and subscriptions.
  • Aspirational Labor — Brooke Erin Duffy:
    • Concepts surrounding the motivations, aspirations, and labor demands placed on influencers.
  • Influencer income reality:
    • Variability in earnings, often highlighting vulnerabilities within this labor model.
  • The algorithm as the boss:
    • Algorithms determine which influencers are seen and promoted, reinforcing power dynamics in the industry.
  • Secondary markets created by influencers:
    • New economies generated by influencer content and services.
  • Comparison to gig workers:
    • Shared vulnerabilities regarding labor protections and reliance on platforms, creating a parallel between influencer work and traditional gig roles.

Social Media Definitions and Characteristics

  • Multiple definitions:
    • Definitions vary among scholars, noting nuances and the multifaceted nature of social media.
  • Carr & Hayes (2014) definition:
    • Most comprehensive definition used in class capturing essential characteristics of social media.
  • Five characteristics of social media (Carr & Hayes):
    • Internet-based: Rooted in the digital realm.
    • Persistent channels: Communications that endure beyond the initial engagement.
    • Perceived interactivity: Users engage dynamically and interactively.
    • User-generated value: Content created and valued by users themselves.
    • Mass-personal communication: Blending mass media dissemination with personal engagement.

History and Timeline

  • 1960s–1980s: Development of Email, Bulletin Board Systems (BBS), and Usenet, establishing early forms of digital communication.
  • 1990s: Emergence of web services, including GeoCities and Classmates.com, allowing for personal page creation and early Social Networking Sites (SNS).
  • 2000–2005: Growth of Web 2.0 technologies, including blogs, wikis, and the beginnings of social networks.
  • 2006 onward: Proliferation of platforms like Twitter and Instagram and the solidifying of the platform economy.

Social Media and Society

  • Social media curation and its commercial incentives:
    • The economic drivers behind selective content management on social platforms.
  • Is social media an online community? (apply Baym's five qualities):
    • An analysis of whether social media platforms meet criteria defining a community.
  • Social media addiction:
    • Exploration of the phenomenon termed social media addiction, including definitions and statistics.
  • Mental health and social media:
    • Examination of the relationship between social media use and mental well-being, informed by empirical research.
  • Zuckerberg's claim and why it is misleading:
    • Critical evaluation of statements made by social media executives and their implications.
  • Meta on trial:
    • Discussions around company practices regarding responsibility for harmful design choices and potential liability.
  • There is no unbiased social media:
    • Acknowledgment of the inherent biases present in social media algorithms and practices.

Online Communities

What Makes an Online Community
  • Baym's five qualities of online communities:
    • Space: A place for engagement and interaction.
    • Shared Practice: Common behaviors or rituals among users.
    • Shared Resources & Support: Availability of content or aid among community members.
    • Shared Identity: A common sense of belonging.
    • Interpersonal Relationships: Connections that form between community members.
  • The difference between a forum and a true community:
    • Distinguishing a simple online forum from a community based on user engagement and connection.
  • Ray Oldenburg's Third Place concept:
    • The idea of a ‘Third Place’ as a social environment outside of home and work that fosters community interaction.
Types of Online Communities
  • Place-based online communities:
    • Examples include platforms like NextDoor or local neighborhood groups connecting users by geography.
  • Interest-based, identity-based, and practice-based communities:
    • Communities emerging around shared interests, identities, and collaborative practices.
Online Identity
  • Personal identity vs. social identity:
    • Examination of how online identities may diverge from one’s real-life identities.
  • Disembodied identities online:
    • The phenomenon where individuals present themselves differently in online spheres.
  • Imagined audiences:
    • Understanding how users perceive and create content with a specific potential audience in mind.
  • Self-branding:
    • The active process of creating a public persona for oneself through digital means.
  • Goffman's concept of multiple social roles — applied online:
    • Analysis of how individuals curtail their identities depending on social contexts, drawn from Erving Goffman's theoretical frameworks.

Study Tips

  • Review all lecture slides:
    • Ensure comprehension of key concepts and topics.
  • Review all reading materials and videos:
    • Familiarize with perspectives and arguments presented across media.
  • Understand concepts and think about real-world examples:
    • Relate concepts explicitly to current events or personal experiences.
  • Be able to explain ideas in your own words:
    • Reinforces understanding and recalls during the exam.
  • Practice distinguishing between similar terms:
    • Gain clarity on nuanced differences that could be tested.
  • Focus on understanding the 'why' behind each concept:
    • Comprehending underlying theories aids in critical thinking.

Remember

  • Budget your time during the exam:
    • Strategically allocate time for each question section.
  • Read questions carefully:
    • Insightfully understanding questions helps filter out what is being asked.
  • Answer what is asked:
    • Directly and succinctly address each part of the question posed.
  • Use specific concepts and terminology from class:
    • Employing accurate language evidences confidence and understanding.
  • For essays, address all parts of the question:
    • Thorough responses require engagement with every element of the prompt.
  • Leave time to review your work:
    • Final checks can help catch mistakes or enhance argument clarity.

Good luck!