Exercises: Labor, Automation, and the Gig Economy

These exercises progress from concept checks to challenging applications. Estimated completion time: 3-4 hours.

Difficulty Guide: - Star-1 Foundational (5-10 min each) - Star-2 Intermediate (10-20 min each) - Star-3 Challenging (20-40 min each) - Star-4 Advanced/Research (40+ min each)

Part A: Conceptual Understanding (Star-1)

Test your grasp of core concepts from Chapter 33.

A.1. Section 33.1.1 defines algorithmic management and identifies four functions it performs: task allocation, performance monitoring, evaluation and discipline, and compensation. For each function, explain (a) how it was traditionally performed by a human manager and (b) how algorithmic management changes the experience for the worker. What is lost in the transition from human to algorithmic management?

A.2. Dr. Adeyemi describes the Accountability Gap as "most personal" in algorithmic management: "When a human manager fires you unfairly, you can at least point to the person who made the decision. When an algorithm deactivates you, who do you appeal to?" (Section 33.1.2). Explain why the opacity of algorithmic management makes the Accountability Gap more acute in the workplace than in other contexts we have studied.

A.3. List the five dimensions of gig worker data asymmetry identified by Sofia Reyes (Section 33.3.2): earnings data, rating data, algorithmic data, market data, and behavioral data. For each dimension, explain in one sentence what the platform knows that the worker does not.

A.4. Section 33.4.2 distinguishes between "task displacement" and "job displacement." Explain the difference and describe why the distinction matters for understanding the impact of automation on employment.

A.5. Define "just transition" as used in Section 33.5.1. What does the term's origin in labor and environmental justice movements tell us about how it should be applied to data-driven automation?

A.6. Sofia Reyes proposes six data rights for workers (Section 33.6.2). List them and explain how each right addresses a specific power imbalance created by algorithmic management.

Part B: Applied Analysis (Star-2)

Analyze scenarios, arguments, and real-world situations using concepts from Chapter 33.

B.1. Consider the following scenario:

A remote worker receives an email from their employer announcing the deployment of "ProductivityTracker Pro," a new monitoring system. The system takes a screenshot of the employee's screen every three minutes, logs all keyboard and mouse activity, classifies applications as "productive" or "unproductive," calculates an "active time" score, and generates a weekly productivity report for managers. Employees who maintain an "active time" score above 85% qualify for a performance bonus. The employer describes the system as "voluntary."

Using concepts from Sections 33.2.1 and 33.2.2, analyze: (a) whether this system is meaningfully voluntary, (b) what data is collected and how it might be used beyond the stated purpose, (c) what behaviors the system is likely to incentivize (both productive and counterproductive), and (d) what legal protections, if any, the employee might have.

B.2. Section 33.2.3 presents evidence that workplace surveillance increases short-term compliance but decreases creativity, erodes trust, increases turnover, and incentivizes counterproductive gaming behaviors. A CEO reads this evidence and responds: "Our company isn't measuring creativity. We're measuring output in a logistics operation where tasks are routine and quantifiable. Surveillance is appropriate for our context."

Evaluate this argument. Is there a legitimate case for intensive surveillance in routine work environments? What are its limits? What governance mechanisms should be in place even for routine work surveillance?

B.3. Apply the Consent Fiction analysis from Section 33.1.3 to the following statement from a ride-hailing company:

"Our driver-partners enjoy the freedom and flexibility of independent work. They choose when to drive, which rides to accept, and how long to stay on the platform. Their participation is entirely voluntary."

Identify at least four ways in which the "freedom and flexibility" described may be illusory or constrained by algorithmic management and data asymmetry.

B.4. Section 33.3.3 describes Veena Dubal's research on "algorithmic wage discrimination" — personalized pay offers to individual drivers based on the platform's prediction of what each driver will accept. Analyze this practice using three ethical frameworks: (a) a rights-based framework (does it violate any worker rights?), (b) a consequentialist framework (what are the net effects on worker welfare?), and (c) a justice framework (does it produce fair outcomes?).

B.5. Sofia's investigation (Section 33.7.1) found that "no worker she interviewed had successfully accessed their data" despite having legal rights under the CCPA. Identify the barriers that prevent workers from exercising their data rights in practice. Then propose three specific changes — one legal, one technical, one organizational — that would make data access rights more meaningful for gig workers.

B.6. Section 33.4.3 discusses the "hype cycle" around generative AI and employment. Ray Zhao warns against both the dystopian narrative (AI eliminates most jobs) and the utopian narrative (AI creates more jobs than it destroys). Identify a specific occupation or profession you are familiar with and analyze how generative AI is likely to affect it over the next decade. Apply the "responsible analysis" criteria from Section 33.4.3: specificity, complementarity, and institutional context.

Part C: Real-World Application Challenges (Star-2 to Star-3)

These exercises ask you to investigate labor and data in your own environment.

C.1. (Star-2) Workplace Surveillance Inventory. If you currently work (or have recently worked), make a list of every data-collecting system in your workplace: time clocks, badge scanners, email monitoring, productivity software, location tracking, camera systems, etc. For each system, identify: (a) what data it collects, (b) who can access the data, (c) whether you were informed about the collection, (d) whether you consented meaningfully, and (e) whether you can access the data collected about you. Present your findings in a table.

C.2. (Star-2) Gig Worker Terms of Service Analysis. Download the terms of service or user agreement for a gig platform (Uber, DoorDash, Fiverr, Upwork, TaskRabbit, or another). Read the sections related to data collection, worker evaluation, payment, and dispute resolution. In a one-page analysis, identify: (a) what data the platform claims the right to collect about workers, (b) how the platform describes its algorithmic management processes, (c) what rights workers have to access their data, and (d) what dispute resolution mechanisms exist.

C.3. (Star-3) Automation Impact Assessment. Choose an industry or sector you are familiar with (healthcare, education, retail, finance, manufacturing, creative work). Identify three specific tasks within that sector that are most susceptible to automation and three that are least susceptible. For each task, explain your reasoning using the routine/non-routine and cognitive/manual distinctions from Section 33.4.2. Then assess: if the automatable tasks are automated within the next decade, what happens to the workers who currently perform them? Who benefits and who bears the cost?

C.4. (Star-3) Worker Data Rights Design. Design a "worker data dashboard" for a gig worker on a ride-hailing platform. The dashboard should display the information the worker would need to make informed decisions about their work — including data the platform currently withholds. Sketch or describe the dashboard layout, explain what data it would include, and identify the barriers (technical, legal, competitive) that prevent platforms from providing such a dashboard today.

Part D: Synthesis & Critical Thinking (Star-3)

These questions require you to integrate multiple concepts from Chapter 33 and think beyond the material presented.

D.1. The chapter draws an explicit parallel between traditional labor relations concerns (workplace safety, fair wages, collective bargaining) and data governance concerns (data access, algorithmic transparency, data portability). Write an essay (300-500 words) arguing either that data rights should be formally incorporated into labor law or that data rights and labor rights should remain separate legal frameworks. Defend your position with specific examples from the chapter.

D.2. Section 33.5.2 describes Denmark's "flexicurity" model, which combines flexible labor markets with generous safety nets and retraining programs. Evaluate whether a similar model could address the displacement effects of AI-driven automation. What institutional conditions are necessary for flexicurity to work? What conditions present in Denmark may not exist in other countries? Be specific about the data governance components.

D.3. The chapter identifies a fundamental tension: algorithmic management is opaque by design because platforms consider their algorithms to be competitive trade secrets. But workers argue they have a right to understand the systems that manage them. Propose a governance framework that balances these competing interests. How much transparency is enough? Who should have access to what information? Is there a role for independent auditors or trusted intermediaries?

D.4. Connect the themes of Chapter 33 (labor) to Chapter 32 (equity). How does the digital divide affect workers' ability to exercise data rights? How does algorithmic management compound existing inequalities along lines of race, gender, and income? Use specific examples to show how the intersection of labor and equity issues produces outcomes that neither chapter alone fully captures.

Part E: Research & Extension (Star-4)

These are open-ended projects for students seeking deeper engagement.

E.1. Worker Classification Across Jurisdictions. Research how at least three jurisdictions (e.g., California, the UK, the EU, Australia) have addressed the classification of gig workers. Write a comparative analysis (1,000 words) covering: (a) the legal test each jurisdiction uses to distinguish employees from independent contractors, (b) how platforms have responded to each jurisdiction's approach, (c) what outcomes workers have experienced, and (d) which approach best protects workers while maintaining flexibility.

E.2. Algorithmic Management Case Study. Choose a specific company or industry known for intensive algorithmic management (Amazon warehouses, call centers, food delivery platforms, content moderation firms). Conduct independent research on its practices. Write a 1,000-word case study covering: (a) what data is collected about workers, (b) how algorithms make management decisions, (c) documented impacts on workers, (d) how the company justifies its practices, and (e) your assessment using the Worker Data Rights Assessment framework from Section 33.8.

E.3. Just Transition Policy Design. Choose a specific sector facing significant automation (trucking, retail cashiers, legal document review, radiology, customer service). Design a just transition plan for workers in that sector. Your plan (800-1,200 words) should include: (a) an assessment of which tasks are most automatable and over what timeframe, (b) a retraining program design, (c) a portable benefits proposal, (d) a data governance component ensuring workers have access to the data necessary for transition, and (e) a financing mechanism.

Solutions

Selected solutions are available in appendices/answers-to-selected.md.