Lens 3: Lifecycle and Labor
Materiality and Extraction
Whose work makes ChatGPT safer?
Documented case · Kenya safety labeling
Who reads the text that users are protected from?
Case: Perrigo (2023) . Content note: workers read texts about violence and sexual abuse. No examples are shown.
Start with the work, not a lifecycle definition. The source is TIME’s investigation from January 2023, based on interviews and documents. Workers labeled harmful passages for a toxicity detector. This is not the same task as writing and ranking answers for human-feedback training (Perrigo 2023; Ouyang et al. 2022) .
Ask participants to follow the work across the companies and workers in the diagram. The learning question is: Who does the work, carries the risk, or has no recourse?
Workers can speak and organize
A worker-led account makes hidden labor visible.
Contextual still: Mathenge (2024) ; videography and editing: P. Teka, CC BY 4.0. This is a separate source from TIME’s interviews.
The image is the published preview of the documentary Data Workers Organizing: The African Content Moderators Union. It is a source about organizing and working conditions at Sama, not a photograph of the labeling task in the 2021–2022 contract. Do not identify the speaker as one of TIME’s anonymous interviewees. The film includes sensitive testimony; use this safe still without playing the film in class (Mathenge 2024) . It is a figure source, not a new assigned reading.
The later organizing account shows why an unknown complaint route in a report is not evidence that workers lack agency. Distinguish worker-led action from what a researcher can change through an institution.
One safety intervention, unevenly carried costs
Reported pay
USD 1.32–2 per hour
Reported take-home pay.
Reported harm
Four interviewees
All reported lasting distress.
Counseling was reported. Access and benefit were disputed.
Reported facts: Perrigo (2023) . Conditions vary across projects.
TIME reviewed documents and interviewed four workers. This is a documented case, not a representative wage survey. Sama disputed the wage range and reported a higher range for some roles. Workers reported heavier workloads than Sama stated. Workers described counseling as ineffective or hard to access; Sama said both group and individual counseling were available. OpenAI said Sama managed pay and mental-health provisions (Perrigo 2023) . Keep these disputes explicit; do not turn the four interviews into an industry-wide rate.
Use the lesson page for the detailed case table. The important question for this slide is whose safety is improved and who bears the cost of that work.
From documentation debt to harm without recourse
Decision
Who set the data and work rules?
Record
Can affected people find the decision?
Recourse
Who can challenge it and seek remedy?
What would a worker or an affected person need to question the decision?
Conceptual bridge: Bender et al. (2021) , sec. 4. Case analysis: Perrigo (2023) .
Bender et al. connect undocumented training data with harm without recourse (Bender et al. 2021, 615) . Apply this as a question to the Kenya case. The link is our analysis, not a claim that their paper investigated Sama.
Explain recourse as a route to challenge a decision and seek remedy. Access to counseling alone does not establish such a route. If the report does not establish an effective complaint route, write Unknown from this report. Do not write None.
Introduce extra terminology only if useful: Gray and Suri call invisible on-demand labor ghost work (Gray and Suri 2019) . Taylor describes the presentation of human work as automation (Taylor 2018) . Crawford discusses labor and data extraction (Crawford 2021, chs. 2–3) . These sources support the notes; participants do not need to have read them.
Follow the work across six stages
Framework: Huber and Mähr (2024) . Diagram: Wagner and Mähr (2026), CC BY 4.0. Lifecycle harms: Suresh and Guttag (2021) .
The course framework builds on Rachel Huber and Moritz Mähr’s Decoding Inequality course, published in 2024 and updated in 2025 (Huber and Mähr 2024) . The current drawing is by Laura Wagner and Moritz Mähr, 2026. Suresh and Guttag research sources of lifecycle harm; they did not author these six course stages (Suresh and Guttag 2021) .
The labels are a course convention. Evaluation happens before and after deployment. Governance applies throughout. Ask where the Kenya work sits: labeling at Data; commissioning and supplier safeguards at Governance.
The original source uses architecture selection, data collection, training, application, evaluation and monitoring, governance and regulation. The course uses shorter labels for analysis. Do not confuse this teaching framework with one universal technical pipeline.
Worked matrix: data and governance
Hypothetical worked analysis
System: ChatGPT. Selected stages: Data; Governance.
Who does the work?
Sama text labelers
OpenAI commissions; Sama manages
Who carries the risk?
Workers exposed to harmful text
Workers relying on supplier safeguards
Who has recourse?
Effective remedy: Unknown; counseling disputed
Effective complaint route: Unknown from this report
Possible intervention (PROPOSED)
Require support designed with workers
Request labor safeguards through university procurement
Highest risk among selected stages: Data. Most influence for our fictional university team: Governance. One intervention: Request evidence of worker support through procurement.
Case: Perrigo (2023) . Matrix: a fictional team’s analysis. Interventions are proposals. Other stages: Not examined.
Read the completed matrix as the example of this Lens. A fictional university team is considering ChatGPT and has read the Kenya report. The report supplies the labeling and contracting facts. The team’s identification of risk, influence, and interventions is a hypothetical analysis.
The team can request evidence through institutional procurement; it does not directly manage Sama workers. Counseling is a reported safeguard, not proof of effective recourse. Mark all four unselected stages Not examined. Explain that highest risk means highest among the stages examined.
Takeaway
Find the people behind the system. Ask who can question its decisions.
Training data · documentation debt · harm without recourse
Return to Bender et al. (2021) , sec. 4.
Keep the lens slot at 15 minutes: case first, 5 minutes; concepts and worked matrix, 10 minutes. The Lens 3 group applies the four guiding questions later in the morning. Transition to Lens 4: Why do these patterns persist? The next lens follows money, infrastructure, and rules.
Sources
Perrigo (2023) : Kenya safety-labeling investigation.
Bender et al. (2021) : documentation debt and recourse.
Huber and Mähr (2024) : conceptual source of the six-stage framework.
Suresh and Guttag (2021) : research on sources of lifecycle harm.
Mathenge (2024) : documentary still, CC BY 4.0. Videography and editing: P. Teka.
Original diagrams: Wagner and Mähr (2026), CC BY 4.0.
Full references and evidence limits
