Schedule
Thirteen sessions, September 9 to December 9, 2026, in a weekly three-hour block. Each session is half seminar and half hands-on lab. The tables list the sequence and the deadlines; the reading for each session follows below.
Enrolled students receive the rest in Canvas, where each session has a page for what to do beforehand and a page for what happens in the room. The syllabus holds the policies, the grading, and the course description, and the assignments are published in full with their rubrics.
Module 1, Foundations (Weeks 1–2)
Working definitions, the history of technology in social services, and the sociotechnical protocol used all semester.
| # | Date | Session | Lab | Due |
|---|---|---|---|---|
| 1 | Sep 9 | Definitions of technology and social good | Collaborative definition workshop | |
| 2 | Sep 16 | History of technology in social services | Protocol on paired artifacts |
Module 2, AI (Weeks 3–6)
Algorithmic bias and fairness, how generative AI works, critical and safe use, and organizational adoption.
| # | Date | Session | Lab | Due |
|---|---|---|---|---|
| 3 | Sep 23 | Algorithmic bias and fairness | The paired-case lab | Ethics Case launched |
| 4 | Sep 30 | Language model mechanics | Transformer Explainer + hallucination hunt | |
| 5 | Oct 7 | AI literacy | Calibration, ask-AI-or-ask-a-person triage sort, our AI-disclosure form | Digital Story track chosen |
| 6 | Oct 14 | AI adoption in organizations, with Lauri Goldkind (Fordham) | Debate Lab 1 | Organization named |
Module 3, Data, industry, and capacity (Weeks 7–10)
Data rights and privacy, how the technology industry is structured, the digital maturity of an organization, and digital storytelling.
| # | Date | Session | Lab | Due |
|---|---|---|---|---|
| 7 | Oct 21 | Data sovereignty and privacy | Digital Defense Playbook | Ethics Case due (25%) |
| 8 | Oct 28 | Structure of the technology industry, with Jes Kane (former Google.org) | Funder grant teardown | |
| 9 | Nov 4 | Digital maturity | Capacity assessment + interview protocol | Strategy Pitch launched, interview protocol |
| 10 | Nov 11 | Digital storytelling | Story circle + communication brief | Video notes and communication brief; rough cut (ungraded) |
Module 4, Equity and implementation (Weeks 11–13)
Production and accessibility, design justice and ICT4D, digital equity, and the strategy pitch briefed in class.
| # | Date | Session | Lab | Due |
|---|---|---|---|---|
| 11 | Nov 18 | Production and accessibility | Production stations + caption sprint | Capacity brief |
| 12 | Dec 2 | Design justice, digital equity, and environmental costs | Debate Lab 2 (Detroit edition) + final consult | Digital Story due (25%), memo outline + metrics |
| 13 | Dec 9 | Strategy pitch briefings | Briefings + feedback, story screening | Briefing (of 25%) |
Readings by session
Everything assigned, in order. Course readings are open access or available through the University of Michigan Library. Where a reading is a video, a report, or a primary document, the link goes to it directly.
Week 1. Definitions of technology and social good
The internet was announced as a great equalizer, and for a long stretch the evidence agreed. Speaking, organizing, and publishing became cheap enough that Benkler credited the network with a new, commons-based mode of production, with free software and Wikipedia as his leading exhibits.
Topics
- Affordances and scripts
- Design and power (e.g., Winner on the Long Island overpasses)
- Kranzberg's first law
- Person-in-environment
- Engineering move and solution-focused move
- Technosolutionism
- Amplification thesis
Readings
- Winner, L. (1980). Do artifacts have politics? Daedalus, 109(1), 121–136 (PDF mirror of Winner 1980).
- Blackwell, A. G. (2017). The curb-cut effect. Stanford Social Innovation Review, 15(1), 28–33.
- Pahlka, J. (2012). Coding a better government (TED, ~17 min).
Week 2. History of technology in social services
American poor relief was housed in buildings before it was filed in records. Boston opened its first workhouse in 1660, and by the nineteenth century the poorhouse was the main form of public aid, its minimal food, forced labor, and crowding kept deliberately punitive to discourage anyone with another option. An 1883 investigation exposed conditions at the almshouse in Tewksbury, Massachusetts, where Anne Sullivan, later Helen Keller's teacher, spent part of her childhood; she called her years there "a crime against childhood."
Topics
- Scientific charity and the casework registry
- Mapping tradition (e.g., the Hull-House maps)
- Technology as public infrastructure (e.g., rural electrification)
- Early computerization debates (e.g., Glastonbury, 1985)
Readings
- Eubanks, V. (2018). The digital poorhouse. Harper's Magazine.
- Klein, L. F., et al. (2024). Between data and truth: W. E. B. Du Bois's "Data Portraits". In Data by design (public beta). MIT Press. Archived copy, because the project site is now a book listing, so if the Internet Archive is down read Cooper Hewitt's exhibition Deconstructing power: W. E. B. Du Bois at the 1900 World's Fair instead.
- NASW, ASWB, CSWE, & CSWA. (2017). Standards for technology in social work practice. Skim the headings.
Week 3. Algorithmic bias and fairness
The harm side of algorithmic bias has its strongest evidence in the MiDAS case below. The override question has its own direct evidence. At Allegheny County's Family Screening Tool, a CHI study found that caseworkers who overrode the algorithm's risk score cut the Black-white screen-in disparity from 20 to 9 percent, drawing on context the algorithm lacked, such as a family's engagement with private services that never enter administrative records. A follow-up analysis on updated county data confirmed the finding.
Topics
- Four sources of algorithmic bias
- The New Jim Code
- Allegheny Family Screening Tool
- Competing definitions of fairness (e.g., the COMPAS dispute)
- Audit outcomes (e.g., Gender Shades)
Readings
- Angwin (2022). The Seven-Year Struggle to Hold an Out-of-Control Algorithm to Account. The Markup.
- Benjamin (2019). Race After Technology, Introduction: "The New Jim Code." Polity.
- Eubanks (2018). Automating Inequality, Introduction. St. Martin's Press. (optional)
- Benjamin (2019). Assessing Risk, Automating Racism. Science.
Week 4. Language model mechanics
The mechanism drafts a plain-language version of a dense notice, produces a first-pass translation for a person to check, and role-plays a hard conversation on demand. The Trevor Project trains crisis counselors against a simulated teenager built this way, and Be My Eyes describes images for blind users with it. Capability has also moved: METR times how long a task a model can finish with 50 percent reliability and finds that horizon doubling on the order of months, and the systems arriving in agencies now call other software and take many steps without checking back, which the agentic systems case below treats.
Topics
- Next-token prediction
- Transformer
- Hallucination and the training objective
- Strengths of the mechanism (e.g., the Trevor Project's crisis-counsellor training)
Readings
- Financial Times. (2023). Generative AI exists because of the transformer (interactive).
- 3Blue1Brown. (2024). Large language models explained briefly (video, ~7 min).
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? FAccT '21.
- Amodei, D. (2024). Machines of Loving Grace. Anthropic's chief executive on what powerful AI would do, written by someone who sells it. Long; the seminar argues the biology forecast, so read that section closely and skim the rest.
Week 5. AI literacy
When a chatbot receives the same health question in English and in Arabic, the Arabic answer is measurably worse. In that study, bilingual experts scored four chatbots on 15 questions about five infectious diseases; English answers averaged 4.6 on a 5-point scale, in the excellent range, while Arabic averaged 4.1. The same drop has been measured for Spanish, on real patient questions about epidurals, where bilingual obstetric anesthesiologists rated Spanish answers significantly lower than English ones, and for Chinese and Hindi across three expert-annotated health question sets. The tool performs worst for many of the people social-impact organizations serve.
Topics
- Calibrated trust
- Ask AI or ask a person
- Language gap (e.g., medical interpreting)
- Error, accountability, and participation (e.g., MiDAS)
- Misinformation and teach-back (e.g., prebunking)
- AI in social work practice (e.g., the NASW technology standards)
Readings
- Campos, H., & Salmi, L. (2025). Critical AI health literacy as liberation technology. NAM Perspectives.
- Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think. Proceedings of the ACM on Human-Computer Interaction. (open copy on arXiv, if the publisher page asks you to verify you are not a bot.)
- Sallam, M., et al. (2024). Language discrepancies in the performance of generative artificial intelligence models. BMC Infectious Diseases.
Week 6. AI adoption in organizations
Randomized trials continue to find that generative AI helps the least-experienced workers most, on exactly the kind of writing work intake involves, and a 2026 RCT found a similar pattern along education lines: with AI access, an education-based productivity gap of 0.548 standard deviations fell to 0.139, closing about three-quarters of it, and participants kept part of the gain in a follow-up round without AI. Automated systems in this same administrative space, including one run by this state, have hurt people at scale.
Topics
- Adopt, defer, or refuse
- Vendors and procurement
- Gap between task and deployment (e.g., the boundary study)
- Adoption as an implementation problem (e.g., NASSS)
- Organizational AI policy (e.g., the NIST AI Risk Management Framework)
- Evidence checks (e.g., the Nigeria tutoring pilot's 0.31 SD)
Readings
- Narayanan & Kapoor (2025). AI as Normal Technology. Knight First Amendment Institute.
- Perron, Goldkind, Qi & Victor (2025). Human services organizations and the responsible integration of AI. Journal of Technology in Human Services.
- Kim, J. Y., & Kesari, A. (2026). How state governments should purchase AI to ensure fair, transparent, and accountable use. Federation of American Scientists. The contract clauses, the risk-tiered review, and the vendor fact sheet.
Week 7. Data sovereignty and privacy
Module 2 asked whether the classification is biased. This week asks who governs the data at all. At the start of class we sort your Case Brief cases by one criterion, whether the affected community could have refused, and almost every harm case (MiDAS, Allegheny, SafeRent) is classified in the no-control column. The conceptual move runs from privacy, an individual defensive right, to data sovereignty, a collective governing right. An individual can consent to a survey, but a community cannot un-consent to what is built from the aggregate.
Topics
- Privacy and data sovereignty
- Networked privacy
- Four power questions (e.g., the Power Chapter)
- CARE and OCAP in production
- Refusal as a justice claim (e.g., Te Hiku Media)
Readings
- D'Ignazio & Klein (2020). Data Feminism, Chapter 1: "The Power Chapter". MIT Press.
- Carroll et al. (2020). The CARE Principles for Indigenous Data Governance. Data Science Journal.
- Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79(1), 119-157. Read to the point where she states the test; the case law that follows is optional.
Week 8. Structure of the technology industry
The people who build a product decide what it does, and some of those roles are open to clinicians and practitioners. Grow Therapy's clinical quality and documentation integrity manager posting accepts a licensed clinical social worker among its eligible credentials and puts that person on the clinical review step for product initiatives, checking that clinical requirements are represented correctly before anything ships. AI governance work has become its own profession, and a 2025 survey by the International Association of Privacy Professionals (IAPP) found 77 percent of organizations building AI governance programs, rising to about 90 percent among organizations already using AI. The function has no established organizational home, splitting mainly across privacy, legal, and IT teams, and only 1.5 percent of the surveyed organizations reported no additional AI-governance staffing needs for the coming year. Supporters argue that changing who holds these roles changes the product before it ships.
Topics
- Product cycles and economics (e.g., Pahlka's Recoding America)
- Role map
- Product roles and governance roles (e.g., the AI governance certification)
- Labor behind the product (e.g., content moderation in Nairobi)
- Worker organizing (e.g., the Tech Workers Coalition)
- Grantor-grantee relationship (e.g., the Silicon Valley Community Foundation)
Readings
- Yoo, Watkins, Perron, Kane & Smith (2026). Building and Governing AI Systems: Advancing Social Workers' Roles across the Technology Industry, Human Service Organizations, and Policy Institutions. arXiv 2608.04273. Read the sections on the technology team and on social workers as technology decision-makers, and both figures.
- The Trevor Project & Google.org (2021). How AI helps volunteers support LGBTQ youth in crisis.
- Muldoon, J., Cant, C., Wu, B., & Graham, M. (2024). A typology of artificial intelligence data work. Big Data & Society, 11(1). Read the six institutional types and the pipeline; the fieldwork detail is optional.
- Matias & Epps (2025). Beware of OpenAI's grantwashing on AI harms. Tech Policy Press.
Week 9. Digital maturity
A technology recommendation made without an assessment of the organization's own capacity is a guess with a budget attached. This week is that assessment, and it has four parts: who owns technology inside the organization, what the data is held in, which of the minimum safeguards are in place, and what the system costs to operate after the grant that bought it is spent. The Strategy Pitch launches on it, and the single interview at the center of that memo is the instrument for taking the read in one conversation.
Topics
- Self-assessment instruments (e.g., NTEN's Tech Accelerate)
- Accidental techie
- Data infrastructure (e.g., Data Orchard's Data Maturity Framework)
- Minimum safeguards (e.g., CIS Implementation Group 1)
- Total cost of ownership
- Realist evaluation
- Semi-structured interview
Readings
- NTEN. Tech Accelerate.
- NTEN (2025). Data Empowerment Report (PDF). Over 220 nonprofit respondents on records management, collection, consent, and who enters the data. Read the records management and informed consent sections.
- Center for Internet Security. CIS Critical Security Controls, Implementation Group 1. The safeguards only.
- NTEN (2025). Equity Guide for Nonprofit Technology (2025 ed., PDF).
Week 10. Digital storytelling
Madden, Harrison, and Vafeiadis argue that nonprofit fundraising works better, and more ethically, when it rests on relational care: treating the storyteller as a partner in an ongoing relationship instead of a means to a donation. Their ethics-of-care model treats fundraising as relationship-building and extends the same dialogue-based obligations to donors, employees, beneficiaries, and volunteers. On that argument, final edit belongs with the person on screen, because the exposure is entirely theirs and the organization's incentives, a stronger ask, a longer video, a better shot, pull against theirs. An experiment on poverty portrayals found that negative, deficit-framed depictions measurably lowered viewers' ratings of the subject's own agency, an editorial choice an organization under fundraising pressure is tempted to make.
Topics
- Story circle
- Public narrative
- Storytelling as organizing (e.g., photovoice)
- Poverty depictions (e.g., Africa For Norway)
- Consent and labor
- Voice and listening (e.g., Dreher)
Readings
- Lambert, J., & Hessler, B. (2018). Digital Storytelling: Capturing Lives, Creating Community (5th ed.), Ch. 7, "The Story Circle." Routledge. Chapter via Taylor & Francis.
- NonProfit PRO. Ethical Storytelling: A Guide for Nonprofits.
- Candid. The Hidden Labor Behind Nonprofits' Authentic Storytelling.
Week 11. Production and accessibility
The Digital Story is due next week, and today is the last supervised build time. Holding a video back until it is fully accessible has a real cost: auto-generated captions run roughly 90 percent accurate, cost nothing, and reach a viewer now, while a video held back until its captions are perfect reaches no one, and a systematic review of assistive technology in education finds real, measurable inclusion gains once tools are actually used, imperfect or not. The same evidence base includes a warning about what is never measured: a systematic review of 11 randomized trials of digital health interventions for children in underserved and rural settings found improved outcomes alongside a near-total absence of cost-effectiveness or equity-disaggregated analysis.
Topics
- Accessibility law (e.g., Section 508)
- Automatic captions
- Production lanes (e.g., DaVinci Resolve)
- Accessibility overlays
- Imagery ethics (e.g., Stella Young on inspiration porn)
Readings
- Young, S. (2014). Inspiration porn and the objectification of disabled people. Disability Visibility Project. The talk that named the pattern, by a disabled writer.
- W3C Web Accessibility Initiative. Captions/Subtitles.
- Section508.gov. Video and Other Synchronized Media. section508.gov
Week 12. Design justice, digital equity, and environmental costs
Design justice, digital equity, and the environmental cost of computing are usually taught as separate subjects, and this session takes them together because a plan written for a real organization has to answer all three. The federal money behind digital inclusion has been withdrawn: the Digital Equity Act, the largest federal investment in digital inclusion ever made, was terminated in May 2025 with the money unspent in most states, and the litigation to restore it is still running. The Affordable Connectivity Program had already lapsed in June 2024 after serving more than 23 million households with $30 to $75 monthly internet subsidies, and the Congressional Research Service weighed the remaining alternatives, FCC Lifeline at $9.25 a month, VA telehealth-connectivity programs, and ISP low-cost plans, and concluded that no single one would fully replace it.
Topics
- Design justice
- Toyama's amplification thesis
- Three levels of the divide
- Federal retreat (e.g., the Digital Equity Act programs)
- Environmental numbers (e.g., Google's own measurement)
- Infrastructure ownership (e.g., municipal broadband)
Readings
- Costanza-Chock, S. (2020). Design values: Hard-coding liberation? Chapter 1 of Design Justice.
- Toyama, K. (2016). Amplifying inequality. Not Evenly Distributed, Medium.
- McClain, C., & Bishop, W. (2026). What we know about internet use, smartphone ownership and digital divides in the U.S. Pew Research Center.
- Benton Institute. (2026). One year without the Digital Equity Act.
- Ren, S., & Luers, A. (2025). The real story on AI's water use, and how to tackle it. IEEE Spectrum.
Week 13. Strategy pitch briefings
Before class
- Any slides you are using, uploaded to Canvas the night before as backup
- A one-page printed leave-behind for the instructor
- Your AI-disclosure line
- Your one specific feedback ask
- A speaking part for both of you