AI-102-paul.wilmoth-2026-08-20-01-12-13
Title of Course [Ethics, Safety, and Responsible AI]
Prefix and Number [AI 102]
Semester and Year [tbd]
Number of Credit Hours [3]
Prerequisites [None]
Course Details
Day and Time: [tbd]
Meeting Location: [tbd]
Instructor Contact Information
Instructor Name: [tbd]
Instructor Contact Information: [office location, phone, email] [tbd]
Instructor Office Hours: [click here for best practices] [tbd]
TA Name: [tbd]
TA Contact Information: [office location, phone, email]: [tbd]
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Course Description
AI 102 introduces the ethical, safety, and societal foundations needed to participate responsibly in the design, evaluation, deployment, and use of artificial intelligence. Students learn to view AI as a socio-technical system whose outcomes depend on people, institutions, data, models, interfaces, incentives, and deployment contexts. The course combines major approaches to ethical reasoning with practical responsible-AI methods.
Topics include stakeholder and value analysis; benefits and harms; data ethics, consent, privacy, provenance, and intellectual property; bias, discrimination, accessibility, and fairness; transparency, explainability, uncertainty, and documentation; human agency, cognitive offloading, automation bias, and human oversight; generative-AI reliability, misinformation, synthetic media, and human-AI relationships; safety hazards, misuse, security, dual use, red teaming, monitoring, and incidence response; professional responsibility, accountability, labor and environmental impacts, ethics washing, and whistleblowing; and major responsible-AI principles and risk-management frameworks.
Through case studies, structured debates, short writing, evidence-based labs, and a team risk-and-impact audit, students practice making and communicating defensible recommendations. The emphasis is on asking the right questions, distinguishing evidence from assertion, documenting limitations, and matching safeguards to the people, context, and severity of potential harm.
Note: The recently proposed PHIL 2XX AI Ethics syllabus provided a strong starting point for the idea for this course through its attention to cognitive offloading, ethical theories, black-box and alignment problems, responsibility gaps, professional responsibility, human-AI relationships, and ethics washing. This course, AI 102, retains these strengths while broadening the safety, security, generative-AI documentation, and governance components expected of a modern responsible-AI foundation. AI 102 is intended to substitute PHIL 2XX and will be cross-listed as AI 102/PHIL 102.
Course Materials
Books: No single textbook is required. Selected chapters or excerpts maybe drawn from Shannon Vallor's Technology and the Virtues, Cathy O'Neil's Weapons of Math Destruction, and other instructor-selected scholarship. Readings will be provided through Canvas, the WSU Libraries, or openly accessible sources.
Core frameworks and professional resources: NIST AI Risk Management Framework 1.0 and its Generative AI Profile; OECD AI Principles; UNESCO Recommendation on Ethics of Artificial Intelligence; ACM Code of Ethics and Professional Conduct; and relevant WSU guidance on academic integrity, AI use, and data stewardship.
Other Materials: Canvas; a web browser; spreadsheet software; instructor-provided case packets, system/model cards, data documentation, incident reports, and audit worksheets. Selected low-risk demonstrations may use institutionally available generative-AI tools or instructor-provided outputs. Students will not be required to purchase a commercial AI subscription or disclose sensitive information to an AI system.
Fees: None beyond standard course materials; no paid AI subscription is required.
|
Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
| 1. Explain AI as a socio-technical system and identify relevant actors, stakeholders, values, benefits, risks, and power relationships across its lifecycle (4a, 6e). | Lifecycle mapping; stakeholder workshops; case discussion; concept mapping | Readiness notes; case analyses; midterm; audit context map |
| 2. Apply ethical theories and principles -- including consequences, duties and rights, justice, care, and virtue -- to analyze AI dilemmas and justify a defensible position (4c, 4e). | Mini-lectures; structured debates; argument mapping; writing workshops | Two position briefs; debate contribution; midterm case analysis |
| 3. Analyze how data practices and AI systems can affect privacy, autonomy, fairness, accessibility, human rights, labor, communities, and the environment (4a, 6e). |
Data-provenance lab; fairness lab; impact cases; stakeholder testimony |
Applied labs; position brief; audit impact analysis |
| 4. Evaluate claims about AI trustworthiness using evidence about data, performance, subgroup impacts, uncertainty, transparency, explainability, documentation, and system limitations (3d, 6b, 6c, 6e). |
Evidence critique; model-card lab; output comparison; documentation review |
Applied labs; midterm; audit evidence and limitations section |
| 5. Distinguish safety hazards, security vulnerabilities, misuse, and systemic risks, and propose proportionate prevention, oversight, monitoring, and incident-response measures (4a, 6e, 6f). |
Hazard analysis; misuse-case workshop; introductory red team; incident review |
Safety lab; case analysis; audit risk register and mitigations |
| 6. Use responsible-AI principles and the NIST AI RMF functions -- Govern, Map, Measure, and Manage -- to conduct a scoped AI risk-and-impact assessment (4e, 6b, 6e, 6f). |
Framework crosswalk; audit studios; peer review; consultation role-play |
Framework exercise; team Responsible AI Risk-and-Impact Audit |
| 7. Communicate responsible-AI decisions to technical and nontechnical audiences, disclose and cite AI assistance appropriately, and reflect on human agency and professional responsibility (3b, 3c, 4f). |
Position-brief workshops; peer feedback; disclosure practice; professional dilemma discussion |
Position briefs; audit presentation; individual reflection |
| Dates | Lesson Topic | Assignment or Activity | Assessment |
|---|---|---|---|
|
Week 1 |
What AI is; socio-technical systems; lifecycle; stakeholder and values | AI system and stakeholder map | Diagnostic reflection |
| Week 2 | Ethical reasoning; consequences, duties and rights, justice, care, and virtue | Argument-mapping workshop | Reading notes 1 |
| Week 3 | Benefits, harms, power, professional responsibility, and responsible innovation | Current-case discussion | Position Brief 1 begins |
| Week 4 |
Human agency, cognitive offloading, automation bias, academic integrity, and disclosure | Personal AI-use protocol | Position Brief 1 |
| Week 5 |
Data ethics: consent, privacy, provenance, intellectual property, and stewardship | Data documentation lab | Applied Lab 1 |
| Week 6 |
Bias, discrimination, fairness tradeoffs, accessibility, and inclusion | Subgroup-impact and fairness lab | Applied Lab 2 |
| Week 7 |
Transparency, explainability, uncertainty, limitations, and model/system cards | Evidence and documentation lab | Applied Lab 3 |
| Week 8 |
Integration of ethical reasoning, data, fairness, privacy, and evidence | Guided case review | Midterm applied case exam |
| Week 9 |
AI safety: hazards, failure modes, alignment, robustness, human oversight, and safe failure | Hazard-analysis workshop | Audit scope and system selection |
| Week 10 |
Security, misuse, dual use, red teaming, monitoring, incident response | Bounded red-team and safeguard lab | Applied Lab 4; stakeholder/value map |
| Week 11 | Generative-AI reliability, misinformation, deepfakes, provenance, and information integrity | Source-verification workshop | Audit evidence inventory |
| Week 12 |
Human-AI relationships, persuasion, well-being, vulnerable users, labor, and environment | Stakeholder hearing role-play | Position Brief 2; audit risk register |
| Week 13 |
Responsibility gaps, professional codes, whistleblowing, ethics washing, and accountability | Incident and accountability review | Audit migration and governance plan |
| Week 14 |
NIST AI RMF, OECD and UNESCO principles, impact assessment, governance, and assurance | Framework crosswalk; audit studio | Complete draft and peer review |
| Week 15 |
Responsible deployment decisions, residual risk, communication, and course synthesis | Team audit presentations | Audit presentation |
| Finals Week | Responsible-AI synthesis | Final audit report and individual reflection | Final project submission |
Expectations for Student Effort
For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class. Note that Global campus courses will automatically include credit hour equivalents in the syllabus.
Grading
| Assessment Category | Description | Weight |
|---|---|---|
| Reading readiness and case preparation | Short questions, concepts checks, and evidence-based preparation for discussion | 10% |
| Two ethical-reasoning position briefs | Structured arguments applying ethical frameworks to contested AI decisions | 20% |
| Four practical responsible-AI labs | Data documentation, fairness/accessibility, transparency/evidence, and safety/information-integrity exercises | 20% |
| Midterm applied case examination | Individual analysis integrating ethics, stakeholders, data, evidence, and mitigation | 15% |
| Team Responsible AI Risk-and-Impact Audit | Milestone-based audit report, risk register, framework mapping, recommendations, and presentation | 30% |
| Individual project reflection | Contribution, learning, disagreement, disclosure, and professional-responsibility reflection | 5% |
| Total | 100% |
| Grade | Percent | Grade | Percent |
|---|---|---|---|
| A |
93 -- 100% |
C | 70 -- 76.99% |
| A- | 90 -- 92.99% | C- | 67 -- 69.99% |
| B+ | 87 -- 89.99% | D | 60 -- 66.99% |
| B | 83 -- 86.99% | F | less than 60% |
| B- | 80 -- 82.99% | ||
| C+ | 77 -- 79.99% |
Attendance and Make-Up Policy
Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-Fail the assignment or exam in which the cheating happened
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.