AI-UG301 — Applied AI, Society & Responsible Use

Institute: Institute for AI Governance, Cybersecurity & Risk Management (IAIGCRM)

Status: Academic model syllabus — v0.1 editorial draft; not accredited or approved by a degree-granting institution

Format: 15 weeks; proposed 3 semester credits; 3 contact hours/week plus independent work

Academic calendar: Instructor to supply dates, location, office hours, accessibility office, institutional policies and official credit-hour equivalence.

Academic standards and responsible AI policy

Students may use AI tools for formative explanation, brainstorming, debugging, or critique where authorized, but must disclose meaningful assistance, verify claims and citations, protect sensitive data, and independently explain submitted work. Fabricated references, undisclosed generated submissions, and submission of code or reasoning students cannot defend violate course expectations. Instructors must offer an equivalent non-AI route when necessary for accessibility, privacy, or equitable access. Research involving human participants, sensitive data, or live systems requires appropriate approvals. All security exercises use owned or expressly authorized environments.

Inclusive teaching and assessment

Use accessible readings, captioned media, reproducible notebooks or non-programming alternatives as appropriate, formative feedback, transparent rubrics, and documented accommodations. Assess conceptual understanding separately from AI-assisted output quality. A final oral defense or live demonstration tests authorship and comprehension.

General grading principles

Each assessment receives a rubric for technical accuracy, evidence, reasoning, reproducibility, ethical awareness, and communication. Late work, attendance, reassessment, academic integrity, and accessibility procedures must be finalized by the adopting institution.

Editorial notes

All readings are indicative; the instructor must verify editions, licensing, links, publication status, and accessibility before adoption. Course credits and prerequisites are proposals rather than institutional approvals.

Course profile

Level: Undergraduate advanced

Prerequisites: Introductory statistics and AI literacy; basic Python or an approved equivalent recommended.

Catalog description: An applied, interdisciplinary course in selecting, prototyping, testing and responsibly using AI systems. Students investigate human-centered requirements, data quality, model evaluation, explainability, fairness, privacy, security and real-world social consequences.

Measurable learning outcomes

1. Translate a real-world problem into a bounded AI use case with alternatives and stakeholder requirements.
2. Construct a small documented baseline and evaluate it against meaningful metrics.
3. Identify and test dataset limitations, distribution shifts, fairness concerns, and uncertainty.
4. Conduct a structured privacy, safety, security and human-oversight assessment.
5. Evaluate explainability techniques and distinguish explanation from causal evidence.
6. Produce a responsible deployment proposal with monitoring, recourse, and a defensible go/no-go recommendation.

Assessment and weighting

Total: 100%. The capstone must include an individually attributable contribution, documentation and a defense.

Detailed 15-week instructional plan

Each week specifies measurable objectives, instructional activities, laboratory or seminar requirements, security integration and assessment evidence. Faculty must assign exact chapters and articles before delivery.

Week 01 — Applied AI project scoping and stakeholder mapping

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of applied ai project scoping and stakeholder mapping. (2) Apply the week's methods in use-case charter and non-ai baseline. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Use-case charter and non-AI baseline. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 02 — Data provenance, consent and documentation

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of data provenance, consent and documentation. (2) Apply the week's methods in dataset datasheet. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Dataset datasheet. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 03 — Exploratory analysis and baseline models

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of exploratory analysis and baseline models. (2) Apply the week's methods in reproducible baseline notebook. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Reproducible baseline notebook. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 04 — Evaluation design and uncertainty

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of evaluation design and uncertainty. (2) Apply the week's methods in metrics and confidence intervals. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Metrics and confidence intervals. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 05 — Fairness concepts and contested definitions

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of fairness concepts and contested definitions. (2) Apply the week's methods in group/subgroup audit. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Group/subgroup audit. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 06 — Explainable AI and interpretability

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of explainable ai and interpretability. (2) Apply the week's methods in shap/lime critique or model interpretation. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: SHAP/LIME critique or model interpretation. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 07 — Privacy engineering and data minimization

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of privacy engineering and data minimization. (2) Apply the week's methods in privacy threat model. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Privacy threat model. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 08 — Midterm design review

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of midterm design review. (2) Apply the week's methods in peer-reviewed design presentation. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Peer-reviewed design presentation. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 09 — Generative AI application patterns and prompt evaluation

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of generative ai application patterns and prompt evaluation. (2) Apply the week's methods in prompt test suite. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Prompt test suite. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 10 — Retrieval-augmented generation and provenance

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of retrieval-augmented generation and provenance. (2) Apply the week's methods in source-grounding experiment. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Source-grounding experiment. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 11 — Adversarial inputs, prompt injection and misuse

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of adversarial inputs, prompt injection and misuse. (2) Apply the week's methods in authorized test cases. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Authorized test cases. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 12 — Human factors, accessibility and oversight

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of human factors, accessibility and oversight. (2) Apply the week's methods in human-in-the-loop workflow. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Human-in-the-loop workflow. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 13 — Social effects: labor, education, misinformation and relationships

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of social effects: labor, education, misinformation and relationships. (2) Apply the week's methods in comparative evidence analysis. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Comparative evidence analysis. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 14 — Impact assessment, incident response and monitoring

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of impact assessment, incident response and monitoring. (2) Apply the week's methods in deployment governance plan. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Deployment governance plan. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 15 — Capstone demonstration and defense

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of capstone demonstration and defense. (2) Apply the week's methods in prototype, model/system card and risk register. (3) Apply security, privacy, human oversight and evaluation controls. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Prototype, model/system card and risk register. A working notebook, test artifact, impact assessment or bounded prototype. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Indicative reading list

• Barocas, Hardt & Narayanan, Fairness and Machine Learning (open text), selected chapters.
• Gebru et al. (2021), “Datasheets for Datasets.”
• Mitchell et al. (2019), “Model Cards for Model Reporting.”
• NIST AI RMF (2023) and Generative AI Profile (AI 600-1, 2024).
• OWASP Top 10 for LLM Applications and selected peer-reviewed HCI/AI-society studies.

Capstone and evaluation evidence

The final submission must state the problem and constraints, method and baseline, data/source provenance, reproducible procedures, quantitative or qualitative evaluation, limitations, ethical and security implications, and evidence supporting conclusions. Include a short individual oral defense. Instructor will publish detailed rubrics and resource/accessibility alternatives before enrollment.

Quality assurance before academic adoption

Faculty peer review; verification of reading accessibility and rights; learning-outcome-to-assessment mapping; workload and credit-hour calibration; disciplinary prerequisites; inclusive pedagogy review; and alignment with the adopting institution's academic governance process.

Curriculum alignment and instructor delivery

This course is part of the proposed IAIGCRM Secure & Governed AI Development Curriculum. It is designed for potential delivery by IAIGCRM faculty or the Institute's founder, subject to final staffing, scheduling, enrollment, institutional agreements and resource availability. No enrollment, credits or accreditation are currently offered by this draft. The graduate integrative capstone is AI-GR551. Security baseline: SEC-01, SEC-02, SEC-03, SEC-04, SEC-05, SEC-07.

Weekly teaching preparation and laboratory quality gate

For each class, prepare slides, instructor demonstration, learner instructions, environment setup, starter files, a marking rubric, accessibility alternatives, and a verified reading packet. Labs must include a learning objective, prerequisites, data provenance, stepwise tasks, tests, expected outputs, failure cases, evidence submission and cleanup procedure. Students may not attack real systems or use non-consensual personal data.