AI Ethics and Vocation
Agentic AI shifts the ethical question from what a model outputs to what a system does on someone's behalf, so a practitioner's accountability now extends to delegated action, machine-produced deliverables, and the data and labor supply chain behind a model. The course keeps its vocation-grounded, case-study identity and adds delegated agency - escalation, guardrails, audit, provenance, and disclosure - as the setting in which that moral reasoning is practiced.
Current description → proposed description
This course explores the ethical, vocational, and societal responsibilities of computing professionals in an age increasingly shaped by Artificial Intelligence and emerging technologies. Grounded in the Christian doctrine of vocation, students examine how their technical work serves both neighbor and society, integrating faith-informed purpose with professional excellence. Students will analyze case studies in data privacy, surveillance, algorithmic bias, intellectual property, cybersecurity, and sustainability, while also grappling with the profound ethical implications of AI development, such as autonomous decision-making, deepfakes, and the use of generative models. Emphasis is placed on cultivating discernment, responsibility, and servant leadership in technology design, deployment, and policy. Through discussion, reflection, and project work, students learn to apply consistent moral reasoning across complex computing contexts, preparing them to lead with integrity in both industry and research settings.
This course explores the ethical, vocational, and societal responsibilities of computing professionals in an age increasingly shaped by Artificial Intelligence and emerging technologies. The course first establishes what is and is not meant by artificial intelligence, weighing semantic, historical, and popular definitions against the autonomy and capability claims made for commercial systems. Grounded in the Christian doctrine of vocation, students examine how their technical work serves both neighbor and society, integrating faith-informed purpose with professional excellence. Students will analyze case studies in data privacy, surveillance, algorithmic bias, intellectual property, cybersecurity, and sustainability, while also grappling with the profound ethical implications of AI development, such as autonomous decision-making, deepfakes, and the use of generative models. As production systems are increasingly built and operated with AI coding agents and tool-calling agents that act on a user's behalf, the course extends this inquiry to delegated action: accountability for machine-produced work, training-data provenance, consent, and annotator labor, human-in-the-loop escalation, guardrails and prompt-injection risk, audit logging, and the energy and access costs of deployment. Emphasis is placed on cultivating discernment, responsibility, and servant leadership in technology design, deployment, and policy. Through discussion, reflection, and project work, students learn to apply consistent moral reasoning across complex computing contexts, preparing them to lead with integrity in both industry and research settings.
What changes
- Delegated agency: accountability for what an AI agent does on a user's behalf
- Training-data provenance and consent, intellectual property, and annotator labor
- Human-in-the-loop escalation, guardrails, prompt injection, and audit logging treated as ethical controls
- Energy, water, and economic-access costs of deployment framed as stewardship
- Disclosure of which portions of professional work were machine-produced
6 proposed outcomes, mapped to 5 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to evaluate a vendor's or employer's claims about an agentic system's autonomy and capability against semantic, historical, and popular-cultural definitions of artificial intelligence, differentiating demonstrated from claimed behavior in a written brief for a non-technical decision-maker.
Weighing a capability claim against semantic, historical, and popular definitions of artificial intelligence is precisely the definitional discrimination PLO 5.2 requires, and rendering that judgment as a written brief a non-technical decision-maker can act on is the clear, responsible communication of AI concepts to a non-technical audience named in PLO 5.1.
Students will be able to evaluate a deployed AI or agentic system against criteria of bias and fairness across affected populations, transparency, privacy and data provenance, accountability for autonomous action, and economic access, producing a written assessment that names each harm, who bears it, and what remedy is owed.
The criteria in this CLO - bias and fairness, transparency, privacy and provenance, accountability, economic access - restate the ethical and legal integrity outcome point for point, and naming who bears each harm and what remedy is owed supplies the accountability evidence; the workbook states this one outcome twice in identical words, as PLO 1.1 under Christian Faith and again as PLO 3.2 under Integrated Disciplinary Knowledge, so a single assessment satisfies both entries.
Students will be able to design an accountability structure for an agentic system that acts on a user's behalf, specifying human-in-the-loop escalation thresholds, audit logging and observability, guardrails against prompt injection and tool misuse, and a named human owner for every autonomous decision, justifying each control against the specific harm it is meant to prevent.
Escalation thresholds, audit logging, guardrails, and a named owner for every autonomous decision are the mechanisms by which transparency and accountability are implemented rather than merely asserted, which is what this outcome asks of an AI system; the workbook carries that outcome in two places under identical wording - PLO 1.1 beneath Christian Faith and PLO 3.2 beneath Integrated Disciplinary Knowledge - so the work that earns one earns the other.
Students will be able to articulate, from the Christian doctrine of vocation, an account of the AI practitioner's calling as neighbor-serving work marked by professional humility and care for the common good, applying that account to a concrete decision to build, modify, or decline to build a system.
The account is drawn from the doctrine of vocation and stated in the outcome's own terms - service to others, professional humility, care for the common good - and applying it to a decision to build, modify, or decline a system is what makes the vocational understanding in PLO 1.2 demonstrable rather than nominal.
Students will be able to communicate the capabilities, limitations, residual risks, and provenance of an AI system, including which portions of a deliverable were produced by AI coding or writing agents, in both a technical disclosure and a briefing for a non-specialist audience such as a board, a patient group, or a regulator.
Producing both a technical disclosure and a non-specialist briefing on the same system, ethical considerations included, is the dual-audience clarity PLO 5.1 demands, while disclosing residual risk and the machine-produced portions of a deliverable is the transparency and accountability required by the ethical and legal integrity outcome - an outcome the workbook records twice in identical language, as PLO 1.1 under Christian Faith and as PLO 3.2 under Integrated Disciplinary Knowledge, so both are credited by the same evidence.
Students will be able to critique the data and labor supply chain behind a generative model, including training-data provenance and consent, intellectual property, annotator compensation, energy and water cost, and unequal access to the resulting capability, defending a position on what a Christian understanding of stewardship and vocation requires of the practitioner who deploys it.
Provenance and consent, intellectual property, annotator compensation, and unequal access to the resulting capability are the privacy, economic-access, and potential-harm concerns of the ethical and legal integrity outcome, which the workbook lists word for word in two locations, as PLO 1.1 under Christian Faith and as PLO 3.2 under Integrated Disciplinary Knowledge; defending what a Christian understanding of stewardship and vocation requires of the deploying practitioner is the worldview-guided responsible innovation and care for the common good described in PLO 1.2.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all twelve.
The workbook maps this course too
The proposal workbook's assessment map already assigns program learning outcomes to this course. The outcomes above were written to cover it.
| Program outcome | Workbook level | In this draft |
|---|---|---|
| PLO 1.1 Ethical and Legal Integrity in AI | D,AE | covered |
| PLO 1.2 Vocation and Christian Worldview | D,AE | covered |
| PLO 3.2 Ethical and Legal Integrity in AI | D,AE | covered |
| PLO 5.1 Communicating AI to Any Audience | D,AE | covered |
| PLO 5.2 Defining and Evaluating AI | I | covered |
I = Introduced · D = Developed · AE = Assessed at Exit