Computational Dilemmas
Agentic AI has moved the hardest questions in professional computing ethics from the misuse of software to responsibility for work no human typed, making accountability, provenance, disclosure, and duty of care over autonomous systems the live dilemmas practitioners now face. The course keeps its vocation-centered moral framework and applies it to those cases rather than becoming a technical AI course.
Current catalog prerequisites — (CSC 200 or 2000).
Current description → proposed description
This course provides the foundation for professional ethics in the fields of Computer Science and Information Technology. Students are familiarized with the doctrine of vocation and its implications for ethical attitudes, policies and behaviors. Students see their work as a means of service with social responsibilities that go far beyond the immediate legal and business-related requirements of their employer. Relevant moral criteria are presented and applied to contemporary case studies.
Prerequisites: (CSC 200 or 2000).
This course provides the foundation for professional ethics in computer science and information technology, grounded in the doctrine of vocation and its implications for ethical attitudes, policies, and behaviors. Students examine their work as a means of service carrying social responsibilities that extend well beyond the legal and business requirements of an employer. Relevant moral criteria and professional codes, including those of the ACM and IEEE, are applied to contemporary case studies. The course gives sustained attention to dilemmas raised by generative and agentic AI systems now ordinary in practice. Students weigh who is accountable for code, decisions, and content a practitioner did not author, what provenance and disclosure obligations attach to such work, and when a human must remain accountable in the loop. They also examine how consent, privacy, and intellectual property govern the data these systems consume, and how algorithmic bias, labor displacement, and unequal access bear on human dignity. Students defend reasoned positions in writing and in discussion, distinguishing a Christian understanding from competing worldviews.
What changes
- Accountability for work the practitioner did not author
- Provenance, attribution, and disclosure duties
- Consent, privacy, and IP in AI training and tool data
- Bias, labor displacement, and inequitable access
- ACM/IEEE codes applied to agentic-AI case studies
6 proposed outcomes, mapped to 9 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 articulate the doctrine of vocation and its implications for the computing professional's obligations to neighbor, employer, and society.
"The doctrine of vocation" and work understood as calling is the vocation-and-purpose stewardship named in PLO 1.3, while "obligations to neighbor, employer, and society" is the Christian value guiding professional decisions named in PLO 1.2.
Students will be able to analyze contemporary computing and AI case studies against established moral criteria and the ACM and IEEE codes of ethics for their societal impact on affected stakeholders.
"For their societal impact on affected stakeholders" is precisely the critical analysis of ethical implications and potential societal impacts named in PLO 4.1, while judging AI case studies against established moral criteria and the ACM and IEEE codes is the fairness and moral-impact assessment of PLO 6.3.
Students will be able to evaluate, from the doctrine of vocation and its duty to neighbor, the accountability, provenance, and disclosure obligations attaching to code, decisions, and content produced by AI agents that the practitioner did not author, including when human review and escalation are ethically required before such work is relied upon.
Reasoning "from the doctrine of vocation and its duty to neighbor" about provenance, disclosure, and accountability for agent-produced work is the reflection on how Christian values guide ethical decisions about transparency and accountability that PLO 1.2 requires; determining when human review and escalation are ethically required before such work is relied upon is an assessment of an AI development's moral impact in the service of responsible innovation, as PLO 6.3 names.
Students will be able to critique deployed AI systems for algorithmic bias, labor displacement, and inequitable access against the dignity of affected communities.
Judging "algorithmic bias" against "the dignity of affected communities" is the bias avoidance and respect for varied cultural perspectives of PLO 2.2, while "inequitable access" to deployed systems is the equitable access to AI technologies that PLO 2.3 makes the measure of responsible stewardship.
Students will be able to differentiate a Christian understanding of technology and human dignity from competing worldviews advanced in contemporary debates over automation and AI.
Distinguishing a Christian understanding of technology and human dignity from competing worldviews rests on the knowledge of biblical teachings that PLO 1.1 names, explored here for how it informs ethical judgments about AI.
Students will be able to defend a reasoned ethical position, in writing and in oral presentation, before non-specialist stakeholders affected by a computing decision.
Defending a position "in writing and in oral presentation" with transparent reasoning is the written and spoken communication with integrity of PLO 5.1, while addressing "non-specialist stakeholders" is the translation of technical knowledge for non-specialists in PLO 5.3.
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 eighteen.