An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The Executive Doctorate in Responsible AI Governance at Brookfield School of Computing is engineered to provide deep competency across core computer science, systems design, and targeted domain applications. Students develop fluency in software craftsmanship, distributed architectures, algorithm optimization, and data governance.
Instruction balances rigorous theoretical foundations with practical code repositories, laboratory simulations, and ethical technology review aligned with modern international computing standards.
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC101 | Philosophy of Technical Sciences & Epistemology in Responsible AI Governance | Theory | Examine computational ontology, paradigm shifts, falsificationism and scientific epistemology in Responsible AI Governance. |
| DOC102 | Doctoral Research Methodology & Design Science Research in Responsible AI Governance | Theory | Formulate Hevner's Design Science Research guidelines, rigor vs relevance cycles and doctoral artifact design in Responsible AI Governance. |
| DOC103 | Empirical Research Formulation & Quantitative Inquiry in Responsible AI Governance | Practical | Design formal empirical investigations, controlled computational experiments and statistical hypothesis test suites in Responsible AI Governance. |
| DOC104 | Theoretical Frameworks & Advanced Systems Modeling in Responsible AI Governance | Theory | Deconstruct axiomatic systems models, state transition systems and formal verification in Responsible AI Governance. |
| DOC105 | Computational Experimentation & Statistical Verification in Responsible AI Governance | Practical | Execute Monte Carlo simulations, power calculations and non-parametric statistical tests in Responsible AI Governance using R and Python. |
| DOC106 | Critical Literature Synthesis & State-of-the-Art Analysis in Responsible AI Governance | Theory | Perform systematic literature reviews (PRISMA), meta-analyses and theoretical gap identification in Responsible AI Governance. |
| DOC107 | Systems Epistemology & Architectural Thinking in Responsible AI Governance | Theory | Evaluate socio-technical systems theory, emergence in complex adaptive architectures and structural decoupling in Responsible AI Governance. |
| DOC108 | Research Ethics, Responsible Innovation & Swiss Standards in Responsible AI Governance | Practical | Audit doctoral research protocols against Swiss human research ethics, algorithmic safety and data governance in Responsible AI Governance. |
| DOC109 | Doctoral Research Scoping & Initial Proposal Portfolio in Responsible AI Governance | Theory | Author the preliminary doctoral scoping portfolio defining novel research questions, significance and scope in Responsible AI Governance. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC201 | Advanced Doctoral Proposal Defense & Technical Roadmap in Responsible AI Governance | Theory | Defend the comprehensive doctoral research proposal, methodology and experimental roadmap in Responsible AI Governance before a faculty panel. |
| DOC202 | Empirical Prototyping & Research Artifact Engineering in Responsible AI Governance | Practical | Construct the foundational computational artifact, reference architecture and experimental software testbed in Responsible AI Governance. |
| DOC203 | Advanced Statistical Learning & Model Evaluation in Responsible AI Governance | Theory | Derive generalization bounds, cross-validation asymptotics and high-dimensional estimation theory in Responsible AI Governance. |
| DOC204 | Field Experimentation & Data Collection Methodology in Responsible AI Governance | Practical | Deploy telemetry collectors, empirical logging probes and instrumentation across production testbeds in Responsible AI Governance. |
| DOC205 | Enterprise Systems Integration & Sociotechnical Impact Analysis in Responsible AI Governance | Theory | Analyze organizational adoption dynamics, human-in-the-loop interfaces and sociotechnical integration barriers in Responsible AI Governance. |
| DOC206 | Intellectual Property, Open Research & Technical Dissemination in Responsible AI Governance | Practical | Manage doctoral software patent disclosures, dual-licensing models and open-source scientific repository governance in Responsible AI Governance. |
| DOC207 | Technology Innovation Strategy & Industrial Research Collaboration in Responsible AI Governance | Theory | Structure university-industry research partnerships, industrial consortium agreements and technology transfer in Responsible AI Governance. |
| DOC208 | Mid-Candidature Doctoral Milestone & Progress Review in Responsible AI Governance | Theory | Successfully pass the comprehensive mid-candidature doctoral review demonstrating substantial empirical progress in Responsible AI Governance. |
| DOC209 | Applied Research Colloquium & Working Paper Series in Responsible AI Governance | Practical | Present working research papers, collect expert peer critique and author academic pre-prints in Responsible AI Governance. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC301 | Doctoral Fieldwork & Experimental Artifact Evaluation in Responsible AI Governance | Practical | Conduct exhaustive empirical fieldwork and evaluate the research artifact across multi-organization deployments in Responsible AI Governance. |
| DOC302 | Comprehensive Data Analysis & Computational Verification in Responsible AI Governance | Practical | Perform advanced statistical triangulation, sensitivity analysis and algorithmic verification of empirical data in Responsible AI Governance. |
| DOC303 | Doctoral Dissertation Drafting: Architectural & Empirical Synthesis in Responsible AI Governance | Theory | Draft dissertation core chapters: theoretical foundation, artifact architecture and empirical findings in Responsible AI Governance. |
| DOC304 | Doctoral Dissertation Drafting: Results Validation & Benchmarking in Responsible AI Governance | Practical | Author dissertation comparative benchmark chapters validating novel contributions against state-of-the-art baselines in Responsible AI Governance. |
| DOC305 | Academic Journal Manuscript & Peer Review Preparation in Responsible AI Governance | Theory | Prepare high-impact peer-reviewed journal submissions conforming to top-tier ACM/IEEE editorial standards in Responsible AI Governance. |
| DOC306 | Industry Technology Transfer & Executive Briefing in Responsible AI Governance | Practical | Synthesize academic findings into actionable executive advisories and commercial technology transfer briefs in Responsible AI Governance. |
| DOC307 | Final Doctoral Dissertation Submission & Quality Audit in Responsible AI Governance | Practical | Complete the full academic dissertation manuscript and pass formal institutional originality audits in Responsible AI Governance. |
| DOC308 | Doctoral Oral Defense (Viva Voce) Preparation in Responsible AI Governance | Theory | Master academic rhetorical defense, anticipate committee objections and articulate unique scholarly contributions in Responsible AI Governance. |
| DOC309 | Final Executive Doctoral Defense & Research Dissemination in Responsible AI Governance | Practical | Successfully defend the doctoral dissertation in public viva voce examination and disseminate scholarly findings in Responsible AI Governance. |
Master’s degree in computing or related quantitative discipline with significant technology leadership experience. Research concept proposal required.
Brookfield School of Computing is an independent private education institution in formation. Academic programmes, entry requirements, fees and awards are subject to institutional review and applicable Swiss legal regulations.