Designing and Evaluating a Computational Ethic Framework for Autonomous Agents

Thesis Abstract

The rapid deployment of autonomous agents across critical domains raises urgent questions about the moral alignment of computational systems with human values, accountability, and societal norms, highlighting a gap between ethical theory and operational practice in real-world environments. This study addresses the problem of designing a Computational Ethic Framework (CEF) that can be integrated into autonomous agents to govern decision-making, ensure transparency, and support verifiable compliance with normative standards. The aim is to develop, implement, and evaluate a modular CEF that operationalizes ethical theories for real-time reasoning, action selection, and outcome assessment in autonomous systems. Specific objectives are (1) to identify core ethical principles relevant to autonomous agency in healthcare, transportation, and information-ecosystem contexts; (2) to operationalize these principles into formalized rules, multi-agent negotiation protocols, and audit trails; (3) to implement the CEF within a simulated autonomous agent platform and two selected real-world testbeds; (4) to evaluate the framework’s performance using a mixed-methods approach combining quantitative metrics (compliance rate, decision latency, violation frequency, user trust scores) and qualitative feedback (stakeholder interviews, thematic analysis). The methodology employs a design-research approach combining constructive theory-building with empirical validation. The population includes autonomous agents deployed in simulated healthcare triage and autonomous vehicle routing environments, and human stakeholders including clinicians, traffic planners, and end-users. A purposive sample of 60 agent scenarios across three domains and 40 stakeholder interviews will be conducted. Data collection instruments comprise (i) a scenario-logging toolkit to capture ethical decision traces; (ii) a standardized compliance checklist mapped to the formalized rules; (iii) pre- and post-deployment surveys to assess perceived trust and transparency; and (iv) semi-structured interview guides. Validity and reliability will be enhanced through triangulation, pilot testing of instruments (n=6 pilot scenarios), and inter-rater reliability checks (Cohen’s kappa > 0.8 for qualitative coding). The analytical strategy combines quantitative and qualitative methods descriptive statistics and inferential analyses including regression and ANOVA to examine relationships between framework features and outcomes such as compliance rate and response latency; time-series analysis to observe causal effects of policy updates on behavior; and thematic analysis of interview data to unpack interpretive factors shaping stakeholder acceptance. The study adopts the normative theories of deontological ethics (Kantian duty) and utilitarian consequentialism, complemented by virtue ethics and the capability approach to guide rule formulation and decision justification, with a meta-ethical reflection on framework boundaries. A conceptual model will link ethical principle operationalization, decision-generation processes, and auditability, enabling traceable justification of agent actions. Anticipated findings include (i) improved compliance with embedded ethical rules across domains, (ii) measurable gains in transparency and explainability as evidenced by higher audit-score metrics and user trust indicators, and (iii) nuanced domain-specific trade-offs between utilitarian efficiency and deontic constraints, informing dynamic policy adaptation. The study’s contribution to knowledge lies in providing a transferable, verifiable CEF that integrates normative theory with engineering pragmatics, offering a reproducible blueprint for embedding ethics into autonomous decision-making, with a rigorous evaluation framework transferable to future system designs. The main conclusion expects that a modular, multi-layered CEF can reduce ethical violations without unduly compromising performance, given appropriate governance and continual learning loops. Recommendations include the adoption of standardized ethical auditing protocols for autonomous systems, ongoing stakeholder engagement mechanisms, and future work to extend the framework to multi-robot ecosystems and cross-domain regulatory compliance.