Ethics-by-Design in AI: A Philosophical Audit Framework

Thesis Abstract

This study addresses the ethical gaps arising in contemporary artificial intelligence (AI) deployment, where rapid technocentric development often outpaces normative scrutiny, potentially embedding value misalignment and accountability gaps into deployed systems. The central aim is to develop a Philosophical Audit Framework (PAF) that operationalizes Ethics-by-Design for AI systems, enabling normative assessment, transparent justification of design choices, and audit-ready documentation for stakeholders. Specific objectives are (1) to articulate a robust set of ethical desiderata grounded in phenomenology of trust, virtue ethics, and computer ethics; (2) to translate these desiderata into audit criteria applicable across domains such as healthcare, finance, and public administration; (3) to integrate a multi-layered audit methodology combining conceptual analysis, empirical stakeholder engagement, and formal normative evaluation; (4) to pilot the framework on three real-world AI systems to assess feasibility, reliability, and robustness; and (5) to propose governance mechanisms that institutionalize Ethics-by-Design in engineering and policy processes. The study adopts a mixed-methods research design, combining normative analysis, case-based evaluation, and empirical inquiry. The population comprises AI systems undergoing high-stakes decision-making within the public and private sectors, with a purposive sample of three case studies a medical imaging triage tool, a credit-scoring algorithm, and a customer-service chatbot deployed in a public utility. From these, 15 subject-matter experts (ethicists, AI engineers, policy makers) and 40 end-users participate in semi-structured interviews and workshops to elicit ethical perceptions, acceptable trade-offs, and trust determinants. Data collection instruments include a normative audit checklist operationalized from virtue ethics and phenomenological concepts, interview guides reflecting moral–psychological dimensions of AI interaction, and system documentation reviewed via the audit interface. Additionally, a designed inventory of ethical constraints is applied to each case, enabling cross-case comparability. Analytical procedures integrate hermeneutic thematic analysis for interview transcripts, using NVivo 12 to identify themes around accountability, explainability, autonomy, beneficence, non-maleficence, justice, and transparency; regression analysis tests the relationship between stakeholder trust indicators and perceived ethical alignment across cases; and a qualitative content analysis of design documentation assesses alignment with the audit criteria. The normative component employs a two-tiered conceptual evaluation (a) deontological and virtue-ethical appraisal of design decisions, and (b) phenomenological analysis of user experience to uncover tacit ethical concerns. A conceptual model is developed to illustrate how design decisions cascade into ethical outcomes, with operational metrics for feasibility, legitimacy, and risk attenuation. Anticipated findings include a) a validated set of audit criteria that effectively discriminate between ethically aligned and misaligned design choices across domains; b) evidence that explicit virtue-ethical justifications and transparent explainability correlates with higher trust scores among end-users; c) demonstration that iterative stakeholder engagement reduces reported ethical risk and improves perceived system legitimacy; and d) practical governance mechanisms, such as auditable design logs and routine ethical red-teaming, that enhance accountability without compromising innovation. The study expects to reveal domain-specific tensions, such as trade-offs between predictive performance and fairness, and delineate thresholds for acceptable risk in different sectors. Contribution to knowledge comprises (1) a novel Philosophical Audit Framework that operationalizes Ethics-by-Design for AI in a way that is auditable, generalizable, and empirically grounded; (2) an integrative methodology combining normative theory, phenomenology, and empirical stakeholder input suitable for postgraduate research and professional practice; and (3) a set of diagnostic tools and governance recommendations that enable organizations to embed ethical reasoning into lifecycle activities, from requirements specification to deployment and post-market surveillance. The conclusion emphasizes the necessity of embedding ethical reasoning within AI design through structured, audit-oriented frameworks that harmonize normative theory with practical governance. Recommendations include institutionalizing mandatory ethical audits at design milestones, creating cross-disciplinary ethics teams embedded within development projects, mandating transparent documentation of ethical justification and risk mitigation, and developing standardized audit reporting formats to facilitate regulatory scrutiny and public accountability.