Ethical Justification of AI-Assisted Deliberation in Multicultural Dialogues

Thesis Abstract

In an era of rapid AI-mediated communication, AI-assisted deliberation across multicultural interlocutors raises critical questions about epistemic fairness, legitimacy, and the ethical justification of mediated consensus. The study investigates how AI-enabled facilitation shapes argumentative quality, inclusivity, and trust in cross-cultural dialogues, and whether such facilitation can be morally justified given responsibilities to autonomy, non-domination, and cultural pluralism. The aim is to articulate a coherent normative framework that justifies or challenges the use of AI in mediating deliberative forums involving participants from diverse cultural backgrounds. Specific objectives include (1) to identify ethically salient mechanisms by which AI facilitators influence participation patterns, argument structure, and perceived legitimacy; (2) to evaluate the extent to which AI-mediated deliberation preserves or undermines epistemic justice for marginalized voices; (3) to examine the alignment of AI decision rules with competing moral theories (utilitarianism, liberal perfectionism, and virtue ethics) and with deontological constraints concerning autonomy and consent; (4) to develop a normative criteria set for evaluating AI-facilitated deliberation in multicultural contexts; (5) to propose governance and design guidelines for ethically justified deployment of AI in public and academic forums. The research adopts a mixed-methods design, integrating normative analysis with empirical investigation. The philosophical component engages theories of epistemic justice (Fricker) and deliberative democracy (Dryzek), alongside cited theories such as Schweitzer’s moral responsibility in automated systems and Floridi’s information ethics. Empirically, the study examines 120 recorded deliberation sessions from an international, multi-lingual online platform using purposive sampling to include participants from at least four cultural regions. Data collection comprises (i) a content corpus of AI-facilitated and human-facilitated sessions, (ii) semi-structured interviews with 40 participants and 12 AI system developers, and (iii) system logs detailing interaction patterns, cueing, and outcome measures. The primary instruments include a validated Ethical Deliberation Scale (EDS) to assess perceived legitimacy, a Cultural Sensitivity Index (CSI) to measure inclusivity, and a Deliberative Quality Metric (DQM) for argumentative coherence and relevance. Triangulation is achieved through cross-method checks, with thematic analysis applied to interview transcripts and session narratives, and regression analyses to identify correlations between AI-facilitated features (turn-taking algorithms, fairness constraints, explanation provisions) and outcomes such as perceived fairness, participation equity, and perceived epistemic status of contributions. A structural equation model tests hypothesized pathways from AI design choices to epistemic outcomes, while a multi-group ANOVA explores differences across cultural groups. The study anticipates finding that AI-assisted deliberation can enhance inclusivity and procedural legitimacy when guided by transparent decision rules, explicit consent models, and culturally aware explanation strategies, but may risk marginalizing minority epistemologies if default fairness constraints disable culturally specific argumentative forms. The expected contribution to knowledge includes (a) a robust normative framework for ethical justification of AI-mediated deliberation in multicultural settings, integrating epistemic justice and deliberative legitimacy with practical governance criteria; (b) empirical evidence on how AI design features influence participation equity and perceived credibility, with implications for responsible AI policy; and (c) methodological advances in combining normative analysis with empirical social science in AI ethics research. The study concludes that ethical justification hinges on transparent governance of AI decision rules, robust consent and control mechanisms for participants, and adaptive interfaces that honor cultural epistemologies without compromising universal standards of fairness and accountability. Recommendations address design guidelines for AI facilitators, governance policies for multi-stakeholder deliberation, and further research into longitudinal effects of AI-mediated dialogue on intercultural understanding and democratic participation.