Abstract

Recent advances in artificial intelligence have transformed digital systems from narrowly functional tools into autonomous companions capable of emotionally resonant, context-sensitive, real-time interactions. These AI companions offer personalized guidance, socio-emotional support, and interactive coaching, yet they also raise profound ethical tensions concerning autonomy, epistemic dependence, and the erosion of reflective agency. Prevailing design paradigms often prioritize behavioral optimization and personalization while sidelining critical dimensions of user agency and ethical deliberation. Challenging this approach, we propose an alternative: a principled, existentialist framework for the design of AI companions, grounded in the philosophy of Jean-Paul Sartre. This framework foregrounds four interdependent existential values that govern relational interaction: freedom, authenticity, intentionality, and responsibility (FAIR). These pillars constitute the FAIR framework, envisioned as both a critical lens for evaluating existing systems and a generative blueprint for designing ethically attuned AI companions. Rather than directing users, FAIR empowers them, recasting AI companion design as an architecture of ‘relational integrity.’ Freedom is promoted through agency-preserving mechanisms that empower users to modulate the AI’s operational scope. These can include adaptive autonomy thresholds that calibrate the system’s decisional latitude, interruptibility protocols that enable users to pause or redirect interactions, and consent-aware interventions that ensure users retain ultimate control over engagement trajectories. Authenticity is fostered via value-expressive infrastructures that foreground personal meaning and epistemic transparency. Value-mirroring user profiles can support the articulation and reflection of users’ normative orientations, while confidence-calibrated outputs and provenance-tracking tools reveal epistemic uncertainty, allowing users to critically appraise and selectively integrate AI-generated insights. Intentionality is enabled through goal-clarification mechanisms that scaffold user reflection and future-oriented reasoning. These include goal disambiguation dialogues, situated query scaffolding, and semantic modeling engines that help users surface latent priorities, regulate impulsivity, and resolve tensions between immediate preferences and enduring commitments. Responsibility is promoted through accountability-enhancing mechanisms that prompt users to reflect on the ethical weight of their actions and recognize their role in shaping AI behavior. Tools such as consequence-aware feedback, reflective prompts, and attribution cues foster moral agency and support accountable engagement. Future research and development should focus on operationalizing these principles through technically grounded mechanisms and affordances. Dynamic intent-modeling engines could track and adapt to users’ evolving goals; epistemic posture indicators might reveal AI’s confidence and evidence; and embedded “friction points” could indicate ambiguous queries, prompting deliberation instead of algorithmic certainty. Meta-cognitive feedback loops—interactive prompts revealing patterns in user–AI exchanges—help users discern how their inputs influence system behavior, fostering epistemic vigilance and moral reasoning. This research agenda embraces a deliberate trade-off: moving beyond frictionless, convenience-driven interaction toward a more reflective and ethically grounded engagement model. While this shift may disrupt prevailing UI/UX norms that emphasize harm reduction, performance, and seamlessness, it advances a deeper objective—the cultivation of empowered, self-authoring users. Anchored in the Sartrean view, the FAIR framework repositions AI design from passive personalization toward participatory, value-centered co-creation, where true relational integrity becomes the medium through which users achieve authentic self-definition.

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