Building trust in the age of human-machine interaction: insights, challenges, and future directions

The paper addresses how trust can be established, sustained, and repaired between humans and machines as AI and robotics become embedded in daily life and high-stakes domains. It contrasts trust foundations in human-human relationships—dependability, competence, generosity, and sincerity—with trust drivers in human-robot interaction—transparency, predictability, autonomy/flexibility, user experience, and emotional engagement. It raises core questions about whether and how humans can trust machines, emphasizes the importance of explainability (XAI), and motivates a theoretical framework (TAAM) to align trust-building tactics with domain requirements.

The paper synthesizes decades of research on interpersonal trust (e.g., Mayer et al., Lewicki & Bunker, Rotter, Lewis & Weigert) and meta-analyses on trust in automation and HRI (Hancock et al.; Hoff & Bashir; Schaefer et al.). It reviews evidence that robots can collaborate effectively with humans in search-and-rescue, education, and healthcare, including work showing PPO/GAIL-based robots excel in complex search tasks when trust is calibrated (Kapoor et al.). It discusses XAI as a mechanism for enhancing trust by making decisions understandable (Arrieta et al.; Miller), and surveys studies on emotional expressiveness and social responses to computers/robots (Breazeal; Brave et al.; Nass & Moon; Nandanwar & Dutt). Cultural influences on trust are summarized, highlighting differences across collectivist vs. individualist contexts and preferences for robot communication styles and behaviors (Gelfand et al.; Li et al.; Rau et al.; Złotowski et al.).

This is an opinion/theoretical contribution that conducts a comparative conceptual analysis of trust in human-human vs. human-robot interaction and proposes the Trust-Affordance Adaptation Model (TAAM). The approach involves: (1) mapping interpersonal trust constructs (dependability, generosity, competence, sincerity) to HRI constructs (transparency, predictability, autonomy/flexibility, emotional involvement), (2) integrating empirical and theoretical literature to derive domain-informed judgments about the relative importance of trust affordances, and (3) illustrating TAAM with a conceptual radar chart for defense, healthcare, education, and social robotics. The radar chart values are hypothetical, aggregated from literature, and not based on new experimental data. No primary data collection or statistical analysis was performed; instead, the methodology relies on synthesis, conceptual alignment, and design-oriented guidance for context-sensitive trust calibration.

- Trust in HRI is primarily driven by system properties—transparency/explainability, predictability, autonomy/flexibility, and emotionally engaging user experiences—rather than social familiarity. - Conceptual correspondences link human-human trust to HRI: dependability→transparency, generosity→predictability, competence→autonomy/flexibility, sincerity→emotional involvement. - Trust in HRI is more transactional and sensitive to failures; small errors can disproportionately reduce trust, necessitating real-time trust calibration and repair. - Cross-cultural differences strongly impact trust expectations and preferred robot behaviors (e.g., collectivist contexts valuing relational behaviors; individualist contexts emphasizing autonomy/control), motivating culturally adaptive HRI. - Psychosocial factors (prior experiences, biases, personality, sociocultural context) and physiological/behavioral sensing (e.g., GSR, thermography, eye-tracking) are promising for estimating and adapting trust but face methodological challenges. - TAAM posits that the prominence of trust affordances is domain-dependent: transparency/predictability dominate defense; emotional engagement/personalization are crucial in healthcare, education, and social robotics. - Future systems should incorporate context-sensitive XAI, adaptive personalization, and biosensor-driven feedback loops for dynamic trust recalibration.

The paper argues that addressing trust in HRI requires aligning trust-building mechanisms with domain-specific needs. Context-sensitive XAI can mitigate opacity in high-stakes decisions, improving user understanding and confidence. Integrating psychosocial factors and physiological sensing can enable systems to estimate user trust and adapt interactions in real time, though sensor noise, individual baselines, and construct specificity present challenges. TAAM offers a flexible framework to prioritize trust affordances (transparency, predictability, emotional engagement, personalization) according to context, enabling robots to balance functional robustness with emotional and cultural intelligence. This addresses the central question of how humans can trust machines by tailoring trust tactics to situational demands and user profiles.

The paper contributes a conceptual mapping between interpersonal and HRI trust factors and introduces TAAM, a framework advocating context-dependent prioritization of trust affordances. It underscores the need for explainability, reliable performance, emotional engagement, and cultural adaptability to foster trust in AI/robotic systems. Recommended future directions include: developing context-sensitive XAI that adapts level and timing of explanations; conducting cross-cultural studies to inform culturally adaptive robot behaviors; advancing real-time trust estimation via multimodal biosensors and sensor fusion; and designing algorithms for proactive trust repair and recalibration in volatile environments.

- Opinion/conceptual nature: no new empirical data; TAAM radar chart values are hypothetical and literature-informed rather than experimentally validated. - Biosensor-based trust estimation faces methodological challenges (signal noise, context dependency, individual variability, difficulty isolating trust from related states like stress/engagement, need for longitudinal calibration). - Current computational models often underrepresent dynamic, emotional, and socio-cultural dimensions of trust; culture-specific frameworks remain underdeveloped and need translation to practice. - Generalizability across domains and cultures requires empirical validation of proposed mappings and adaptations.