A Conceptual Model for Trustworthy Proactive Device Personalization

Engineering notes

Key topics: autonomous driving, prediction, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Proactive device personalization enables digital systems to anticipate user needs and automatically activate contextually appropriate modes, settings, services, or interface configurations. Although predictive personalization can reduce interaction effort and improve accessibility, poorly timed or incorrect actions may interrupt tasks, compromise privacy, weaken user autonomy, and erode trust. Existing research often evaluates predictive accuracy separately from the human consequences of automated intervention, leaving limited guidance for determining when a device should act, request confirmation, provide a recommendation, or remain silent. This paper develops a conceptual model for trustworthy proactive device personalization that integrates machine-learning-based intent inference with human–computer interaction principles. The model connects behavioral and contextual signals, inferred intent, prediction confidence, false-activation cost, action reversibility, user vulnerability, transparency, perceived control, and longitudinal trust. It conceptualizes proactive personalization as a risk-sensitive decision process in which system behavior depends not only on the probability of a predicted intention but also on the consequences of acting incorrectly. The framework distinguishes advisory, confirmatory, reversible automatic, and restricted actions, assigning progressively stronger authorization requirements as potential harm increases. It also incorporates preference learning, explanation timing, feedback capture, uncertainty calibration, consent boundaries, and mechanisms for recovering from erroneous personalization. Examples include automatically enabling driving mode, changing accessibility settings, suppressing notifications during meetings, and adjusting energy-management policies. The paper proposes design principles and testable relationships for evaluating prediction quality, interruption cost, trust calibration, acceptance, controllability, and adaptation over time. By bridging machine learning, responsible automation, and interaction design, the framework provides a foundation for developing proactive devices that are useful without becoming intrusive, opaque, or excessively autonomous in everyday environments.

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