Towards Ethical and Personalized Web Navigation Agents: A Framework for User-Aligned Task Execution

Generative AI has advanced the capabilities of autonomous agents, enabling autonomous execution of complex web navigation tasks that can reshape digital interactions across various domains. Yet, to reach their full potential, these agents must be ethically aligned and personalized to individual user needs-a challenge complicated by privacy concerns and the risk of reinforcing biases. This work introduces a novel framework that enables responsible, user-guided personalization of web navigation agents, ensuring alignment with ethical standards and user preferences. By developing agents capable of perceiving, reasoning, and adapting in alignment with user preferences, this work proposes an approach that transcends generic task execution. Employing a structured representation of user-specific tasks, the agent utilizes interactive and reasoning actions to personalize workflows, adapting responsively to individual contexts. Evaluation through task success metrics and user satisfaction scores further assesses the ethical alignment and utility of personalized interactions. This research lays the groundwork for responsible agents that offer personalized assistance while adhering to ethical and privacy standards, with implications for information retrieval, e-commerce, and other knowledge-intensive applications.