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@article{194570,
        author = {Ayutee Arun Parange and Anvi Sharma and Vaibhavi Panchal and Dr. Arundhati Niwatkar and Sheetal Mhatre},
        title = {Human-centered and Accountable AI for Digital Knowledge Platforms: A Case Study of TBC},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4836-4842},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194570},
        abstract = {The increasing integration of artificial intelligence into digital knowledge and reading plat-forms has significantly transformed how users discover, organize, and consume information. While AI-driven personalization improves accessibility and engagement, excessive automation and opaque recommendation mechanisms intro-duce concerns related to user autonomy, transparency, algorithmic bias, and long-term trust. These challenges highlight the need for design approaches that prioritize responsible, accountable, and human-centered AI.
This paper presents a human-centered frame-work for AI-assisted digital knowledge plat-forms, emphasizing user agency, explainability, and accountable system behavior. The frame-work is demonstrated through a partially implemented reading management prototype that integrates transparent recommendation logic with user-controlled personalization features. Rather than replacing human decision-making, the system is designed to support informed user choices while mitigating risks associated with over-personalization and algorithmic dominance.
An exploratory qualitative evaluation involving a small group of approximately 10–30 student users was conducted to assess usability, interpretability, and perceived trust. The findings indicate that incorporating explainable mechanisms and user-in-the-loop controls enhances user confidence, promotes transparency, and encourages balanced content exploration.
This work contributes to the discourse on re-sponsible and regulated AI by illustrating how human-centered design principles can be operationalized in early-stage digital knowledge plat-forms. The proposed approach demonstrates that accountable AI systems can support personalization while preserving user autonomy and ethical alignment.},
        keywords = {Human-Centered AI, Responsible AI, Recommendation Systems, User-Centric Design, Explainable AI, Digital Knowledge Platforms, AI Governance, Ethical AI},
        month = {March},
        }