Nansukusa, YudayaKalyankolo, Umaru2025-01-232025-01-232024-12-31Nansukusa, Y., & Kalyankolo, U. (2024). Towards a framework for bias prevention to ensure open data quality. International Journal of Innovative Research & Development, 13(12). https://doi.org/10.24940/ijird/2024/v13/i12/DEC24028.2278 –0211https://dir.muni.ac.ug/handle/20.500.12260/721The rapid growth of open data initiatives has emphasized their potential to enhance transparency, foster innovation, and support equitable decision-making across sectors. However, the quality and reliability of open data remain compromised by biases that alter outcomes and spread inequalities. This paper critically examines the systemic sources of bias, including sampling, annotator, and algorithmic biases, that undermine data integrity and decision-making processes. It proposes a comprehensive framework to mitigate these biases through standardized data management protocols, inclusive data collection practices, robust data stewardship, and cross-sector collaboration. The study also highlights the ethical imperatives and practical challenges of bias prevention, emphasizing the need to balance inclusivity with privacy and resource constraints. By prioritizing fairness, inclusivity, and dependability, the proposed interventions aim to enhance the credibility and societal impact of open data, reaffirming its role as a catalyst for equitable innovation and policy development. The findings underscore that addressing biases in open data is not only a technical necessity but also a moral imperative essential for sustaining its transformative potential.enBias preventionAlgorithmic biasData stewardshipData integrityEquityArticle