Open-Source AI Algorithms: A Qualitative Study on Transparency, Bias Mitigation, and Ethical Accountability
Year: 2025
Type: article
Abstract: Artificial Intelligence (AI) algorithms are increasingly deployed in high-stakes domains, yet their opaque and proprietary nature often perpetuates bias and undermines ethical accountability. This paper investigates open-sourcing as an approach to mitigate these challenges by enhancing transparency, fostering collaborative development, and enabling independent auditing. The literature review identifies critical issues in existing proprietary Open-source AI frameworks, including algorithmic bias, limited transparency, and ethical shortcomings, and assesses how open-source initiatives address these concerns. Employing qualitative methods, including case studies, thematic analysis, and a comparative evaluation of open-source versus proprietary models, this study reveals significant benefits of open-source AI, such as improved fairness, transparency, and increased public trust. However, it also highlights critical challenges, including intellectual property rights, potential misuse, and technical complexity. The paper concludes that open-sourcing, while beneficial, necessitates standardized auditing frameworks, robust governance policies, and broad stakeholder participation to ensure ethical integrity and accountability in AI development.
DOI: 10.63680/hgcfnnh854
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4409725458