Ensuring ethical integrity and bias reduction in machine learning models






Algorithmic performance, Bias mitigation, Demographic analysis, Ethical concerns, Task-specific challenges, Machine learning applications.

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  • R. Gomathi Department of Computer Science & Engineering, Bannari Amman Institute of Technology, Sathya Mangalam, Tamil Nadu, India
  • Balaji V Department of Electrical and Electronics Engineering, MAI-NEFHI College of Engineering and Technology, Asmara, Eritrea.
  • Sanjay R. Pawar Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, Mumbai, India.
  • Ayesha Siddiqua Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India.
  • M. Dhanalakshmi Computer Science and Engineering New Horizon College of Engineering, Bangalore, Karnataka, India.
  • Ravi Rastogi Electronics Division, NIELIT Gorakhpur, MMMUT Campus, Deoria Road, Gorakhpur, Uttar Pradesh, India


This research focused on the multifaceted realm of machine learning algorithms, focusing on the pivotal themes of ethical concerns and bias mitigation (Zeba G. et al., 2021). Employing a dual-pronged research methodology, the study first evaluates algorithmic performance across diverse tasks, such as audio transcription, content moderation, and system implementation. The research uses quantitative assessments and visual comparisons to highlight nuanced improvements in algorithmic efficiency and accuracy. The second dimension involves an in-depth analysis of demographic contributions in tasks like image categorization and content moderation. By scrutinizing the geographical distribution of contributors and demographics like age and gender, the study aims to unravel potential correlations between algorithmic effectiveness and contributor demographics. The graphical representations provide valuable visual insights, including bias distribution across categories, evolution over time, and baseline and improved performance comparisons. The findings contribute to the discourse on responsible AI development, emphasizing the need for tailored enhancements and inclusive participant recruitment strategies. Complemented by comprehensive results and discussions, this research methodology lays a robust foundation for addressing ethical concerns and advancing bias mitigation strategies in machine learning algorithms.

How to Cite

Gomathi, R., V, B., Pawar, S. R., Siddiqua, A., Dhanalakshmi, M., & Rastogi, R. (2024). Ensuring ethical integrity and bias reduction in machine learning models. The Scientific Temper, 15(01), 1799–1805. https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.1.31


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