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VOL. 2, ISSUE 1 (2026)
Bias and fairness in machine learning models: Detection and mitigation
Authors
Dr. Nikita Bahaley, Prathamesh Mane, Atharva Mhatre, Ashwin John
Abstract
In recent years,
intelligent computing systems have become deeply integrated into everyday
decision-making environments. These systems depend largely on previously
collected data, which may contain hidden imbalances. When such patterns are
learned, the system may produce outcomes that unintentionally favor or
disadvantage certain groups. This raise concerns related to fairness and
responsible use of technology. The present study explores how bias appears in
machine learning-based systems and why it is important to address it. A
structured survey involving 50 final-year Information Technology students was
conducted to understand their awareness, opinions, and level of familiarity
with fairness-related concepts. The responses were collected using a scaled
questionnaire to capture different levels of understanding. The study
highlights that improving fairness in intelligent systems requires both technical
knowledge and awareness. Strengthening education in this area can help future
developers build systems that are more balanced, transparent, and socially
responsible.
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Pages:174-176
How to cite this article:
Dr. Nikita Bahaley, Prathamesh Mane, Atharva Mhatre, Ashwin John "Bias and fairness in machine learning models: Detection and mitigation". International Journal of Research in All Subject, Vol 2, Issue 1, 2026, Pages 174-176
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