Bias and Fairness in AI Models: How can Bias in AI Models be Identified, Mitigated, and Prevented in Data Science Practices?


Authors : Shaik Mohammad Jani Basha; Aditya Kulkarni; Subhangi Choudhary; Manognya Lokesh Reddy

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/55xw7jsy

Scribd : https://tinyurl.com/4rrk35ja

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP789

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Abstract : Artificial intelligence (AI) and machine learning (ML) systems are progressively used in different areas, going with basic choices that influence individuals' lives. In any case, these frameworks can sustain and try and fuel existing social predispositions, prompting uncalled for results. This paper looks at the wellsprings of predisposition in simulated intelligence models, assesses current methods for distinguishing and relieving inclination, and proposes an extensive structure for creating more pleasant simulated intelligence frameworks. By coordinating specialized, moral, and functional points of view, this exploration plans to add to a more evenhanded utilization of computer-based intelligence across various areas, guaranteeing that artificial intelligence driven choices are fair, straightforward, and socially dependable.

Keywords : Artificial Intelligence (AI), Machine Learning (ML), Bias, Fair AI systems, Bias Mitigation.

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Artificial intelligence (AI) and machine learning (ML) systems are progressively used in different areas, going with basic choices that influence individuals' lives. In any case, these frameworks can sustain and try and fuel existing social predispositions, prompting uncalled for results. This paper looks at the wellsprings of predisposition in simulated intelligence models, assesses current methods for distinguishing and relieving inclination, and proposes an extensive structure for creating more pleasant simulated intelligence frameworks. By coordinating specialized, moral, and functional points of view, this exploration plans to add to a more evenhanded utilization of computer-based intelligence across various areas, guaranteeing that artificial intelligence driven choices are fair, straightforward, and socially dependable.

Keywords : Artificial Intelligence (AI), Machine Learning (ML), Bias, Fair AI systems, Bias Mitigation.

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