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With the increasing implementation of technologies like Artificial Intelligence, Machine Learning and sophisticated data analysis algorithms, the process of decision-making and recommendations is being automated with more emphasis on a trained system to generate valuable required outcomes. Taking into consideration the aspect of how the mechanism of any computer-based decision-making process takes the order, this paper aims at understanding the possibility of bias in any output, which may, further, become a cause for bias in the real world. This paper tries to address a stepwise approach to discovering the factors of how such a bias can be quantified and presents a view of the impact parameters for various stakeholders while trying to process an approach for the overall fairness of the system.
[Rahul Sethi, Vedang Ratan Vatsa and Parth Chhaparwal (2020); IDENTIFICATION AND MITIGATION OF ALGORITHMIC BIAS THROUGH POLICY INSTRUMENTS Int. J. of Adv. Res. 8 (Jul). 1515-1522] (ISSN 2320-5407). www.journalijar.com
Article DOI: 10.21474/IJAR01/11418
DOI URL: http://dx.doi.org/10.21474/IJAR01/11418
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