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Bias Mitigation and Fairness in AI-Based HR Tools

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  • Anshul Shetty

    (Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India)

  • Dr. Shreevamshi N.

    (Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India)

Abstract

Forecasting has long served as a cornerstone of strategic decision-making in financial services. Traditionally grounded in econometric models, statistical inference, and time series analysis, financial forecasting has been employed to anticipate market movements, project economic trends, and guide investment strategies. Early models such as the Autoregressive Integrated Moving Average (ARIMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), and Vector Autoregressions (VAR) formed the bedrock of quantitative finance, offering structured approaches to interpreting historical data and identifying trends. These methods, while rigorous, often rely on assumptions of linearity, stationarity, and normality that may not hold in complex, volatile, and non-linear market environments.

Suggested Citation

  • Anshul Shetty & Dr. Shreevamshi N., 2025. "Bias Mitigation and Fairness in AI-Based HR Tools," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(6), pages 1049-1067, June.
    • Handle: RePEc:bjf:journl:v:10:y:2025:i:6:p:1049-1067
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