Bias Correction - Definition and Explanation

Bias Correction – Definition and Explanation

Bias Correction

In the realm of Diversity, Equity, and Inclusion (DEI), understanding and addressing Bias Correction is crucial. Bias Correction is a significant aspect of this discourse, but what exactly does it entail?


Listen to this article

Free DEI Research Briefings


Bias correction refers to the process of identifying and rectifying biases within systems, processes, or decision-making frameworks to foster fair and equitable outcomes. 

Why Bias Correction Matters in DEI:

In fostering diverse and inclusive environments, it’s imperative to acknowledge and address biases that may exist within organisational structures or societal frameworks. These biases, whether conscious or unconscious, can perpetuate inequalities and hinder opportunities for marginalised groups. Bias correction acts as a proactive measure to mitigate these disparities and promote fairness and equity for all individuals.

Understanding Bias Correction:

It encompasses various strategies and techniques aimed at identifying, mitigating, and eliminating biases. This process involves:

  • Awareness and Recognition: The first step in bias correction involves acknowledging the presence of biases within systems or decision-making processes. This requires a deep understanding of how biases manifest and impact outcomes.
  • Data Analysis: Data plays a pivotal role in identifying biases. Through rigorous analysis, organisations can uncover patterns or disparities that indicate the presence of bias.
  • Implementation of Corrective Measures: Once biases are identified, organisations can implement corrective measures to address them. This may involve revising policies, restructuring processes, or providing training to mitigate biases.
  • Continuous Monitoring and Evaluation: Bias correction is an ongoing process that requires constant monitoring and evaluation. Organisations must regularly assess their systems and practices to ensure that biases are effectively mitigated and equitable outcomes are achieved.


Consider a recruitment process where historically, candidates from certain socio-economic backgrounds have been disproportionately favoured over others. To address this bias, an organisation implements bias correction measures such as blind recruitment, where identifying information such as name, gender, or educational background is removed from applications before they are reviewed. Additionally, the organisation provides unconscious bias training to hiring managers to raise awareness and mitigate any implicit biases that may influence their decision-making. As a result of these measures, the recruitment process becomes more equitable, providing equal opportunities to all candidates regardless of their background.


Bias correction plays a pivotal role in advancing Diversity, Equity, and Inclusion (DEI) initiatives by addressing biases within systems and processes. By fostering awareness, implementing corrective measures, and continuously monitoring outcomes, organisations can create environments that promote fairness and equality for all individuals. As DEI continues to gain prominence, bias correction remains an indispensable tool in building inclusive societies and workplaces.


Cordeiro, G. M., & McCullagh, P. (1991). Bias correction in generalised linear models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 53(3), 629-643.

Cortes, C., Mohri, M., Riley, M., & Rostamizadeh, A. (2008, October). Sample selection bias correction theory. In International conference on algorithmic learning theory (pp. 38-53). Berlin, Heidelberg: Springer Berlin Heidelberg.

MacKinnon, J. G., & Smith Jr, A. A. (1998). Approximate bias correction in econometrics. Journal of Econometrics, 85(2), 205-230.

Be impressively well informed

Get the very latest research intelligence briefings, video research briefings, infographics and more sent direct to you as they are published

Be the most impressively well-informed and up-to-date person around...

Powered by ConvertKit
Like what you see? Help us spread the word