Algorithms are becoming increasingly prevalent in our lives, from the ads we see on social media to the decisions made by banks and other institutions. However, as algorithms become more widespread, it is crucial to ensure they are not biased or discriminatory through appropriate auditing approaches.
In a recent Harvard Business Review article, the importance of auditing algorithms to ensure fairness and equity is highlighted. While algorithms may seem objective and neutral, they are nevertheless created by humans who are subject to biases and themselves mostly rely on biased data. This can lead to algorithms that perpetuate discrimination.
The resulting consequences of biased algorithms can be severe. For example, an algorithm used by a company for screening job applicants may disproportionately exclude qualified candidates from underrepresented groups, perpetuating inequality in the workplace. Likewise, an algorithm used by a bank to evaluate loan applications may discriminate against certain groups, leading to unequal access to credit.
This topic is also being discussed in academic research. Acccording to a recent article in the Journal of Business Ethics, previous research has also shown that algorithmic decisions can reflect gender bias, which can lead to broad and structural disadvantages for women. This can ultimately cause women to turn to algorithms in the hope of receiving neutral and objective evaluations. In three studies involving over 1,100 participants, the article’s researchers found that unemployed women are more likely to choose to have their employment chances evaluated by an algorithm if the alternative is an evaluation by a man rather than a woman. Overall, the study’s findings suggest the need to provide education for those at risk of being adversely affected by algorithmic decisions, as well as improving algorithmic literacy for evaluators and evaluatees. Managing algorithms ethically in evaluation settings is crucial for ensuring fairness and equity.
The article can be found here.
What’s your opinion on that? Have you had experiences in dealing with algorithms that revealed a certain bias?