Algorithms quietly shape many of the choices we make every day. We often think of data, AI tools, and digital systems as neutral machines—but in reality, they are built on human decisions, human data, and human assumptions. And because the data we feed them is never complete or perfectly representative, algorithms tend to repeat and even amplify the inequalities already present in society (Noble, 2018). As these systems become more prevalent, particularly in areas such as hiring, finance, and policing, the issue of algorithmic bias is becoming increasingly difficult to ignore.
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Algorithmic Bias in Hiring and AI Interviews
AI-powered hiring tools started gaining popularity between 2016 and 2018. These systems analyse everything from your voice to your facial expressions, trying to predict whether you’re the “right fit” for a role (Hoffmann, 2019). But algorithmic judgment actually begins long before the interview even starts. The moment you upload your CV, automated systems start comparing your information with the profiles of previously successful employees.
A famous example of this went wrong is Amazon’s experimental hiring algorithm. The company trained the system using ten years of past hiring data—data that came mostly from men working in technical roles. As a result, the AI learned to favour male candidates. It began downgrading CVs that included the word “women’s”, like “women’s football team”, and even penalised graduates from women’s colleges. Once Amazon realised the model consistently scored men higher than equally qualified women, the project was abandoned (Dastin, 2018).
This case shows the main issue: if the history is biased, the algorithm will be biased too. For example, if men have historically been hired more often for certain roles, an AI might automatically score male applicants higher (Raghavan et al., 2020). Women with the same level of skill—or even stronger qualifications—may still receive a lower score simply because of what the system has learned from past data.
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Balancing the Benefits and the Risks
There’s no doubt that algorithms make some tasks faster and more efficient. They can process huge amounts of information and help companies save time. But the risks are just as significant. AI cannot understand context, empathy, or intention. It can only work with the data it is given—data that is often incomplete or biased.
People are more than numbers on a spreadsheet or patterns in a database. That’s why algorithmic results should guide decisions, not define them.
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Conclusion
Algorithms are becoming powerful decision-makers in our society, especially in recruitment. But these systems learn from the past, and if the past is unequal, the future they create will be too. Gender bias in hiring shows how easily algorithms can reinforce old patterns of discrimination. While AI offers speed and efficiency, it must be used responsibly. Transparency, regular evaluation, and strong oversight are essential. Ultimately, algorithms should support human judgement—not replace it.
References
Dastin, J. (2018) ‘Amazon scraps secret AI recruiting tool that showed bias against women’, Reuters, 10 October. Available at: https://www.reuters.com (Accessed: [insert access date]).
Hoffmann, A.L. (2019) ‘Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse’, Information, Communication & Society, 22(7), pp. 900–915.
Noble, S.U. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
Raghavan, M., Barocas, S., Kleinberg, J. and Levy, K. (2020) ‘Mitigating bias in algorithmic hiring: evaluating claims and practices’, in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York: ACM, pp. 469–481.
