Join us on a journey to explore the invisible face of artificial intelligence, and how it contributes to entrenching AI bias instead of erasing it.
Article contents:
- Introduction
- The root of the problem: Historical data
- Practical examples of bias
- The role of social structures
- Toward equitable AI governance
- Hope for change
- Conclusion
The world is witnessing a growing interest in artificial intelligence and the extent of its impact on our daily lives, as it has become an integral part of hiring decisions, targeted advertising, and even security risk assessments. Despite rapid technological developments, several studies have emerged indicating the presence of AI bias. In a recent study involving a group of researchers led by Professor Tuba Berkan, it was clarified that AI bias is not only due to technical shortcomings, but is closely linked to societal structures and a history of discrimination rooted in the data upon which it is trained.
The root of the problem: Historical data
Many focus on the importance of data cleansing and improving algorithmic models to reduce AI bias. However, the new study confirms that the problem runs deeper than that, because it is attributed to artificial intelligence relying on historical data saturated with discrimination and bias. When models are trained on this data, they automatically absorb unfair societal patterns, and then work to reinforce and spread them rather than challenging them.
Practical examples of bias
One prominent example mentioned by the study is the recruitment system developed by Amazon. Despite its stated goal of accelerating screening and selection processes, the algorithm was found to favor male candidates over female ones, which reinforced the gender gap in the labor market. Another more dangerous example involves government systems aimed at detecting fraud, where suspicions focused unfairly on immigrant groups or vulnerable populations, leading to false accusations against innocent people. These examples reflect the need to rethink how artificial intelligence technologies are developed and adopted to combat AI bias.
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The role of social structures
The study argues that technology is not isolated from the social and political context; the decisions made by programmers, companies, and regulators play a major role in determining the goals and functioning of artificial intelligence. In other words, algorithms cannot be separated from social frameworks, as an algorithm may reinforce the status quo if it is not designed to detect and work to reduce discrimination. Professor Tuba Berkan emphasizes the necessity for various social actors—including governments, civil societies, and technical experts—to participate in establishing standards for using artificial intelligence in fair and responsible ways to minimize AI bias.
Toward equitable AI governance
The study recommends developing governance policies that ensure transparency and accountability. Responsibility should not be restricted to tech companies alone; it must also include lawmakers, community organizations, and users themselves. Suggested reform steps include auditing data before training models, establishing independent oversight bodies, and involving affected communities in technical decision-making stages. In this way, artificial intelligence systems can be designed to tackle discrimination instead of entrenching it and to reduce AI bias.
Read also: When AI creates the past: Art or misinformation?
Hope for change
Despite significant challenges, the study anticipates the possibility of turning artificial intelligence into a tool for achieving societal progress if ethical and human values are integrated into all stages of development. It points out that the success of this requires close cooperation across different disciplines, such as sociology, psychology, and law, alongside technical experts. It also stresses the need to view past experiences as a catalyst for learning, a way to avoid repeated mistakes, and a means to enhance collective responsibility in dealing with AI bias.
Conclusion
AI bias is not merely a software glitch that can be solved with simple adjustments, but rather a reflection of a broader societal reality. Therefore, it becomes essential for everyone working in the tech field to understand that the products and services they develop carry a historical legacy of discrimination and inequality. Accordingly, this study serves as a call for concerted efforts, clear policies, and strong governance to ensure that artificial intelligence systems become a means to bridge social barriers rather than amplify them.
