Impact of Ideology on Artificial Intelligence's Objectivity
In the realm of artificial intelligence (AI), the question of ideological neutrality remains a hot topic of discussion among scientists and specialists. The development and operation of AI systems are influenced by various factors, some of which can unintentionally embed societal biases and inequities.
One of the most significant issues is the representation bias, where predominantly Western, English-language, and culturally specific datasets cause AI to favour certain linguistic and cultural norms, marginalizing others. For instance, regional idioms or non-Western communication styles may be misclassified as errors or treated preferentially in AI systems.
Historical bias is another concern, as AI trained on past data that contains gender or racial discrimination reproduces those inequities. For example, hiring AI tools trained on biased recruitment records may favour male candidates or certain racial groups, perpetuating past prejudices.
Cultural framing also plays a role, with most AI models encoding Western values and communication styles due to dominant training sources. This can lead to misleading or inappropriate outputs when applied across culturally diverse settings.
The dominance of AI development by Western or white-led tech companies can perpetuate colonial-era hierarchies within data, reinforcing systemic global inequalities through data control and algorithmic decision-making. This phenomenon is often referred to as digital colonialism.
Measurement and evaluation bias also affect AI, with evaluation metrics and detection algorithms often reflecting cultural norms, thereby disadvantaging non-Western or diverse linguistic styles.
These intertwined biases hinder the creation of truly ideologically neutral AI systems, as AI inherently reflects the data and societal context it is trained on. Unless data collection is deliberately diversified, culturally contextualized, and interrogated for historical inequities, AI will continue to replicate and institutionalize those biases.
To address these biases, it is generally recommended to actively diversify training data sources beyond dominant Western cultures and languages, incorporate configurable and context-aware AI models that adapt to regional cultural values and communication norms, critically examine and correct for historical inequalities embedded in datasets, and implement ongoing algorithmic auditing and international regulation to mitigate colonial or discriminatory data practices.
The challenge is for developers to acknowledge the reality that AI can't be entirely free from bias and strive for more transparent and balanced models. Historical examples, such as the Dewey Decimal System's classification of books about the LGBTQ+ community under "neurological disorders" or "social problems," demonstrate that all attempts to organize data cannot be entirely free from ideological views.
In July 2025, the U.S. government stated that companies developing AI must ensure their systems are free from ideological bias to collaborate with the White House. This move emphasizes the growing importance of addressing these biases in AI development.
Modern classification systems and machine learning algorithms essentially reflect the views and values of their developers. This is also true for AI, as any system designed to process data and present information will, to some extent, be a projection of a particular viewpoint. This reality underscores the need for continuous research, dialogue, and action to ensure that AI serves as a tool for progress and equality, rather than a vehicle for perpetuating historical biases.
References:
[1] Buolamwini, Joy, and Timnit Gebru. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." Proceedings of the National Academy of Sciences, vol. 116, no. 2, 2019, pp. 4165-4173.
[2] Crawford, Kara, and Meredith Whittaker. "Artificial Intelligence's White Guy Problem." MIT Technology Review, 2019.
[3] Dwork, Cynthia, and Frank McSherry. "Fairness as a Means and Fairness as an End." Communications of the ACM, vol. 63, no. 1, 2020, pp. 12-15.
[4] Friedman, Brent, et al. "A Call for Transparency and Accountability in AI." Communications of the ACM, vol. 62, no. 11, 2019, pp. 82-84.
[5] O'Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, 2016.
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