The Challenge of Achieving Neutrality in AI

Artificial Intelligence (AI) systems are built and trained by humans, inheriting biases present in their creators and the data they are fed. Achieving truly neutral AI is a significant challenge as it requires identifying and mitigating a wide range of biases - from the data selection to the algorithms' objectives. While efforts to improve neutrality are underway, complete neutrality is difficult due to the subjective nature of what is considered 'neutral' in various contexts.

Artificial Intelligence (AI) systems are built and trained by humans, inheriting biases present in their creators and the data they are fed. Achieving truly neutral AI is a significant challenge as it requires identifying and mitigating a wide range of biases - from the data selection to the algorithms' objectives. While efforts to improve neutrality are underway, complete neutrality is difficult due to the subjective nature of what is considered 'neutral' in various contexts.

Empowered by Artificial Intelligence and the women in tech community.
Like this article?
Claudia Bibiana Ruiz
Director - Researcher - Lecturer at Innovation Center of Meta Econova at Santo Tomas University

AI reflects the worldviews, biases, and limitations of those who create it because, at its core, AI is a human product. Striving for neutrality isn’t just a technical challenge; it’s an ethical responsibility. We must go beyond refining algorithms and question the very foundations of our datasets, objectives, and definitions of fairness. True neutrality may be unattainable, but that shouldn’t stop us from demanding transparency, accountability, and diverse perspectives in AI development. The future of ethical AI depends on our willingness to challenge the status quo.

...Read more
0 reactions
Claudia Bibiana Ruiz
Director - Researcher - Lecturer at Innovation Center of Meta Econova at Santo Tomas University

I strongly believe diversity in data isn’t just about representation, it’s all about justice. When AI is trained on narrow, biased datasets, it reinforces inequalities instead of reducing them. We need to ask: Whose voices are missing? Whose experiences are undervalued? The answer isn’t simply more data but better data curated with intention, oversight, and a commitment to inclusion. AI should serve all of humanity, not just the privileged few. We must build technology that reflects the full spectrum of human experience.

...Read more
0 reactions
Contribute to three or more articles across any domain to qualify for the Contributor badge. Please check back tomorrow for updates on your progress.

Bias in AI and Algorithms

Interested in sharing your knowledge ?

Learn more about how to contribute.

More articles on Bias in AI and Algorithms

See all

More articles from related categories

How Can Cultural Competence Training Empower Women in Tech?
Cultural Competence Training
What Happens When We Uncover Hidden Biases? A Deep Dive into Unconscious Bias Workshops
Unconscious Bias Workshops
What Role Does Allyship Play in Advancing Gender Diversity in Tech?
Diversity Advocacy
Are We Perpetuating Gender Stereotypes Through Algorithmic Decisions?
Bias in AI and Algorithms