Navigating the Debate on AI Bias and Fairness Across Various Sectors

Jay Burgess
3 min readJul 30


Photo by Wesley Tingey on Unsplash

Artificial Intelligence (AI) is fast becoming a buzzword in contemporary society, shaping different sectors, including recruitment, criminal justice, and healthcare. As its use proliferates, it has left many considering its implications. Specifically, AI bias and fairness have become hotly debated topics. Can we trust AI to make decisions less discriminatory than humans? Or does the technology amplify the problem further?

The banking industry has recently heavily integrated AI into its system, especially within credit card and banking apps. The technology sifts through voluminous consumer data, providing financial institutions with predictive models to make prudent decisions. But while the convenience and precision of AI are unmistakable, questions about bias linger. Industry insiders and consumers alike ask: are AI-enabled banking and credit card apps making decisions without prejudice? Can it identify and eliminate human biases encoded in the training data, or does it imprint and extend them?

The translation industry has also seen a surge in the use of AI. Language translator apps now use AI to interpret a vast array of world languages in real time, breaking down barriers and fostering global communication. However, critics argue these applications may embody cultural, racial, and gender biases rooted in the training data.

Similarly, AI advancements in face recognition apps have been groundbreaking and controversial. Initially hailed as the future of technology, face recognition tools have increasingly come under fire for possible racial and gender disparities. The problem lies within the algorithm. Is the AI simply mirroring societal biases, or does the algorithm maintain a level of objectivity throughout?

To fully comprehend the root of AI bias, we must first understand that AI, whether used in recruitment, criminal justice, healthcare, or financial sectors, is only as unbiased as the data it learns from. If the training data is skewed, the AI’s decisions will inevitably reflect that bias. Consequently, to achieve fairness in AI, we need to ensure diversity and balance in the training data, establish strict regulatory standards for AI use, and continually audit AI systems for fairness.

Although we are quick to point out the bias in AI, we must also recognize that humans are inherently biased. AI allows us to quantify and detect this bias and work towards eliminating it. The ability to track, measure, and redress bias gives AI a potential fairness advantage over humans, provided that regulators, designers, and users handle its development and deployment with care.

The discussion around AI bias and fairness is highly pertinent, navigating the complexities that require an open dialogue. This dialogue not only enables awareness but also permits multiple perspectives to come together and approach the issue comprehensively.

The journey of understanding AI bias is one that we should embark on together. As technology evolves rapidly, let’s ensure we grow in our knowledge and understanding of these ongoing debates.

As you delve deeper into AI bias and fairness, I invite you to share your perspectives and join me in further probing this compelling issue. Connect with me, Jay Burgess, on my LinkedIn profile — I eagerly await your thoughts and to engage in rich, insightful conversations about the future of AI.



Jay Burgess

I use data and innovation to help people and companies make better decisions. I also like writing about leadership. #BusinessIntelligence #datascience #ai