Artificial intelligence is no longer a technology working in the background; it is an invisible decision-making mechanism that affects our loan applications, hiring processes, education and healthcare. When we talk about such power, we have to ask the question: Is it really possible to remain human-centered when developing AI, or is this just a fancy concept?
Human-centered AI is not simply about “user-friendly” interfaces. It is essentially about seeing technology as a means, not an end, and putting human rights, needs and well-being at the center. When designing a system, it is important to ask not only “How can this problem be solved with data?” but also “How will this solution affect whose lives, who will be empowered, who will be left behind?” In other words, you need to start with the human story, not the code. Today, algorithms for recruitment, credit, education or social assistance can deepen existing inequalities if they are not carefully designed. Biases in historical data can be carried over into the model; certain age groups, genders or socioeconomic groups can be systematically disadvantaged. This is why human-centeredness is also a matter of justice and inclusion. Our view of data is also critical here. Instead of seeing data as “fuel” or “oil”, we need to treat it as a trust. Clearly explaining what data we collect and why, not collecting more than necessary, and caring about security and privacy as much as efficiency are the foundations of trust.
Of course, this process cannot be left to software developers alone. It is imperative to establish transparent, explainable and open to objection systems where lawyers, psychologists, sociologists and real users are also involved in the process. In short, human-centered artificial intelligence is not a technical detail; it is a choice related to corporate culture, management approach and ethical stance. When this choice is made, AI can go from being just a tool to speed things up to the infrastructure of a fairer and more reliable digital future.
