Governing Artificial Intelligence: Power, Responsibility, and Human Choice

Hanna Barakat  & Archival Images of AI + AIxDESIGN / https://betterimagesofai.org  / https://creativecommons.org/licenses/by/4.0/

Artificial intelligence does not transform society on its own. What truly shapes the future are human choices: the data we collect and value, the goals we set, the economic incentives we accept or reject, and the ethical priorities we choose to defend. Every AI system is a mirror of our decisions—explicit or implicit. For this reason, governing AI is governing ourselves. We are not managing machines; we are managing human choices. We are deciding what kind of society we want to be as these technologies become increasingly present. We are talking about power, responsibility, and values.

It is essential to reject the idea that there is a “technological solution” to all social problems. Issues such as inequality, sustainability, inclusion, justice, or even democratic cohesion are not solved with algorithms, but with policies, public deliberation, strong institutions, and collective responsibility. All technology, including AI, is embedded in real socio-economic systems that involve power asymmetries, structural failures, interests, and conflicts. There are no technological shortcuts to structural problems. There is no technology that can, by itself, fix what is fundamentally political or economic; these challenges require informed human choices, institutional courage, and sustained social commitment. AI can help, but it cannot replace human action, nor resolve for us the tensions, priorities, and dilemmas that define life in society.

At the same time, the way we use AI introduces new risks. We live in a culture that idolizes efficiency: doing more faster, producing more quickly, automating whenever possible. The promise of generative AI fits perfectly into this narrative—delegating cognitive tasks, producing content in seconds, reducing human effort. But we need to ask: what kind of efficiency is this? If efficiency simply means reducing the time needed to produce texts, images, or other outputs, perhaps we are gaining. But if it means replacing the intellectual effort that creates depth, rigor, discernment, and critical reflection, then what we lose is far greater than what we gain.

We must also remember that, by definition, large language models produce results or automatisms that are plausible, but not necessarily true. When we become accustomed to machines writing, summarizing, or even deciding for us, we risk disengaging from issues and losing our capacity for critical thinking. We are not merely facing a technical challenge; we are facing an epistemological crisis. This cognitive erosion is not a minor technical issue—it is a social and democratic risk. A society that thinks less is more vulnerable to manipulation, more permeable to disinformation, and more dependent on synthetic content that simulates consensuses that do not exist. Today, it is not only content that can be fabricated, but also reactions to content—comments and public sentiment. This creates an illusion of social debate that never actually occurred. And when we can no longer distinguish what is human from what is artificial, what is deliberated from what is fabricated, the very notion of the public sphere becomes unstable.

For this reason, the first question we should ask is not “how should we use AI?”, but “why use AI here?” And above all: “for what purpose?” This is the fundamental question—what we call Question Zero. Before questioning the tool, we must question the need. Before seeking efficiency, we must seek purpose. Technology should serve clear, legitimate, and socially valuable human ends. It should not be adopted simply because it is possible, available, or demanded by the market.

Answering Question Zero forces us to consider social impact, risks, alternatives, and, above all, values. It requires us to recognize that not everything that can be done should be done; that not everything should be automated; that not all problems require AI; and that not all applications bring real benefits. This responsibility is not only ethical; it is political.

When I say that AI is not neutral, this is not an abstract philosophical claim. It is very concrete: every design decision—what data to include, which metrics to optimize, which risks to accept, which values to prioritize—is a political decision. Algorithms need rules, but there are many ways to define and represent them. Responsible data governance does not mean storing everything that has ever been created or sensed. The current dominant approach is not an inevitability; it is a choice—and, I would dare say, a rather lazy one. The belief that it is inevitable is part of a narrative of power.

AI is a technology that reconfigures power. Those who control data control part of reality. Those who define models define how that reality is interpreted. Those who determine incentives determine the direction of technology. Governing AI is governing these systems of power; it is governing ourselves. And that requires being able to explain not only how systems work, but why they exist, for whom they were created, and who bears their costs—economic, human, and environmental.

Because we cannot ignore impact. At present, AI depends on energy, water, materials, mineral extraction chains, and physical infrastructure. Digitalization is not immaterial. Behind every system there is also human labor: data annotators, content moderators, invisible workers who perform repetitive and emotionally draining tasks so that we can “play” with chatbots as if everything were automatic and magical. These are people who work long hours, under precarious conditions, to train, clean, and correct what models cannot do on their own.

Responsible AI also means this: the duty to integrate environmental and social objectives from the outset—reducing emissions, avoiding excessive consumption, ensuring circularity, protecting those who work in AI’s invisible supply chains, and aligning innovation with planetary limits and global justice. Technology can help address the climate and social crisis, but it can also worsen it. Responsibility always begins in design and intention, never as an afterthought.

Finally, we must talk about trust. A society only progresses with technology when it trusts that technology to serve the public interest. Trust does not come from promises; it comes from practices—practices of transparency, participation, scrutiny, and accountability. Governance and ethics are not the opposite of innovation; they are its fundamental condition. Clear, predictable, and values-driven governance allows innovation to occur in a sustainable, fair, and meaningful way. Governance is innovation.

For AI to contribute to more just, more democratic, and more sustainable societies, we need three things:

• human resilience, to keep questioning and thinking critically;

• strong institutions, capable of regulating, guiding, and holding actors accountable;

• citizen participation, to ensure that technology reflects collective priorities rather than narrow interests.

Artificial intelligence will not decide the course of society. We will. We can allow AI to amplify inequalities, consume resources without limits, and weaken democracy—or we can use it to reinforce human dignity, support sustainability, and strengthen public debate. There are no inevitabilities. There are choices.

And that requires the courage to slow down when necessary, to ask why before asking how, to resist the temptation of easy efficiency, and to build critical resilience in people and institutions. True innovation does not lie in making systems faster or more powerful. It lies in building societies that are more responsible, more transparent, and more aligned with the common good—capable of steering technology toward what truly matters.

The future of AI will be whatever we choose it to be.