Transparency and Trust in the Age of AI

Artificial intelligence is no longer just a developing promise and has become part of the operational fabric of many industries. In particular, in the field of public safety and security, its impact is already tangible: It enables the processing of large volumes of information in seconds, the detection of patterns invisible to the human eye, and the anticipation of risks before they materialize. However, along with these opportunities, its widespread adoption brings an unavoidable requirement: the need for transparency. Not as a symbolic gesture, but as a structural and sustained practice over time.

The incorporation of AI can transform an organization’s response capacity, expand the scope of data analysis, and reduce the margin of human error in critical situations. But it is also a fact that, in contexts where public safety and trust are at stake, the value of a technological solution does not depend solely on its technical performance, but on the trust it can generate. And trust begins with understanding. How many corporate leaders could truly identify themselves as experts in artificial intelligence? My guess is that there is interest and willingness, but also room for improvement in terms of application and boundaries.

One of the main challenges we face today is how to make AI understandable to all stakeholders. This includes operators in monitoring centers, private companies, public safety authorities, policymakers, and even the citizens who benefit from or are impacted by these technologies. There is a common need: to understand how and why an AI reaches a conclusion.

Automated decisions cannot be unclear, especially when they have implications for safety, privacy, or individual rights. In a command center, for example, it is not enough for a video analytics system to detect unusual behavior. It is essential that the operator understands which indicators triggered that alert and with what level of certainty it was issued. Only then can informed and responsible decisions be made.

This leads us to a central conviction: AI must be explainable. This is not about revealing every line of code or exposing trade secrets, but about providing clarity on the system’s purpose, the data it uses, the criteria it applies, and the degree of human oversight it involves. Applied consistently, this principle changes the way people interact with technology and strengthens the bond of trust between developers, operators, and society.

Clarity is not just a technical requirement, it is an ethical commitment. This means that if an AI system recommends an action, it must be possible to trace the logic behind that recommendation. In the field of security, where decisions can have immediate and far-reaching consequences, this traceability is indispensable.

Transparency also means establishing clear accountability frameworks. AI must not become an excuse to dilute responsibility. On the contrary, those who design, develop, and deploy these systems must commit to being accountable for the implications and results. This includes everything from the ethical management of data — ensuring its quality, relevance, and protection — to effective communication with users and society.

In this regard, manufacturers must ensure that our solutions comply with ethical and regulatory standards; operators must use them correctly and in accordance with protocols; and authorities must establish clear regulatory frameworks that protect people without stifling innovation.

A concrete example of how to put this commitment into practice is the recent incorporation of “nutritional label”-style tags in AI solutions implemented by Motorola Solutions. These labels provide structured and accessible information on key aspects of each system’s operation: what data it uses, how it processes it, what level of human intervention it requires, and what its limitations are.

What matters is not the tool itself, but the logic behind it: enabling understanding, strengthening trust, and ensuring accountability. When an operator knows what to expect from a system and what its real scope is, they can integrate it more effectively into their decision-making process.

These types of initiatives do not arise in isolation. In this case, they are the result of the ongoing work of our Technology Advisory Committee (MTAC), an interdisciplinary space that brings together experts in engineering, ethics, law, public safety, and user experience. This committee helps anticipate risks, assess impacts, and make decisions aligned with ethical and operational principles.

Transparency, understood as a sustained and evolving practice, is one of these guiding principles. It is not about meeting a static checklist of requirements, but about adapting as technology, regulations, and social expectations evolve.

There is a substantial difference between adopting technology and adopting it well. Doing it well means integrating transparency from the design stage, not as a late add-on. It means speaking a clear language, establishing auditable standards, and maintaining a proactive attitude toward regulation and public engagement.

It also means recognizing that AI is not infallible. Like any tool, it depends on the quality of the data it receives, the criteria with which it was trained, and the oversight exercised over its results. That is why transparency is not only an ethical value, but also a condition for continuous improvement.

The evolution of AI will continue to accelerate. We must ensure that our ability to explain, regulate, and improve it grows at the same pace.

In the coming years, we will see AI become even more integrated into critical operations, from emergency management to the protection of large-scale events. Mexico, for example, will host the 2026 World Cup, a global sporting event that will bring together millions of people in its main cities. The magnitude of this logistical and security challenge will require the collaboration of multiple stakeholders and the use of advanced technologies. In that context, trust in AI tools will be as important as their technical capabilities.

The future of AI will be only as trustworthy as the principles that guide it. That is why, beyond innovating, our challenge as an industry is to do so with integrity, responsibility, and openness. The legitimacy of artificial intelligence is not built solely with more sophisticated algorithms, but with practices that put people at the center, protect their rights, and foster an open dialogue about its benefits and risks.

From my perspective, technological innovation and social responsibility are not opposing goals, but complementary. True transformation happens when technology not only solves problems, but does so in an ethical, transparent, and sustainable way.

Only then will we ensure that artificial intelligence, beyond being powerful, is also legitimate in the eyes of the society that adopts it. And only then will it be possible to harness its full potential to build safer, more resilient, and more trustworthy environments.