Amidst the global race to adopt the latest technologies, smart systems and artificial intelligence emerge as promising tools for governments, carrying with them the bright promise of unprecedented efficiency, objective decisions based on big data, and the liberation of public administration from the burdens of slowness and bureaucracy. This technological allure, despite its attractiveness and its genuine potential to bring about immense improvements, may conceal profound and serious consequences if reliance on it shifts from “empowerment” to blind “delegation.” The over-reliance on smart systems in government decision-making is not merely a technical update; it is a fundamental transformation that touches the nature of authority, the concept of accountability, and the very foundations of the citizen-state relationship, necessitating a critical analytical pause to understand and proactively manage these consequences.
The first and most impactful of these consequences on the essence of public service is the erosion of human discretion and practical wisdom. Government work, especially at points of direct contact with the public, relies not only on the strict application of rules but also requires flexibility, empathy, and an understanding of contexts and exceptional circumstances that no algorithm can fully comprehend. An over-reliance on a system that makes automated decisions based on specific inputs negates the public servant’s ability to use their judgment to assess a unique human situation or to apply the spirit of the law rather than its literal text. This can lead to decisions that are technically “correct” but are unjust or illogical in human reality.
As a trainer in the field of artificial intelligence and education, I witness daily how AI facilitates work, providing useful tools that help educators and learners alike to achieve and grow. Nevertheless, I firmly believe that technology should be a tool to support human endeavor, not a replacement for human experience and judgment. The most profound impact is realized when artificial intelligence converges with human discretion.
Following this is the fallacy of absolute neutrality and the risk of systemic algorithmic bias. It is tempting to believe that decisions made by a machine are purely objective and free from human biases. However, this view ignores the fact that AI systems are trained on historical data, which is often a reflection of pre-existing societal biases. Consequently, a system trained on imbalanced historical data may reproduce and amplify those biases on a massive scale and at immense speed, but this time under a veneer of false technological legitimacy. This can lead to systemic discrimination against certain groups in sensitive areas such as employment, social services, or even criminal justice.
As these systems grow in complexity, the “black box” dilemma emerges, creating an accountability vacuum. In many deep learning models, it becomes extremely difficult, even for the programmers themselves, to explain the precise reason that led to a specific decision. When a citizen is harmed by a decision from an opaque smart system, the fundamental principle of accountability in good governance is lost. Who is responsible? Is it the programmer, the entity that provided the data, the employee who implemented the system’s recommendation, or the decision-maker who adopted the system in the first place? This ambiguity undermines a citizen’s right to understand the reasons for decisions affecting their life and to challenge them effectively.
In the long term, over-reliance threatens the atrophy of vital human skills within the government workforce. When employees and leaders become accustomed to receiving ready-made recommendations from smart systems, their capacity for critical thinking, independent analysis, complex problem-solving, and creative thinking may gradually decline. This “de-skilling” creates a fragile administrative apparatus that is heavily dependent on technology it may no longer fully understand or be able to challenge. This leaves the institution brittle and less capable of managing unexpected crises or even questioning a flawed algorithmic recommendation.
Finally, interacting with a machine for fateful decisions negatively impacts the nature of the relationship between the state and the citizen. The feeling that important life decisions (such as eligibility for support or assessment of a violation) are made by a cold, impersonal algorithm can generate a sense of alienation, procedural injustice, and helplessness, even if the final outcome is correct. Part of the legitimacy of government action stems from fair procedures, human communication, and the ability to provide understandable justifications—aspects that can be eroded by excessive automation.
Confronting these consequences does not mean rejecting technology, but rather requires adopting an ethical governance approach that keeps humans in control. The solution lies in the “Human-in-the-Loop” model, where smart systems function as powerful decision-support tools for the public servant, not as their replacement. The recommendations of AI must be interpretable and explainable, and employees must be trained not only on how to use these systems but on how to critically evaluate their outputs, challenge them, and override them when necessary based on their wisdom and experience.
In conclusion, the true innovation in future governments lies not in replacing human judgment with artificial intelligence, but in creating a sophisticated synergy that combines the speed and analytical power of the machine with the wisdom, insight, and empathy of the human. Over-relying on smart systems may seem like a shortcut to efficiency, but it carries the inherent risk of undermining the essence of public service and democratic accountability. Therefore, the goal must be to use technology to empower public servants and provide them with better insights, not to absolve them of their fundamental responsibility to make wise and just decisions that serve the public good.
