{"id":4166,"date":"2026-02-18T11:25:42","date_gmt":"2026-02-18T10:25:42","guid":{"rendered":"https:\/\/pharos390.com\/?p=4166"},"modified":"2026-02-18T16:35:49","modified_gmt":"2026-02-18T15:35:49","slug":"ai-without-criteria-is-merely-artificial","status":"publish","type":"post","link":"https:\/\/pharos390.com\/en\/ai-without-criteria-is-merely-artificial\/","title":{"rendered":"AI without criteria is merely artificial"},"content":{"rendered":"\n

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The expansion of artificial intelligence (AI)<\/strong> in professional environments is significantly altering the relationship between people, technology and decision-making. As AI<\/strong>-based systems become integrated into increasingly complex processes, the debate is gradually shifting from the ability to automate tasks to how the results <\/strong>produced by these technologies are governed<\/strong>. In this scenario, the approach known as human in the loop <\/em>is establishing itself as an important reference point for understanding how human intervention<\/strong> is articulated in AI<\/strong>-assisted systems.<\/p>\n\n\n\n

The concept of human in the loop <\/em><\/strong>is based on a simple idea, but one with profound organisational implications: AI<\/strong> can generate highly efficient results, but it requires human supervision<\/strong> to ensure that those results translate into appropriate decisions. The person does not appear as an accessory element of the system, but as the one who provides interpretation, judgement and responsibility. This logic is particularly relevant in contexts where decisions are not limited to mathematical optimisation, but incorporate contextual factors that are difficult to model (JAMES 2026).<\/p>\n\n\n\n

Recent literature warns of one of the main risks associated with the intensive use of artificial intelligence<\/strong>: the uncritical acceptance <\/strong>of the results <\/strong>generated by the systems. When AI<\/strong> outputs are perceived as technically sound or statistically accurate, there is a tendency to assume they are valid without sufficient analysis of their assumptions, limitations or possible biases. According to the McKinsey Global Institute (2024), this phenomenon is not due to a lack of technological capacity, but rather to a lack of human skills geared towards interpreting, questioning and contextualising the information produced by AI<\/strong>.<\/p>\n\n\n\n

In this context, critical thinking <\/strong>takes on a central role. The professional profiles capable of adding value in environments augmented by artificial intelligence are those who know how to ask the right questions, detect inconsistencies and understand that a technically correct result may not necessarily be appropriate for a specific decision. The use of AI<\/strong> does not eliminate the need for human judgement; on the contrary, it increases its importance, as it shifts people towards tasks where evaluation and interpretation are decisive (WORLD ECONOMIC FORUM 2025).<\/p>\n\n\n\n

The use of AI<\/strong> puts a fundamental principle at risk: responsibility <\/strong>for decisions <\/strong>remains human<\/strong>. Although systems generate information automatically, the obligation to answer for them remains non-delegable to those who adopt them, hence the need to rigorously verify and validate the information generated. In this context, it is particularly important to establish clear governance and traceability guidelines that clarify the effective authorship of decisions and their basis.<\/p>\n\n\n\n

From this perspective, the development of capabilities related to artificial intelligence<\/strong> cannot be approached solely from a technical standpoint. The most effective upskilling<\/em> strategies are those that understand the adoption of AI<\/strong> as a process of organisational change<\/strong>, in which technology is integrated into workflows and decision-making processes, rather than as an added layer of tools that operate in isolation (DE SMET et al. 2023). This approach reinforces the idea that training should include not only the use of AI <\/strong>systems, but also an understanding of their impact on decision-making.<\/p>\n\n\n\n

The digital transformation of the logistics-port<\/a><\/strong> sector is advancing in a highly complex context, where organisations with diverse functions and processes converge and interact along the same supply chain ( ). The adoption of artificial intelligence increases the speed and scope of available information, highlighting how each decision can affect multiple links in the chain. In this scenario, critical thinkers become strategic because of their ability to understand interdependent relationships, anticipate effects on overall operations, and contribute to the stability and resilience of the sector.<\/p>\n\n\n\n

Artificial intelligence <\/strong>allows for increasingly higher levels of efficiency, but the real value emerges in the decisions that humans make based on what technology facilitates. Assessing context, reading nuances and considering ethical implications remain areas where human intervention is indispensable. Thus, the ability to apply critical thinking to the results derived from these systems ultimately continues to depend on the irreplaceable <\/strong>soundness of the human judgement <\/strong>that guides them.<\/p>\n\n\n\n

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