We must not lose sight of the fact that decisions that affect people must ultimately be human.
Artificial intelligence ( AI) has become a key element in decision-making across multiple sectors. From financial systems that approve or deny loans to algorithms that optimize medical diagnoses, AI is present in increasingly crucial aspects of our daily lives. Its ability to process large amounts of data in record time has transformed the way organizations operate and how individuals interact with technology.
However, as we delegate more responsibilities to these systems, an inevitable question arises: Who is responsible when decisions made by an AI fail or have unexpected consequences? Algorithms, after all, spain telegram data have no morals and cannot be judged legally. So where does responsibility lie when an algorithmic error affects a person or an entire society? This dilemma leads us to explore a terrain where ethics, law and technology intertwine.
The role of AI in decision making
Artificial intelligence has been gaining ground in areas where decisions previously depended solely on human judgment. Sectors such as finance, health , and human resources have adopted AI-based solutions to improve efficiency, reduce costs, and make faster and more accurate decisions.
In the financial sector, for example, AI algorithms are able to analyse credit profiles in a matter of seconds, assessing the risk of granting a loan based on large volumes of data. This has allowed financial institutions to process applications more quickly, but has also raised concerns about possible errors or algorithmic discrimination , such as denying loans to certain population groups.
In the healthcare field, AI is used to diagnose diseases, recommend treatments or detect patterns in medical images that might otherwise go unnoticed by doctors. A notable example is the use of AI to analyse mammograms or MRIs, where the aim is to detect early signs of cancer. Although this technology has proven to be a valuable ally, errors or biases in the algorithms could lead to incorrect or delayed diagnoses, with serious implications for patients.
In human resources, AI is revolutionising recruitment processes. Automated recruitment systems filter through thousands of CVs and professional profiles, suggesting candidates based on key skills or experience. However, this poses the risk of unconscious biases being perpetuated in algorithms, affecting diversity and equal opportunities in the workplace.
As we rely more on these automated decisions, it’s critical to understand the limits and risks of AI. A mistake in a medical diagnosis or an unfair denial of a loan can have significant consequences, which leads me to wonder: who should be held accountable for these mistakes?
Who is responsible?
When an AI-based decision fails or causes a problem, identifying who is responsible is not as straightforward as it might seem. Unlike human decisions, where accountability can be clearer, the automated nature of AI blurs the lines of responsibility . There are several actors who could be involved, but the question remains: who should take responsibility when something goes wrong?
On the one hand, algorithm developers play a crucial role. They design the systems, set the decision parameters, and are ultimately responsible for how the software behaves. A programming error or poor design can lead to incorrect results. However, developers often work under guidelines provided by the companies that commission the use of AI, which further complicates the assignment of responsibilities.
Companies that implement these systems also have a share of responsibility. By choosing to use AI instead of human intervention in critical processes, these organizations assume the associated risks. For example, if a company decides to rely on an algorithm to manage hiring, it must ensure that the system is free of bias and complies with labor regulations. Ignoring these aspects can lead to both legal repercussions and damage to its reputation.
Finally, end users, who interact directly with AI systems, have a degree of responsibility for how they use the recommendations or decisions provided by these systems. However, their ability to influence outcomes is limited, as they typically lack direct control over how the algorithms operate.