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 hong kong telegram data 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.
Legal perspective
The legal landscape around AI is still evolving. Currently, in many countries, there are no specific laws that directly address liability for decisions made by AI, leaving a grey area in accountability issues. In general, tort and contract laws still apply, meaning that companies implementing AI are often ultimately liable when a problem occurs.
In the European Union , stricter regulations on AI are being developed, most notably with the proposed Artificial Intelligence Act (AI Act), which aims to establish a clear legal framework for the use of AI, especially in high-risk areas such as health and human rights. These types of regulations seek to assign more concrete responsibilities to both developers and companies implementing AI, establishing measures to ensure transparency, security and fairness in algorithmic decisions.
As AI continues to evolve, the need for an appropriate legal framework will be crucial to protect users and ensure that errors or abuses do not go unpunished.