Territori 17: Machine learning and ethics
Machine learning is a branch of artificial intelligence on which applications such as ChatGPT are based. Ariadna Font, a leading international authority in machine learning and ethics, talks to us about the possible applications of these new technologies and the ethical challenges they pose.
The emergence of artificial intelligence through applications such as ChatGPT, developed by OpenAI, is changing our lives and revolutionising many jobs. The evolution of machine learning aims to develop algorithms and systems that can learn and make predictions from data or from interaction with users. “It allows computer systems to automatically improve their performance in a specific task through experience,” says Font.
This technology is being used in various fields such as health, security, commerce, robotics and industry to automate processes, perform financial operations, detect fraud and diagnose diseases. However, their use also raises a number of ethical challenges that need to be considered.
If algorithms are trained on data reflecting societal biases and inequalities, the results can be unfair to specific individuals or groups and lead to discrimination. This is of particular concern in contexts such as recruitment, law enforcement and lending, where prejudice can have a significant impact. As Font explains, “machine learning models can make important decisions that affect our lives, so it is very important to consider what effects these decisions have on us”.
On the other hand, machine learning can put people’s privacy and security at risk. For example, the data used to train the algorithms may contain personal and sensitive medical information, and if not properly protected, it can be stolen or misused.
To address these ethical challenges, it is important for designers and developers of this technology to adopt an ethical perspective and think about the implications for individuals and society at large. Adopting privacy protection practices, considering fairness and equality in data selection or ensuring transparency will be essential to ensure that the ethical and human side is not lost with the implementation of artificial intelligence.
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