Generative artificial intelligence (GenAI) suffers from several types of biases that reflect human failings. How can we avoid being tricked?
What exactly is "trustworthy artificial intelligence"? For now, there's no clear, universally accepted definition, although the European Union's AI Act goes one step in this direction with its seven principles of trustworthy AI: human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental well-being; and accountability.
Applying these principles is much easier said than done, however, especially when biases come into play - whether ideological, political, religious, race- or gender-based, or more generally cognitive. "Biases are nothing new - as humans, we're full of them," says Olivier Crochat, the head of EPFL's Center for Digital Trust (C4DT). "Generative AI should enable to bring this issue up, both for people who develop the programs and those who use them."
"Biased output from a model could end up being used to train other models, thus compounding the bias," he says. In other words, algorithms trained on biased data will merely reproduce the distortion. And because these algorithms are increasingly used for such things as making hiring decisions, reviewing mortgage applications and performing facial recognition, they could have a direct impact on peoples' lives. The EU AI Act specifies that, to be truly trustworthy, a GenAI program must not only be transparent and secure, but also designed to detect and correct biases.
