who are often wrack! by guilt and other negative feelings, creating a vicious circle in which further errors are more likely to occur. Machine learning models train! on critical data, like the one evaluat! last year by BMC Health Services Research, can accompany health professionals taking decisions about m!ication for elderly patients, and improve precision screening for errors in neo-natal intensive care units. However, notwithstanding the positive results demonstrat! by these promising systems, the WHO has caution! against a rush to automate decision making, which could potentially increase the number of m!ical errors and undermine confidence in artificial intelligence.
“Errare humanum est
Gabriel Ganascia reminds us with a smile, when we mention using artificial intelligence (AI) to eliminate this option allows purchasing in the interaction between humans and complex systems. Algorithms are written by humans, who are fallible, so they may introduce errors or sources of errors be numbers programming, emphasizes the AI expert and president of the CNRS (French National Centre for Scientific Research) ethics committee. Relying on AI to eliminate errors means taking the risk of seeing the AI make other mistakes, in good faith, you might say.
A good example of this was the accident caus!
Uber autonomous car in March 2018, recalls Jean-Gabriel Ganascia. “The AI was not faulty, the programme work! perfectly,” points out the expert, who adds that the self-driving vehicle had been programm! to take into account cyclists and p!estrians but not a p!estrian how to build content marketing in 10 steps a bicycle. It had also been train! to ignore extraneous phenomena such as plastic bags flying into its path, which would otherwise cause it to stop unexpect!ly. So, the model compli! perfectly with instructions it had been given.
but from the programmers who didn’t comprehensively describe all the possible hazards in this situation.