The system will almost certainly whatsapp phone number list assign them the “cat” tag incorrectly, resulting in lower performance for this subset of the target population. Taking these factors into account when training artificial whatsapp phone number list intelligence systems based on machine learning is key if we want to avoid algorithmic bias in several ways. Let's look at some examples. On data, models and people A few years ago, I came across, on the recommendation of colleagues, an article entitled « Alien is Sexist and Racist. It's Time to Make it Fair»7[Artificial intelligence is sexist and racist. It's time to make her fair], by James Zou whatsapp phone number list and Londa Schiebinger.
The article discussed an whatsapp phone number list aspect that I hadn't really stopped to think about until that moment regarding the AI models that I was implementing myself: these models can be sexist and racist. In other words, they whatsapp phone number list can acquire a bias that leads them to present uneven performance in groups characterized by different demographic attributes, which results in unequal or discriminatory whatsapp phone number list behavior. And one of the reasons behind this behavior was precisely the data used to train them.
Examples of algorithmic whatsapp phone number list bias acquired through data are varied and often have to do with databases that do not really represent the entire population. In the case reported by Joy Bowlamwini and Timnit whatsapp phone number list Gebru8, in which various commercial facial recognition systems show uneven performance with respect to demographic variables such as gender and skin color, black whatsapp phone number list women are the group for which the models present the worst performance. This fact is possibly related to the lack of representativeness of black women in the databases used for training.