It is rather unfortunate, but unconscious bias has more far-reaching consequences than initially conceived. While these biases are not inherently malicious, the outcomes certainly feel as such, as exemplified by facial recognition technology’s failure to differentiate between people of the same ethnicity. These errors have been attributed to incomplete databases being fed to these algorithms, so while the machine has learned to identify white people it has trouble applying incompatible knowledge to different ethnicities. This problem has extended itself to our automotive safety features, the pedestrian cameras failing to identify black or even wheelchair-bound pedestrians. Logistical errors like this can be fixed with a greater push towards diversity, as having a diverse team allows for them to cover unconscious bias.
To read more and find out about other areas within the industry that need addressing, click here.
No responses yet