As reported in the Forbes article “The Role Of Bias In Artificial Intelligence”, facial recognition systems are under scrutiny. The class imbalance is a leading issue in facial recognition software. A dataset called “Faces in the Wild,” considered the benchmark for testing facial recognition software, had data that was 70% male and 80% white. Although it might be good enough to be used on lower-quality pictures, “in the wild” is a highly debatable topic.

Apart from algorithms and data, researchers and engineers developing any system are also responsible for bias. According to VentureBeat, a Columbia University study found that “the more homogenous the [engineering] team is, the more likely it is that a given prediction error will appear.” This can create a lack of empathy for the people who face problems of discrimination, leading to an unconscious introduction of bias in these algorithmic-savvy systems.

So, how can we eliminate the negative impact of bias in the use or development of our technology?

After this workshop you will be able to:

• Minimize if not eliminate the embedding of bias in the development of your technology
• Minimize if not eliminate the embedding of bias in the configuration of your technology
• Minimize if not eliminate bias in the use of your technology

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