This guest post from Alegion explores the reality of machine learning bias and how to mitigate its impact on AI systems. It exists as a combination of algorithms and data; bias can occur in both of these elements. When we produce AI training data, we know to look for biases that can influence machine learning (ML). In our experience, there are four distinct types of bias that data scientists and AI developers should avoid vigilantly. The key to successfully mitigating bias is to first understand how and why it occurs.
By routing your camera feeds to our AI Engine, you can be informed in just 3 seconds when a firearm is detected in surveillance cameras. Additionally, this AI technology can track shooters in real time, providing shooter location(s) and fast live updates to police, school security and educators. In the wake of school shooting incidents over the past 10 years, people are anxious about creating safe environments. The ability to detect weapons on premises is unfortunately a necessity now. Cameras are already in place at most schools.
Zegami provides an image-based data visualisation platform designed to enable users to explore large image datasets in order to unlock insights and build machine learning models. The company points out that, with any system that is reliant on data, overall effectiveness is dependent on the quality of data that it utilises. If the data is good, the value of output will reflect this, and AI is no different. In machine learning-based models, when trained on incorrect, underrepresented or biased data, the models can Themselves become biased. "In the field of AI, we typically encounter five different types of bias: algorithmic, sample, prejudice, measurement and exclusion bias. These can be difficult to eliminate, particularly as certain biases may be unconscious," states Zegami.
Artificial Intelligence technology is completely dependent on the data sets that are provided to train its underlying machine learning (ML) model. Machine learning models are built by developers based on their collected and annotated training data sets. These training data get used to training the ML model to make predictions about the world. The better the annotated data, the better the predictions. Problems arise when that data is wrong or distorted.
Back in 2018, the American Civil Liberties Union found out that Amazon's Rekognition, face surveillance technology used by police and courting departments across the US, shows AI bias. During the test, the software incorrectly matched 28 members of Congress with the mugshots of people who have been arrested for committing a crime, and 40% of the false matches were people of color. Following mass protests wherein Amazon's employees refused to contribute to AI tools that reproduce facial recognition bias, the tech giant has announced a one-year moratorium on law enforcement agencies using the platform. The incident has stirred new debate about bias in artificial intelligence algorithms and made companies search for new solutions to the AI bias paradox. In this article, we'll dot the i's, zooming in on the concept, root causes, types, and ethical implications of AI bias, as well as list practical debiasing techniques shared by our AI consultants that worth including in your AI strategy.