We all understand that the performance of an artificial intelligence (AI) module depends entirely on the quality of datasets provided in the training phase. However, they are usually discussed on a superficial level. Most of the resources online specify why quality data acquisition is essential for your AI training data stages, but there is a gap in terms of knowledge that differentiates quality from insufficient data.When you delve deeper into datasets, you will notice tons of intricacies and subtleties that are often overlooked. We’ve decided to shed light on these less-spoken topics. After reading this article, you will have a clear idea of some of the mistakes you’re making during data collection and some ways you could optimize your AI training data quality.Let’s get started.The Anatomy of an AI ProjectFor the uninitiated, an AI or an ML (machine learning) project is very systematic. It is linear and has a solid workflow.
To give you an example, here’s how it looks in a generic sense:Proof of conceptModel validation and model scoringAlgorithm developmentAI training data preparationModel deploymentAlgorithm trainingPost-deployment optimizationStatistics reveal that close to 78% of all AI projects have stalled at one point or the other before getting to the deployment stage. While there are major loopholes, logical errors, or project managerial issues on one side, there are also subtle errors and mistakes that cause massive breakdowns in projects. In this post, we are about to explore some of the most common subtleties.Data BiasData bias is the voluntary or involuntary introduction of factors or elements that unfavorably skew results towards or against specific outcomes. Unfortunately, bias is a plaguing concern in the AI training space.If this feels complicated, understand that AI systems don’t have a mind of their own. So, abstract concepts like ethics, morals, and more don’t exist. They are only as smart or functional as the logical, mathematical, and statistical concepts utilized in their design. So, when humans develop these three, there are obviously going to be some prejudices and favoritism embedded.Bias is a concept that is not associated directly with AI but with everything else surrounding it. Meaning it stems more from human intervention and could be introduced at any given point in time. It could be when a problem is being addressed for probable solutions, when data collection happens, or when the data is prepared and introduced into an AI module.Can We Completely Eliminate Bias?Eliminating bias is complicated. A personal preference is not entirely black and white. It thrives on the grey area, and that’s why it is subjective as well. With bias, it’s tough to point out holistic fairness of any kind. Besides, bias is also difficult to spot or identify, precisely when the mind is involuntarily inclined towards particular beliefs, stereotypes, or practices.That’s why AI experts prepare their modules considering potential biases and eliminating them through conditions and contexts. If done correctly, skewing of results can be kept at a bare minimum.