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 Project
For 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 concept
- Model validation and model scoring
- Algorithm development
- AI training data preparation
- Model deployment
- Algorithm training
- Post-deployment optimization
Statistics 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 Bias
Data 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.
Data Quality
Data quality is very generic, but when you look deeper, you’ll find several nuanced layers. Data quality can consist of the following:
- Lack of availability of estimated volume of data
- Absence of relevant and contextual data
- Absence of recent or updated data
- The abundance of data that is unusable
- Lack of required data type – for instance, text instead of images and audio instead of videos and more
- Bias
- Clauses that limit data interoperability
- Poorly annotated data
- Improper data classification
Nearly 96% of AI specialists struggle with data quality issues resulting in additional hours of optimizing the quality so machines can effectively deliver optimal results.
Unstructured Data
Data scientists and AI experts work more on unstructured data than their complete counterparts. As a result, a significant amount of their time is spent on making sense of unstructured data and compiling it into a format that machines can understand.
Unstructured data is any information that doesn’t conform to a specific format, model, or structure. It’s disorganized and random. Unstructured data could be video, audio, images, images with text, surveys, reports, presentations, memos, or other forms of information. The most relevant insights from unstructured datasets have to be identified and manually annotated by a specialist. When you are working with unstructured data, you have two options:
- You spend more time cleaning the data
- Accept skewed results
Lack of SMEs for Credible Data Annotation
Of all the factors we discussed today, credible data annotation is the one subtlety we have significant control over. Data annotation is a crucial phase in AI development that dictates what and how they should learn. Poorly or incorrectly annotated data could completely skew your results. At the same time, precisely annotated data could make your systems credible and functional.
That’s why data annotation should be done by SMEs and veterans who have domain knowledge. For instance, healthcare data should be annotated by professionals who have experience working with data from that sector. So, when the model is deployed in a life-saving situation, it performs up to expectations. The same is true for products in real estate, fintech eCommerce, and other niche spaces.
Wrapping Up
All these factors point in one direction – it’s not advisable to venture into AI development as a standalone unit. Instead, it’s a collaborative process, where you need experts from all fields to come together to roll out that one perfect solution.
That’s why we recommend getting in touch with data collection and annotation experts like Shaip to make your products and solutions more functional. We are aware of the subtleties involved in AI development and have conscious protocols and quality checks to eliminate them instantaneously.
Get in touch with us to find out how our expertise can help your AI product development.
