Data BiasApart from bad data and its sub concepts, there exists another plaguing concern called bias. This is something that companies and businesses around the world are struggling to tackle and fix. In simple words, data bias is the natural inclination of datasets towards a particular belief, ideology, segment, demographics, or other abstract concepts.Data bias is hazardous to your AI project and ultimately business in a lot of ways. AI models trained with biased data could spew results that are favorable or unfavorable to certain elements, entities, or strata of the society.Also, data bias is mostly involuntary, stemming from innate human beliefs, ideologies, inclinations, and understanding. Due to this, data bias could seep into any phase of AI training such as data collection, algorithm development, model training, and more. Having a dedicated expert or recruiting a team of quality assurance professionals could help you mitigate data bias from your system.Data VolumeThere are two aspects to this:Having massive volumes of dataAnd having very little dataBoth affect the quality of your AI model. While it might appear that having massive volumes of data is a good thing, it turns out that it isn’t. When you generate bulk volumes of data, most of it ends up being insignificant, irrelevant, or incomplete – bad data. On the other hand, having very little data makes the AI training process ineffective as unsupervised learning models cannot function properly with very few datasets.Statistics reveal that though 75% of the businesses around the world aim at developing and deploying AI models for their business, only 15% of them manage to do so because of the lack of availability of the right type and volume of data. So, the most ideal way to ensure the optimum volume of data for your AI projects is to outsource the sourcing process.Data Present In Silos
So, if I have an adequate volume of data, is my problem solved?Well, the answer is, it depends and that’s why this is the perfect time to bring to light what is called data silos. Data present in isolated places or authorities are as bad as no data. Meaning, your AI training data has to be easily accessible by all your stakeholders. The lack of interoperability or access to datasets results in poor quality of results or worse, inadequate volume to kickstart the training process.Data Annotation ConcernsData annotation is that phase in AI model development that dictates machines and their powering algorithms to make sense of what is fed to them. A machine is a box regardless of whether it is on or off. To instill a functionality similar to the brain, algorithms are developed and deployed. But for these algorithms to function properly, neurons in the form of meta-information through data annotation, need to be triggered and transmitted to the algorithms. That is exactly when machines begin to understand what they have to see, access and process and what they have to do in the first place.Poorly annotated datasets can make machines deviate from what is true and push them to deliver skewed results. Wrong data labeling models also make all the previous processes such as data collection, cleaning, and compiling irrelevant by forcing machines to process datasets wrongly. So, optimum care has to be taken to ensure data is annotated by experts or SMEs, who know what they are doing.Wrapping UpWe cannot reiterate the importance of good quality data for the smooth functioning of your AI model. So, if you’re developing an AI-powered solution, take the required time out to work on eliminating these instances from your operations. Work with data vendors, experts and do whatever it takes to ensure your AI models only get trained by high-quality data.Good luck!