{"id":256539,"date":"2022-10-21T07:34:18","date_gmt":"2022-10-21T02:04:18","guid":{"rendered":"https:\/\/www.jigsawacademy.com\/?p=256539"},"modified":"2022-10-21T07:35:13","modified_gmt":"2022-10-21T02:05:13","slug":"biases-in-data-collection-types-and-how-to-avoid-the-same","status":"publish","type":"post","link":"https:\/\/www.jigsawacademy.com\/blogs\/business-analytics\/biases-in-data-collection-types-and-how-to-avoid-the-same\/","title":{"rendered":"Biases in Data Collection: Types and How to Avoid the Same"},"content":{"rendered":"

Introduction<\/span><\/b>\u00a0<\/span><\/h3>\n

An inaccuracy known as bias in data occurs when specific dataset components are overweighted or overrepresented. The key to overcoming bias is being aware of its warning signs. We have various implicit and explicit biases as humans. Biases are deliberate errors in reasoning that are impacted by one’s culture and experiences. Biases affect our views and lead us to make bad choices. The bias against automation is one that many people have. <\/span>\u00a0<\/span><\/p>\n

In reality, computers, data, and algorithms are not entirely objective. Data analysis can indeed aid in better decision-making, yet bias can still creep in. It’s we, humans, that technologies and algorithms. As a result, human biases are frequently incorporated into them. It is obvious that when using a GPS while driving, we must pay attention to other information streams (such as our eyes and ears). Similarly, we must consider other data sources while assessing the findings or recommendations of data analysis.<\/span>\u00a0<\/span><\/p>\n

Understanding the various biases that emerge at each data analysis stage is necessary if we wish to use data and algorithms responsibly. In more detail, let’s examine some biases affecting data analysis and data-driven decision-making.<\/span>\u00a0<\/span><\/p>\n

What Does Bias Mean in Data Analytics?<\/span><\/b>\u00a0<\/span><\/h2>\n

We must first gather data before we can evaluate it or apply Machine Learning techniques. Data bias might occur when you train an algorithm for Machine Learning with a dataset that isn’t accurately reflective of its intended usage. For instance, if you’re marketing premium spirits and only train your AI using data that mimics beer drinkers’ behavior, the results will be highly wrong and distorted. The data utilized to train the ML models is a major contributor to these biases. It is known that practically all massive data sets produced by ML\/AI-powered systems are <\/span>biased<\/span><\/b>. However, even if they are aware of these biases, most ML modelers are unsure how to address them.<\/span>\u00a0<\/span><\/p>\n

Sampling and estimation are both prone to bias. Our data wouldn’t be <\/span>biased <\/span><\/b>if we had complete knowledge of every entity in it (such as customers, insurance claims, and software sessions) and if we could save data on every imaginable entity. Humans also make poor, intuitive statisticians, and their estimates are frequently off. These issues are so pervasive that they are frequently discovered in meticulously planned and controlled statistical tests.<\/span>\u00a0<\/span><\/p>\n

How Does Bias Show in Analytics?<\/span><\/b>\u00a0<\/span><\/h2>\n

The source material is not the only way bias can enter data. It can also be introduced via data collection and analysis techniques. There are a variety of biases that might harm the data, including the following:<\/span>\u00a0<\/span><\/p>\n

Propagating the Current State\u00a0<\/span><\/b>\u00a0<\/span><\/p>\n

In data analysis, propagating a current state is a typical form of bias. Men were preferred in Amazon’s recruiting processes since they were more reflective of their current workforce. The algorithms were prejudiced by other factors they considered that were indirectly related to gender, such as sports, social activities, and adjectives used to characterize accomplishments, even though they didn’t expressly know or consider the applicants’ gender.<\/span>\u00a0<\/span><\/p>\n

In essence, the AI detected these minute variations and searched for candidates that met their internal definition of successful candidates. Providing context and relationships to your AI systems is a good countermeasure.<\/span>\u00a0<\/span><\/p>\n

Trained on the Wrong Thing<\/span><\/b>\u00a0<\/span><\/p>\n

When an algorithm makes intentional, recurring mistakes that result in unfair outcomes, such as favoring one group over another, this is known as algorithmic bias. Selection bias can be the starting point for algorithmic bias, which is subsequently strengthened and maintained by other bias types.<\/span>\u00a0<\/span><\/p>\n

The algorithms used in facial recognition software are proprietary. Therefore, we cannot determine the algorithm’s design or functioning, in addition to not knowing what data were utilized for training and testing the algorithm. <\/span>\u00a0<\/span><\/p>\n

Transparency is essential for avoiding algorithmic bias, particularly when it comes to the data used to train and test an algorithm. In response to darker females’ low facial recognition performance, a new benchmarking dataset was created that is more inclusive of the entire human population. This is significant, provided businesses that develop and market facial recognition software use the new dataset.<\/span>\u00a0<\/span><\/p>\n

Under-representing Populations<\/span><\/b>\u00a0<\/span><\/p>\n

Selection bias occurs when the data you will examine is not chosen at random. When selecting or omitting data for a specific reason, such as when dealing with outliers, it might result in an inaccurate depiction of the outcomes your company is attaining. Under-representing population samples that don’t fairly represent the full target group results in selection bias. A major community, for instance, would not be accurately represented by data gathered from a single area.<\/span>\u00a0<\/span><\/p>\n

Selection bias can occur for a variety of reasons, some of which are purposeful and others unintentional, such as voluntary participation, participation restrictions, or insufficient sample size. There are three types of selection bias:<\/span>\u00a0<\/span><\/p>\n