Data collection is an important process for most businesses. Companies rely on information to make informed decisions. Because of the importance of data, information collection methods have been developed to automate much of the process. However, these tools rely a lot on machine learning (ML), which could potentially lead to biased results.
In this article, we’ll explore different data collection methods and how biases can impact the results of these efforts. We’ll also look at how tools like the Google Reverse Image Search API and others that rely on ML are affected by biases and why these exist.
Table of Contents
Data Collection Methods
When it comes to collecting vast amounts of information from the internet, there are a few methods commonly used. These methods improve efficiency by automating a lot of the gathering process. Let’s take a look at the three most common automated data collection methods.
Web scraping is the process of collecting data from websites, search engines, and even images. To use a web scraper you input the parameters of the information you want and the websites to be collected from. Once accepted the tool then crawls all of these websites and collects the relevant data. Once complete the information is compiled into a single format where it can be evaluated.
Tracking is an automated process of following your web users to see what other websites or platforms they visit. Businesses gain a deeper insight into the user’s preferences, browsing habits, and more. Tracking can take place through cookies, web beacons, and other means. To start tracking a user, they will first need to visit your website and accept your cookies.
API (Application Programming Interface)
APIs aren’t technically data collection tools, but they do facilitate the process of collecting information making it easier to find. When a business or platform collects data in a database, it can use an API to make this information easily available for other users that can benefit from the information. APIs are frequently used by governments and businesses that support open data systems. Collecting data through APIs is considered the best option as it complies with all necessary privacy and data protection regulations.
Data Biases Affecting Information
Unfortunately, when we automate processes we depend on various ML models to complete the tasks. The same goes for when we use web scraping, tracking, and even APIs to collect information. The problem with this is that it can cause data biases.
What Are Data Biases?
Data bias occurs when the content used to train the ML model contains certain information that can create a bias. For example, if the Google Reverse Image Search API’s model was trained using only images of fair-skinned individuals, the results might be biased to only showing images of light-skinned individuals. Similarly, if the information used to train your web scraper was based largely on certain genders, then your results may also be biased toward that gender.
This is a major concern when it comes to data collection as it means that your information could be inaccurate or incomplete. If the program you use contains bias, you might be excluding an entire sector of the market without even realizing it.
Types of Data Bias
To understand how biases can occur, let’s have a look at a few different types of data bias.
Response or Activity Bias
These are generated by humans and are typically opinions. These can include reviews on Amazon, Twitter tweets, Facebook posts, and other similar content. The problem with this is that only a few people leave reviews or comments. This means that the views collected only include a small portion of the population.
Omitted Variable Bias
This bias occurs in models where critical aspects that influence the outcome of ML are missing. This often happens in systems where humans input the data. Since humans are inherently biased, they may unconsciously only include a few aspects that they feel are important, while missing out on others.
This is the most well-known type of bias to occur in data collection and can also be referred to as label bias. This occurs in content created by humans and can be from social media posts, blog posts, or news articles. This content often includes stereotypical biases such as race or gender.
How to Overcome Data Bias?
It won’t be easy to overcome data bias as a lot of the content on the internet is inherently biased because the content is created by humans. Up until the last decade content was predominantly created by white men, so, there are bound to be biases in the information. ML models rely on data to educate their programs, but if the data you use is already biased, chances are those biases will come out in the program as well.
As such, it’s important that when the ML models are developed, they check the data used for learning to ensure that it’s unbiased. They should also ensure to include content from all genders, ages, races, and abilities.
If you’re collecting data, you should be aware of potential biases. When evaluating your data you should be critical and exclude overly biased information. You should also endeavor to collect as wide a range of data as possible – often by making these specifications when collecting information.
Data collection is an essential process for businesses, however, the information could contain biases. The way that you collect your data, such as your data targets, can also have an impact on whether your data is biased. It’s important to be aware of these biases and evaluate any collected information accordingly to exclude bias from your final results.