Solutions for Uncovering and Addressing Algorithmic Biases with AIOps

The business world is increasingly turning to artificial intelligence (AI) systems and machine learning (ML) algorithms to automate complex and simple decision-making processes. Thus, to break through the paradigm in the field of IT operations, IT professionals and top managers started opting for AIOps platforms, tools, and software, as they promised to streamline, optimize, and automate numerous tasks quickly and efficiently. However, there are a few shortcomings, like algorithmic bias, that have been a major concern for IT professionals and other employees in the company.

Overcoming Challenges in AIOps Algorithmic Biases

Insufficient Or Low-Quality Data

AI system functions are being trained with a set of relevant data topics that need to be tacked properly. However, IT professionals often struggle to fill their algorithms with the right quality or necessary data, either because they don’t have access to it or because the quantity doesn’t exist. This imbalance can lead to discrepant or even biased results when operating your AI system.

Solution: This situation can be prevented if you make sure to use high-quality or representative data that will help kickstart your AI journey with a simpler algorithm and control bias and modification accordingly.

Overestimating Your AI System

The technological advancement that we have witnessed has made us believe that technology can do no wrong. However, AI technology relies on data, and if it is incorrect it will make biased decisions. Thus, overestimating the AI system can be challenging and rather complex, especially when formulating a set of data that we can import into a system.

Solution: Here, the role of AI explainability is crucial to successfully transiting the data into a machine learning platform by breaking down the algorithm and training the users on making transparent decisions that help prevent faulty operations.

Examples of Algorithmic Biases

Algorithmic bias can manifest itself in several ways and varies with the degree of consequence for the subject group. Let’s look at the following examples, which give us an idea of the range of causes and how they affected society or groups:

Bias in Word Associations

Princeton University researchers used shelf-machine learning AI software to analyze and link 2.2 million words. The researchers found that European names were perceived as more pleasant words than African-American words. Apart from that, the words “woman” and “girl” are associated with the arts instead of science and math, which are likely connected to males. Thus, analyzing these words associated with the training data, the ML algorithm picked up racial and gender biases that humans conducted.

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