3 Steps To Better Mitigate Bias in Your Data Analysis

Gartner had once predicted that by 2022, 85% of AI projectsOpens a new window would deliver skewed outputs because of bias. With the new year just around the corner, and instances of tech bias becoming all the more prevalent and dangerous, it’s essential that organizations think critically about how to identify and eliminate bias from occurring in insights generated by AI and analytical systems. 

Biased insights can occur in a variety of ways; sometimes, it’s a case of humans looking only at a subset of data, perhaps because they feel more comfortable with it. We have a natural inclination to look for answers we understand,which can inadvertently mean looking at an incomplete or incorrect data set. On top of that, there’s simply too much data for humans alone to analyze. According to an IDC reportOpens a new window , the amount of data created over the next three years will be more than the data created over the past 30 years—in large part due to the vast digital working world created during the pandemic.

Here are three steps that every IT, business, and data science professional can take to mitigate bias in their organizations’ data sets. 

Step 1: Ensure All Available Data Is Included in Analytical Decision Making

It’s long been reported that enterprises are not using all the data at their disposal. A 2020 joint studyOpens a new window of 1,500 global business leaders by IDC and Seagate Technology found that about two-thirds (68%) of data available to enterprises goes unleveraged. 

The vast amount and velocity of data being created isn’t the only issue here. There have been platforms—like data warehouses—created to help, but they still require the expertise of someone who knows how to clean, prepare, and transform data to be analyzed (a skill that is in short supplyOpens a new window with the demand for data scientists growing). With a gap in technical expertise and resources, organizations are limited in the types and amount of data they can physically access and review—ultimately skewing any data analysis they do.

To overcome this challenge, tech leaders should consider how to modernize their analytics stacks to include solutions that can automate the preparation, transformation, and analysis of data across the enterprise. By utilizing all of the data available in analyses, companies can feel confident that their insights are derived from the richest, most inclusive data sets.

See More: How AI-Powered Analytics Can Bridge the Insights Gap

Step 2: Open Access To Data to more People

While data and IT teams have long been considered the experts in this arena, they should no longer be the only ones responsible for interpreting data to inform business decisions. Limiting data access and analysis to a small subset of individuals within an organization can lead to bias; it’s critical that a wide and diverse range of perspectives be involved in sourcing and analyzing data. 

To increase data literacy, organizations should identify tools that make it easy for non-technical users to drill into data. Features like natural language search can make it easy for business users to ask questions of the data their company stores and then receive automatic visualized insights that are easier to understand. While the data scientist experts should still be tapped for the most complex data problems, they can feel confident that the rest of their team has the tools they need to interpret and analyze data that will deliver the most relevant and unbiased insights. 

See More: An Approach To Mitigating AI Bias in Transforming Marketing Operations

Step 3: Strengthen the Feedback Loop Between Humans and Machines

Automation and data democratization aren’t the only key features that tech leaders should consider in new platforms. To truly mitigate bias, it’s essential that humans and machines work together and be able to effectively draw the correct conclusions from data. 

Many platforms today lack explainability and transparency for humans to be able to understand why a decision was made (and whether that decision was influenced by a biased data set). To overcome this, companies should focus on building a process and using tools that strengthen the feedback loop between humans and machines. In this loop, employees can inspect the insights generated by machines, and easily validate them or provide guidance to move towards relevant conclusions by including, excluding, or improving how input data is being interpreted. By ensuring humans participate in an iterative data collection and analysis process, these more advanced platforms will eliminate any instances of the machine going rogue or making decisions without validation. 

Biased data analysis presents a range of dangers to both businesses and society—and it’s up to the people responsible for interpreting data to fix it. By collecting and analyzing all available data; opening that data up for more diverse perspectives; and investing in transparent, explainable tools, businesses can ensure they are leveraging the most accurate, most representative, and—thus—the most valuable insights to inform business decisions.

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