Be Wary of Bias: Avoiding Data Bias in Artificial Intelligence

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Shortcomings in artificial intelligence (AI) technology are rising to the forefront with growing attention on a slew of AI topics like ChatGPT and deep fakes. As everything continues to move digital, many are looking to bring these tools into their everyday lives. From a business perspective, looking to train their AI on proprietary data, it requires that their AI have direction, commitment, and oversight for an organization to make the most of the technology. Without it, unmonitored AI trained on flawed data will begin to reflect human biases in day-to-day operations. 

Bias in the Machines 

The first step to preventing algorithmic bias is understanding what it is and the potential occurrences. Data bias is when a machine gives a certain set of outputs to one defined group and a different set of outputs to another, typically in line with historical human biases like race, disability, gender, sex, age, or nationality. Decisions made with data bias can negatively impact a business’s finance, IT, digital, operations, sales and strategy. Piling onto that, data bias leads to poor customer experiences, one that may damage the company’s reputation. In a business setting, bias arises in online hiring, predictive modeling, and credit assessments, among many other processes. Data bias occurs for various reasons; a data scientist could use data that ignores large groups of people or intentionally omit specific sets of data. Either way, data is at the core of successful AI implementation. 

Beyond just business, data bias has also developed in everyday technologies like facial recognition, digital accessibility, search engines, and beyond. While seemingly farfetched to the average person, instances of data bias are already well documented. 65% of business and IT executives surveyed by Progress believe there is currently data bias in their organization. The bottom line is, the algorithms will only be as good as the data provided to them. Bad AI decision-making, typically caused by bias, can largely be mitigated by human intervention. 

Training & Oversight 

A good starting point for preventing bias is providing AI with diverse data sets for ingestion. Companies typically have mountains of structured and unstructured data ranging from Excel files to financial reports layered throughout their operation. By feeding AI a wide range of diverse data, the machine can use all of it to make the best, unbiased decision. Businesses can train AI algorithms to avoid culture, age, and gender-based bias by providing as many data points as possible about a subject, in theory making a resulting answer more “right”. AI will use the data provided to identify wrong from right, taking an educated guess on what’s more likely. Hence, the need for such a wide array of data. 

The next step to improving AI technology is to add human oversight to the process. AI can’t govern itself, and when left unattended, AI struggles ethically and often produces inaccurate and discriminatory predictions. Regardless of the quality and quantity of data provided, AI requires the necessary context for whatever it is being trained on. To provide this context and identify bias, companies need to create a rules-based system that can accurately categorize data using the appropriate classifications. By doing so, the machine can label data while creating the best assumptions to move forward without bias.

All of this is not to say that we should be removing the human from this process, in fact the opposite is true. We’re looking to apply human expertise, from the business, both data and subject-matter expertise to the data. Doing the above allow us to scale this expertise human expertise, to a machine scale, something that not only save time and money but also provides greater context and insight to the data, ahead of the AI to ensure the best results with the lowest possible bias.

Mitigate Bias, Improve Business Processes 

AI can greatly improve human decision-making; few can argue with that. But business leaders must be more responsible and encourage their organizations to reduce data bias in AI. Despite the many benefits, downsides to using AI do exist, and potentially massive risks do exist. Recognizing AI’s potential in a business setting is only the very first step to achieving results. AI is already so widely used that issues like data bias will only intensify before being resolved. It takes commitment to deploy AI systems across operations while remaining unbiased successfully. By providing transparency, people can trust an organization’s systems to produce unbiased results.  

About the Author

Philip Miller is a Customer Success Manager for Progress, looking after the International Standards Bodies and Publishing accounts. Philip also leads the customer webinar series Digital Acceleration and Progress Vision events. Always keen to advocate for his customers and provide a voice internally to improve and innovate the Progress Data Platform. Named as a Top Influencer in Onalytica’s Who’s Who in Data Management. Outside of work, he’s a father to two daughters, a fan of dogs, and an avid learner, trying to learn something new every day.

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