Data bias is becoming an increasingly pressing issue for businesses using artificial intelligence and machine learning, but many organizations struggle to effectively address it.
Two-thirds of executives believe there is currently data bias in their organizations, according to a global survey sponsored by Progress and conducted by Insight Avenue.
Data bias has become an even more prominent concern for companies using artificial intelligence (AI) and machine learning (ML) to analyze and make sense of their data. As everything goes digital, companies now have access to a wealth of information – in many cases too much information to know where to start.
AI and ML can derive actionable insights from big data, helping companies make better business decisions.
Therefore, it is not surprising that more businesses have started to use and rely on artificial intelligence. The study found that 66% of organizations expect to rely more on AI and/or AI for decision making.
While the goal is to make businesses smarter and more efficient, the use of AI has also brought some unintended consequences; data bias is key.
Decisions made based on biased data can negatively impact finance, IT, digital, operations, sales and strategy. Even worse, data bias can lead to poor customer experiences, damage companies’ reputations, and delay inclusion and diversity efforts.
“Every day, bias can negatively impact business and decision-making, from the financial implications of lost governance and customer trust to the potential legal and ethical implications,” said John Ainsworth, EVP and General Manager, Application and Data Platform, Progress.
“We put our customers at the center of everything we do, and as we explore all that AI/ML can do, we want to ensure our customers are armed with the right information to make the best decisions to drive their business forward to decide,” he added. .
At Progress, we wanted to understand how widespread data bias is, the actions businesses can take to prevent and eliminate bias, the barriers to addressing it, and the consequences of unchecked bias. In partnership with UK-based Insight Avenue, we commissioned a global survey of 640 business and IT leaders who use data to make decisions or are planning to use AI or ML to support decision making. All executives managed companies with 500+ employees.
The study found that while 78% believe data bias will become a bigger problem as AI/ML use increases, only 13% currently address data bias and have an ongoing assessment process to eliminate it : In addition, more than half of respondents identified a lack of awareness of potential bias as a barrier to addressing data bias.
Read more highlights from the study below or download a copy of the study to get a full picture of the state of data bias in business.
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Recognizing the threat of data bias
While companies vary in their strategies to address potential data bias, businesses are aware of the risks and consequences that data bias can bring.
77% of respondents admit they need to do more to understand and address bias in their organization, and 76% say there are wider social consequences if companies don’t adequately address data bias.
Most leaders (78%) are aware that as AI becomes more widely used, the problem will only get worse. With that in mind, 67% of CEOs believe their organization has evaluated technology to combat data bias, and 40% said data bias is a consideration when evaluating AI/ML vendors.
Avoiding data bias will likely require a combination of people, tools, training and policy; 76% recognize that data drift is best handled centrally within the organization, rather than by regulatory departments.
Where organizations can improve
When it comes to combating data bias, organizations must overcome several hurdles before making progress. Key barriers to addressing data bias include lack of awareness of potential bias (51%), lack of bias detection (43%), and lack of expert resources (31%).
Just 9% of respondents said they did not see data bias as a problem, indicating that inaction can be attributed to struggles with planning and execution rather than a failure to recognize the threat of data bias.
77% of respondents said their organizations still need to do more to understand data bias. Executives believe the most effective measures will be technology and tools (65%), more training (59%) and refining their strategy and vision (49%).
How businesses can address and avoid data drift
As more organizations begin to rely on AI and ML, the need to address potential data biases becomes more pressing. Companies must have plans and processes in place to identify and prevent data breaches, and organizations must recognize how it can threaten all aspects of the business.
Organizations must consider all parts of the program, from hiring and team diversity to training and technology. Data bias can affect the day-to-day decisions of any company, and it can have a detrimental effect on its victims. Those who lead technology and training efforts must ensure that their work promotes fairness and justice in the workplace.