Interest in artificial intelligence (AI) and machine learning (ML) is increasing as more businesses witness its benefits in various use cases. However, there are also some valid concerns surrounding AI technology—AI bias. Not only does the bias potentially drive business leaders to make poor decisions, but it can also put the organizations in legal jeopardy. In this article at TechNewsWorld, John P. Mello Jr. shares insights from the DataRobot survey and explains why more than one in three firms is burned by AI bias. The author also shares some AI bias examples and ways to reduce the risk.
The Survey Highlights
DataRobot surveyed more than 350 UK and US-based technology leaders to understand how businesses identify and mitigate AI bias examples. Survey respondents included IT directors, IT managers, development leads, CIOs, and IT managers.
In the survey, 36% of respondents revealed that their organizations incurred losses due to AI bias. The study highlighted that:
62% lost revenue61% lost talented workers to AI bias61% lost valuable customers35% incurred legal fees due to lawsuits or legal action
Real-Life AI Bias Examples
Racism in the Healthcare system is one of the AI Bias Examples
In 2019, an algorithm used in US hospitals built to predict which patients required additional medical care favored white patients over black patients by a considerable margin.
Displaying High-Paying Positions as Males
One of AI bias examples includes depicting CEOs and other C-suite executives as male employees. Though women make up 27% of CEOs in the United States, only 11% of the individuals that appeared in the Google images search for the term ‘CEO’ were women.
AI Bias Examples: What Can You Do About It?
Testing Algorithms in Real-Life Settings
To minimize bias, organizations must consider the background and experiences of different individuals. Data scientists must ensure the data provides a holistic picture of diversity to end users.
Get Input from Your Customers
Another way to minimize AI bias is by considering the limitations of your data and then assessing customer experience. Additionally, organizations must be receptive to regular customer feedback and collect a sample of their personal experiences with AI. Then, data scientists must look at the algorithms to fix any issues.
Constantly Monitor Bias
Even though there are precautionary measures at every phase to help prevent bias, reviewing and monitoring the results is critical to ensure that unintended bias does not creep in.
To learn more about AI bias examples, click on https://www.technewsworld.com/story/more-than-one-in-three-firms-burned-by-ai-bias-87387.html.
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