Biases are often inherited by cultural and personal experiences. When data is collected and used in the training of machine learning models, the models inherit the bias of the people building them, producing unexpected and potentially harmful outcomes. Yet, despite the potential legal and financial pitfalls associated with data bias, there is a lack of understanding around the training, processes and technology needed to tackle data bias successfully.
The Progress survey indicated that 78% of business and IT decision makers believe data bias will become a bigger concern as AI/ML use increases, but only 13% are currently addressing it and have an ongoing evaluation process. The biggest barriers they see are lack of awareness of potential biases, understanding how to identify bias as well as the lack of available expert resources, such as having access to data scientists.
The survey findings also show:
- 66% of organizations anticipate becoming more reliant on AI/ML decision making, in the coming years.
- 65% believe there is currently data bias in their organization.
- 77% believe they need to be doing more to address data bias.
- 51% consider lack of awareness and understating of biases as a barrier to addressing it.
“Every day, bias can negatively impact business operations and decision making – from governance and lost customer trust to financial implications and potential legal and ethical exposure,” said
Commissioned by Progress, the survey, “Data Bias: The Hidden Risk of AI,” was conducted by
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Source: Progress Software Corporation