Machine learning bias, also known as algorithm bias or artificial intelligence (AI) bias, occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process. This interesting topic came to us from Tech Target in their article, “Eliminating bias in AI is no easy feat, but fixes do exist.”
AI systems are only as good as the data we put into them. Bad data can contain implicit racial, gender, or ideological biases. Many AI systems will continue to be trained using bad data, making this an ongoing problem.
Bias in AI system mainly occurs in the data or in the algorithmic model. This can be addressed by paying attention to the basics. And that is data governance.
Properly organizing and formatting data isn’t a simple process, but it simply can’t be done without developing an approach to data governance.
Identifying and mitigating bias in AI systems is essential to building trust between humans and machines that learn.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
