Bias in data and technology has been a frequent topic lately. However, it isn’t the only challenge we face as we gather larger and larger quantities of data and dive more deeply into using analytics to draw conclusions from that data. CMS Wire brought this interesting topic to our attention in their article, “Rolling Stone, Data Analysis and the Problem of Recency Bias.”

Organizations need a varied degree of competency when it comes to truly leveraging analytics capabilities. Both data scientists and data entrepreneurs have a role to play.

The fact is almost all big data sets, generated by systems powered by machine learning and artificial intelligence (AI) based models, are known to be biased. However, most are not aware of these biases and even if they are, they do not know what to do about it.

This is why it has never been more important for organizations to understand how AI systems make the decisions they do and how the results are being applied in various fields. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.