AI and Machine Learning are quickly becoming a cornerstone feature of Big Data and Analytics.  Machine Learning has the potential to unlock short-and long-term business value across business functions and there is a clear risk of being left behind for organizations who choose to rest on their laurels in the accelerating race for adoption.

However, emerging technologies come with fresh uncertainty and new, unforeseen dangers. Bias in Machine Learning is a pervasive yet often concealed pitfall, with grave implications for efficiency, cost, and accuracy in the absence of action.

As a specialized sub-field of Artificial Intelligence, ML essentially refers to the self-improvement of a computer algorithm as it is trained using large volumes of data. With an ever-expanding repertoire of powerful offerings, including improved productivity, accurate forecasting, and reduced costs, it is no secret that the exploration and incorporation of Machine Learning is a transformative and lucrative tool for organizations in almost every industry.

However, neglecting possible bias in Machine Learning can jeopardize the actualization of many of these benefits, as well as the organization itself. When using machine learning to inform data-driven business decisions, comprehension of the underlying algorithm is crucial. While simpler algorithms like linear regression can be easier to understand, interpreting the more complex algorithms used in ML can be extremely challenging, often blocking reliable insight into what your model is actually doing. This “black-box” effect allows bias to creep in and often go unnoticed for extended periods of time. Fortunately, new and innovative ways to reduce bias in machine learning are being introduced to the AI world as visibility of the significance and ubiquity of ML bias are increasingly recognized.

In this recorded webcast, we introduce key concepts of bias in ML, including its significance in a business environment and common scenarios where bias can arise. We will follow with an investigation into various methods of reducing ML bias, before exploring a particularly innovative business use-case that demonstrates the use of a specialized data science library for bias assessment and improvement.

What we cover:

• A brief introduction to ML
• The significance and frequency of insidious bias in ML
• Common scenarios where ML bias can arise and how to fight it
• A business use case and demonstration that invokes a particularly interesting solution to ML bias

Is it for you?

• Have you recently adopted/Are you looking to adopt machine learning in your organization?
• Are you concerned about the visibility you have of your machine learning models?
• Are you committed to maintaining accurate and objective machine learning initiatives?