Rooting Out Algorithmic Bias

Experience the impact of Algorithmic Bias

Included in your content download you will receive the following:

• Algorithmic Bias Research Paper & Tech Release Preview
• A guided overview and sample of how the code for our Algorithmic Bias Workshop works with a dataset. See how slides for the workshop are generated, and try it out with your own data by downloading the package called “preview” (the code is in R).

The full paper explores:

• Ethical ramifications of algorithmic bias
• Financial and reputational consequences for businesses
• Potential regulations on AI technology
• The importance of mitigating bias in your organization
  

What Is Algorithmic Bias?

Bias in AI is nothing new. Any AI system operates on bias to be able to differentiate between different parts of the dataset; there is no such thing as a bias-free algorithm.

But in the design of the algorithm, including in its training or dataset, there may also be entry points for unintended bias, and even intended biases can have negative downstream consequences unforeseen by the designers.  

While it may not seem urgent on the surface, bias in AI is a critical issue for business leaders to consider and take measures to address. Besides the ethical ramifications of ensuring algorithms aren’t making unfair decisions or giving unfounded preference to certain groups of people, it won’t be long until there are financial and reputational consequences around bias in AI.