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Avoiding bias in AI: how a representative workforce empowers due diligence-grade data for financial decision-making

July 2021

Bias can exist in artificial intelligence (AI) and machine learning. As the world’s largest ESG technology company committed to providing the most comprehensive dataset on ESG risks, a part of RepRisk’s work involves proactively mitigating biases in our dataset by leveraging the value of a representative research staff.  

As stated in part one of our series on AI and machine learning in ESG, RepRisk’s unique combination of cutting-edge technology and human intelligence is the best approach to delivering speed and driving the scale of our dataset without sacrificing data quality or granularity.  

There is a human behind every piece of technology. Algorithms will reflect the bias of whomever programmed it, or in the case of supervised machine learning, the person who labels the dataset used to train the algorithm. With that in mind, it is important to shed more light on how the human element is critical to ensuring high-quality machine learning-based solutions. 

How does bias in AI occur?

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