MIT researchers have found that, if a certain type of machine learning model is trained using an unbalanced dataset, the bias that it learns is impossible to fix after the fact. They developed a technique that induces fairness directly into the model, no matter how unbalanced the training dataset was, which can boost the model’s performance on downstream tasks. (Article)

The scope of possible uses for AI and machine learning in finance stretches across business functions and sectors. In step with this increase in the use of AI, it is important that controls on how AI is set up and applied are put in place to ensure systems are robust, fair and safe. A look at how to drive innovation in finance through trustworthy AI. (Article)

As companies increasingly apply AI, they must address concerns about trust. As part of the World Economic Forum's Global Future Council on AI for Humanity, a collective of AI practitioners, researchers and corporate advisors propose 10 practical interventions for companies to employ to ensure AI fairness. (Article)

“The business value of AI in banking will reach $300 billion by 2030.”  – IHS Markit

“Our intelligence is what makes us human, and AI is an extension of that quality. Artificial intelligence is extending what we can do with our abilities. In this way, it’s letting us become more human.” – Yann LeCun

Does data governance filter down at every level in your organization? Do you have the infrastructure to enforce it?

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