Data, AI, & Machine Learning
Auditing Algorithmic Risk
How do we know whether algorithmic systems are working as intended? A set of simple frameworks can help even nontechnical organizations check the functioning of their AI tools.
Data, AI, & Machine Learning
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Data, AI, & Machine Learning
Avoid ML Failures by Asking the Right Questions
Machine learning solutions can miss the mark when data scientists don’t check their assumptions. Adopting a beginner’s mindset in any domain can help.
Dusan Popovic, Shreyas Lakhtakia, Will Landecker, and Melissa Valentine
Data, AI, & Machine Learning
How AI Skews Our Sense of Responsibility
Research shows how using an AI-augmented system may affect humans’ perception of their own agency and responsibility.
Ryad Titah
Data, AI, & Machine Learning
Reinventing the Organization for GenAI and LLMs
Previous waves of technology have ushered in innovations that strengthened traditional organizational structure. Not so for generative AI and large language models.
Ethan Mollick
Data, AI, & Machine Learning
AI and Statistics: Perfect Together
Many companies develop AI models without a solid foundation on which to base predictions — leading to mistrust and failures. Here’s how statistics can help improve results.
