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Using the wrong datasets to train AI models can result in legal risks, bias, or lower-quality models. The Data Provenance Initiative’s tool can help.

Generative AI and financial data are opening up the digital economy to more consumers and small businesses, but crypto concerns remain.

When employees contact an ombuds, what issues are raised — and which are most important to all stakeholders, including the organization?

Fundamentals and frameworks for first-time and serial entrepreneurs alike. Plus, a practical new guide to engaging with innovation ecosystems.

Sarah Biller builds tech organizations ready for a “permissionless, frictionless, contactless” financial services sector.

Schmittlein arrived on campus in 2007 “not to change MIT, but to help it be the best version of its distinctive self.”

New MIT Sloan research offers a framework of human-intensive capabilities and a set of metrics to evaluate tasks across all occupations and better understand the effects of AI on the labor market.

From defining impact to soliciting input, here’s how to drive innovation within your organization.

From risk management policies to the five stages of AI ethics, here’s how some organizations approach ethical AI.

Effective data leadership starts with modernizing data technology — and calls for taking action, no matter how daunting.