Data, AI, & Machine Learning

Understanding how AI algorithms are trained and validated can help decision makers pick the right tools and avoid risk.

Based on their research, the authors share four key ways companies can advance their strategic data-sharing initiatives.

It’s impossible to abolish AI bias in the data behind artificial intelligence models, but companies can remediate it.

Artificial intelligence is quietly improving the management of data, including its quality and security.

New data-efficient AI techniques can help when developers lack sufficient volumes of labeled training data.

Building responsible AI systems starts with recognizing that technology solutions implicitly prioritize efficiency.

The 2022 MIT SMR-BCG AI and Business Strategy report finds organizations get more value from AI when workers benefit too.

Most companies that are using AI are deploying it for augmentation, not large-scale automation.

Offers detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor.

The 2022 MIT SMR-BCG responsible AI report supports the prioritization of RAI as a business imperative.