Data-driven decision making

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

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

Before investing in products touting business insights from brain research, managers must understand how they fall short.

Managers need more thoughtful and effective data collection practices to advance DEI in their organizations.

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

Leaders must answer eight questions to successfully tackle innovation’s toughest trade-offs.

Successful digital initiatives require metrics that track business results, not technology use.

When we rely on machines to make decisions, we substitute data-driven calculations for human judgment.

AI techniques can generate training data that retains the predictive power of real-world data.

New research shows that an individual’s decision-making style informs the choices they make when using AI-based inputs.

To address algorithms’ potential harm, companies must be willing to focus on users and rethink their business models.

Mapping employees’ working relationships can help guide leaders’ decisions about post-pandemic work models.

Evaluators can be nudged to make less biased decisions in hiring and other contexts.