Data

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.

Are you experimenting with artificial intelligence, or are you “AI future-ready”? A new model maps four stages of enterprise AI maturity.

Artificial intelligence can be useful in the workplace, but humans have to first define what success looks like, according to MIT Sloan’s Danielle Li.

Machine learning can drive climate action initiatives, but its widespread use could have negative implications, according to Climate Change AI’s Priya Donti.

Experts from Salesforce, S&P Global, and Corning share six key strategies to unlock generative AI’s potential without falling for the hype.

Top-performing companies invest in CEO-level data leadership, data value realization, and data resource life-cycle measurement.

AI workloads have sent data center emissions skyrocketing. An MIT expert details ways to reduce energy use and promote sustainable AI.

Indirect emissions that occur along a company’s value chain account for 75% of the organization’s overall emissions, on average. They remain difficult to track.

Businesses have identified two types of generative AI: broadly applicable tools that boost personal productivity, and tailored solutions for specific purposes.

The AI Risk Repository, a database of over 700 risks posed by AI, aims to provide a shared framework for monitoring and maintaining AI risk oversight.