It’s impossible to abolish AI bias in the data behind artificial intelligence models, but companies can remediate it.
Data, AI, & Machine Learning
Page 4 of 27
-
How AI Is Improving Data Management
Artificial intelligence is quietly improving the management of data, including its quality and security.
-
How to Build Good AI Solutions When Data Is Scarce
New data-efficient AI techniques can help when developers lack sufficient volumes of labeled training data.
-
When Algorithms Rule, Values Can Wither
Building responsible AI systems starts with recognizing that technology solutions implicitly prioritize efficiency.
-
Achieving Individual — and Organizational — Value With AI
The 2022 MIT SMR-BCG AI and Business Strategy report finds organizations get more value from AI when workers benefit too.
-
What Machines Can’t Do (Yet) in Real Work Settings
Most companies that are using AI are deploying it for augmentation, not large-scale automation.
-
Working With AI: Real Stories of Human-Machine Collaboration
Offers detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor.
-
To Be a Responsible AI Leader, Focus on Being Responsible
The 2022 MIT SMR-BCG responsible AI report supports the prioritization of RAI as a business imperative.
-
Product-Led Growth Companies Find a New Way to Serve Customers
Companies from Zoom to Slack lean on the product itself to find customers and convert them to paying.
-
Unlocking the Potential of Digital Twins in Supply Chains
Digital twins can deliver immense benefits across a wide range of supply chains with the right implementation strategy.