Organizations can develop effective digital twins despite limited training data by combining AI and human knowledge.
Data, AI, & Machine Learning
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The People Powering AI: Readying an Organization for AI Opportunity
Developing a cohesive approach to AI deployment requires the right people — and mindset. Here's how to bring together the human resources to manage the opportunities and risks.
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When Generative AI Meets Product Development
Image- and text-generation tools are helping innovation groups improve ideation and creativity and gain market insights.
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Auditing Algorithmic Risk
Two frameworks can help organizations identify potential harms posed by algorithms, AI tools, or large language models.
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Avoid ML Failures by by Asking the Right Questions
Checking assumptions and mapping out work processes can help ensure that ML solutions fit the job to be done.
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How Generative AI Can Support Advanced Analytics Practice
The natural language capabilities of LLMs can augment the predictive powers of advanced analytics.
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Managing Data Privacy Risk in Advanced Analytics
Gaining value from data assets that include customers’ personal information requires advanced data protection tactics.
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How AI Skews Our Sense of Responsibility
When humans are in the loop with an AI system, they may not feel a sense of responsibility to intervene if things go wrong.
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The Hazards of Putting Ethics on Autopilot
Digital nudges can encourage reactive thinking and limit employees’ ethical thinking.
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How Lufthansa Shapes Data-Driven Transformation Leaders
Lufthansa launched an effective program to turn all its leaders into data leaders and propel its digital transformation.