Data Science Fails: Case Studies and Lessons Learned

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AI adoption rates are on the rise, and 2020 has become the year that AI becomes business-as-usual rather than a science experiment. But while the adoption of AI has become widespread, not all AI projects are living up to the hype. “Despite the promise of AI, many organizations’ efforts fall short…only 8% of firms engage in core practices that support widespread adoption,” says McKinsey. Great software such as automated machine learning, can help lower the barriers to success, but businesses also need to learn from the mistakes of others.

Join us in this session, where we discuss 6 case studies in AI failure, and discover how you can avoid becoming yet another data science failure statistic.

You will learn how to avoid the following traps:

  • Hype surrounding complex new algorithms
  • Perverse incentives
  • Human error
  • Obsolescence in a rapidly changing world
  • Ignoring business rules and human expertise
  • Too much or too little transparency and explainability

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