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The AI in Business Podcast
[Achieving AI ROI] Quantifying AI Risk - with Head of AI Insurance at Munich Re, Michael Berger
Mar 15, 2022
20:06
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Head of Insure AI at Munich Re, Michael Berger, discusses quantifying and managing AI risk. Topics include evaluating risks in AI projects, minimizing uncertainties, incorporating uncertainty information in AI predictions, and adopting new AI methods for increased confidence in applications.
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Quick takeaways
- Assessing and mitigating risks is crucial for achieving ROI in AI projects.
- Quantifying risks involves evaluating factors like predictive performance, fairness, operational costs, sustainability impacts, and cybersecurity vulnerabilities.
Deep dives
Importance of Considering Risk in AI ROI
When embarking on AI projects, it's crucial to assess and mitigate risks to ensure a positive return on investment. Investments in AI involve costs for data acquisition, model building, maintenance, and cloud deployment. Uncertainty in AI performance and potential liabilities, such as discrimination issues, can impact the expected returns. Evaluating risks upfront enables companies to make informed decisions on pursuing AI projects or sticking with traditional approaches.
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