Retailistic

Understanding and Managing the Risks of AI

Episode Summary

In this episode, Renee Hartmann and John Harmon discuss the risks and concerns associated with AI implementation. They highlight the importance of understanding and managing these risks to ensure the desired outcomes. The conversation covers topics such as model drift, hallucination, toxicity, biases, environmental sustainability, societal impact, and cybersecurity. They emphasize the need for companies to clean and organize their data, establish policies and procedures, and set clear business objectives when implementing AI. The episode concludes with a discussion on the future of risk mitigation in AI.

Episode Notes

Takeaways

• Understanding and managing the risks associated with AI implementation is crucial for achieving desired outcomes.
• Model drift, hallucination, and toxicity are potential risks of AI that need to be monitored and managed.
• Data cleaning and organization are essential for obtaining accurate and unbiased results from AI models.
• Companies should establish policies and procedures to ensure the ethical and responsible use of AI.
• Environmental sustainability and societal impact should be considered when implementing AI.
• Cybersecurity measures are necessary to protect AI models and data from potential breaches.
• Setting clear business objectives is key to developing an effective AI strategy.
• The future of risk mitigation in AI includes ongoing advancements in data management, cybersecurity, and ethical guidelines.

"With great power comes great responsibility."

"AI models want to get better over time. They want to give you better results over time."

"There's this tendency to get a little over reliant on AI without really testing it and making sure that it's correct."