Humility in AI: Building Trustworthy and Ethical AI Systems

AI is becoming ubiquitous. More and more critical decisions are automated through machine learning models, determining the future of a business or making life-altering decisions for real people. The number of critical touch points is growing exponentially with the adoption of AI.

But with the incredible pace of the modern world, AI systems continually face new data patterns, which make it challenging to return reliable predictions. This could mean a catastrophic failure by the system down the line, especially without proper guardrails. These failures can also significantly erode human trust in AI, rendering it ineffective for real-world applications in many industries.

With the rising stakes, AI systems must be built to be humble, just like humans. AI should know when it is not sure about the right answer to transfer the critical decision-making process back to people.

In this ebook, we explore the concept of humility in AI systems and how it can be applied to existing solutions to ensure their trustworthiness, ethicality, and reliability in a fast-changing world.

Download this ebook to learn:

  • The basic concepts behind humility in AI
  • What makes AI systems susceptible to performance and accuracy issues
  • How AI systems can exhibit humility
  • What it takes to develop a systemic, qualified, and actionable understanding of the potential areas for weakness in AI systems
  • How humility in AI systems impacts their decisions
  • Real-life examples of business problems and issues with the underlying data used for predictions that may benefit from a humility framework
  • How humble AI system can improve tactical and strategic decisions
  • What actions an automated system should perform when it’s not sure about its predictive output
  • How DataRobot tackles predictive uncertainty with its Humble AI capability


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