How do you navigate uncertainty in AI decision making?

1 Understand the sources of uncertainty

Uncertainty in AI can arise from different sources, such as data quality, model complexity, algorithmic bias, or environmental changes. You need to identify and assess the potential sources of uncertainty in your AI project, and how they may affect your goals and expectations. For example, if your data is noisy, incomplete, or outdated, your AI model may not perform well or generalize to new situations. If your model is too complex or opaque, you may not be able to explain or validate its decisions. If your algorithm is biased or unfair, you may face ethical or legal issues.

2 Choose the right level of uncertainty

Not all uncertainty is bad or avoidable. Sometimes, uncertainty can reflect the complexity or variability of the real world, and help you explore different scenarios or alternatives. You need to choose the right level of uncertainty for your AI decision making, depending on your context and objectives. For example, if you are using AI for strategic planning or innovation, you may want to embrace some uncertainty and use it as a source of creativity or learning. If you are using AI for operational efficiency or compliance, you may want to reduce uncertainty and use it as a source of quality or reliability.

3 Manage the risks of uncertainty

Uncertainty in AI can also pose risks, such as errors, failures, or harms. You need to manage the risks of uncertainty in your AI decision making, and mitigate or prevent them as much as possible. For example, you can use data validation, model testing, algorithm auditing, or human oversight to check and correct the accuracy, completeness, and consistency of your AI results. You can also use risk assessment, contingency planning, feedback mechanisms, or ethical frameworks to identify and address the potential impacts or consequences of your AI actions.

5 Learn from the uncertainty

Uncertainty in AI is not a static or fixed condition, but a dynamic and evolving one. You need to learn from the uncertainty in your AI decision making, and use it as an opportunity to improve or adapt your AI systems. For example, you can use data analysis, model evaluation, algorithm refinement, or human feedback to monitor and measure the performance, validity, or fairness of your AI solutions. You can also use data collection, model retraining, algorithm updating, or human collaboration to enhance or modify your AI capabilities.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?