How do you deal with the uncertainty and ambiguity of your AI system deployment outcomes and impacts?

1 Define your goals and metrics

Before you deploy your AI system, you need to have a clear and measurable definition of what you want to achieve and how you will evaluate your success. This will help you set realistic expectations, track your progress, and identify any gaps or problems. You should also consider the ethical, social, and legal implications of your AI system, and how you will address them in your deployment strategy.

2 Test and validate your AI system

You can reduce the uncertainty and ambiguity of your AI system deployment by testing and validating your system in different scenarios and contexts. You should use a variety of data sources, methods, and tools to assess the accuracy, reliability, robustness, and fairness of your AI system. You should also involve your stakeholders, such as users, customers, partners, or regulators, in the testing and validation process, and collect their feedback and input.

3 Monitor and adjust your AI system

Once you deploy your AI system, you need to monitor and adjust it regularly to ensure that it meets your goals and metrics, and that it does not cause any unintended or harmful consequences. You should use data-driven and user-centric approaches to measure the performance, behavior, and impact of your AI system, and to identify any issues or opportunities for improvement. You should also communicate and collaborate with your stakeholders, and respond to their needs and concerns.

4 Learn and iterate your AI system

You can deal with the uncertainty and ambiguity of your AI system deployment by adopting a learning and iterative mindset. You should treat your deployment as an ongoing experiment, rather than a one-time event, and use the feedback and data you collect to refine and optimize your AI system. You should also embrace the uncertainty and ambiguity as sources of innovation and discovery, and use them to explore new possibilities and solutions.

5 Document and share your AI system

You can also deal with the uncertainty and ambiguity of your AI system deployment by documenting and sharing your system with others. You should document your goals, metrics, methods, results, challenges, and lessons learned from your deployment, and make them transparent and accessible to your stakeholders and the wider community. You should also share your AI system with other developers, researchers, or practitioners, and seek their feedback, advice, or collaboration.

6 Build trust and confidence in your AI system

Finally, you can deal with the uncertainty and ambiguity of your AI system deployment by building trust and confidence in your system among your stakeholders and the public. You should demonstrate the value, quality, and safety of your AI system, and explain how it works, what it does, and why it does it. You should also respect the rights, preferences, and expectations of your users and customers, and ensure that they have control, consent, and recourse over your AI system.

7 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?