How do you create feedback for AI learning systems?

1 Feedback types

Feedback for AI learning systems can be classified into two main types: explicit and implicit. Explicit feedback is when you directly tell the AI system what it did right or wrong, and how to correct or improve its actions or decisions. For example, you can rate, review, or annotate the output of an AI system, such as a recommendation, a translation, or a prediction. Implicit feedback is when you indirectly indicate your preferences, satisfaction, or behavior to the AI system, without explicitly stating them. For example, you can click, scroll, pause, or purchase something based on the output of an AI system, which can reveal your interests, needs, or goals.

2 Feedback design

When designing feedback for AI learning systems, there are some key considerations to keep in mind. Firstly, consider the purpose of the feedback - what does the AI system need to learn? Are you trying to improve its accuracy, relevance, diversity, or fairness? Or is it to optimize performance, efficiency, or user experience? Secondly, consider the source of the feedback - who is providing it? Is it reliable, diverse and representative? Thirdly, think about the timing of the feedback - when is it given? Is it immediate, delayed or periodic? And finally, consider the format of the feedback - how is it communicated to the AI system? Is it numerical, textual, visual or auditory? Is it binary, categorical, ordinal or continuous? Explict, implicit or hybrid?

3 Feedback methods

Feedback for AI learning systems can be implemented using different methods, depending on the type, design, and domain of the AI system. Supervised learning involves providing labeled data to the AI system, such as images of faces with their names as labels. Reinforcement learning utilizes rewards or penalties to reflect the quality or value of its actions or decisions. Active learning allows the AI system to request feedback from you when it is uncertain or confident about its output. Lastly, interactive learning involves a dialogue or collaboration between you and the AI system, where you can provide feedback, guidance, or suggestions and the AI system can provide feedback, explanations, or questions.

4 Feedback scenarios

Feedback for AI learning systems can be applied in various scenarios and domains, such as education, healthcare, and entertainment. For example, in education, an AI system can personalize the learning experience of students and teachers by recommending learning resources, activities, or paths based on feedback and progress. In healthcare, an AI system can improve the health outcomes and quality of care of patients and providers by diagnosing, treating, or preventing diseases and conditions. And in entertainment, an AI system can create more engaging and immersive experiences by generating or adapting content based on feedback and preferences. Feedback for AI learning systems is a complex topic that requires careful thought and experimentation. When done right, it can leverage the power of AI to enhance our lives in the digital age.

5 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?