How can you improve learning in AI project feedback loops?

It is essential to make feedback loops short and frequent, involve a variety of stakeholders in the feedback process, use a variety of feedback methods, prioritize feedback that is relevant to the project goals, use feedback to make changes to the project and communicate the results of input to the project team. Additionally, defining clear goals and expectations that align with the project vision and objectives is crucial. Breaking the project into smaller tasks and deliverables can help improve feedback loops. Incorporating feedback from diverse users and stakeholders can help identify and address any issues or biases in the system. Evaluating and iterating the design based on feedback and data

To improve learning in AI project feedback loops, it is important to make feedback loops short and frequent, involve a variety of stakeholders in the feedback process, use a variety of feedback methods, prioritize feedback that is relevant to the project goals, use feedback to make changes to the project, and communicate the results of feedback to the project team. In addition, it is important to make sure that feedback is specific and actionable, encourage feedback from all stakeholders, use feedback to improve the project's documentation and training materials, and celebrate successes and learn from failures.

Babu's comments are good. One way to add feedback and to make it specific, is to incorporate into the technology a specifically reserved tag for that purpose which always include who the user is ( assuming we are also able to classify users) and in fact award some sort of points system to users to reward them for doing so. Given feed back from some people is more valuable than others. Think of the 5 Ws from journalism and how the user experience led them to making the comment; e.g. in a questionnaire, what series of questions already answered led them to provide the feed back.

Feedback loops are key to make your model testing a continuous improvement, rather than a one-time task. If your context has been only partially described by your training and test data, then integrating feedback loop in production might allow you to gather additional data and to label predictions as good, or bad. For example, if you are delivering a classification tool for quality control in manufacturing, allow the line operator to confirm, or correct, the prediction can help you have more realistic performance metrics (eg: precision, recall, etc) and realize if the production realm differs, in some way, from the one described by your training and test data.

I, and people who work with me, make large extensively integrated professional questionnaires ( some over 1,000 questions, although there is in fact no inherent limit) and most start with less than 20 questions. I was also managing a team of 120+ accountants in seven offices. One of my recently hired staff ( he had been with a large international firm before) came to my office one day and thanked me for requiring him to use the interactive and integrated questionnaire. He had failed professional exams the prior to joining us. He attributed the knowledge gained using the system on audit clients, to finally passing tax exam. This feed back led me to start integrating questionnaires for different professional development courses.

AI efforts often tend to become horizontal span of inter-connected operations to realize the perceived benefits. Feedback loops becomes precipitating a multi-stakeholder agreement and collaborative participation. Any change brings about a change in status quo, AI brings a lot more critical introspection with new progressive understandings at every stage. So establishing and empowering AI program management roles, practices, markers, becomes pivotal reference in designing effective feedback loops.

A Scrum Master ensures effective Agile adoption, crucial given AI's iterative nature. They can be a key addition to an AI team. They facilitate task breakdown into sprints, producing tangible outcomes regularly. Organizing daily stand-ups, addressing impediments, and conducting retrospectives fall under their purview. They assist in task prioritization, aligning with stakeholder expectations. Given AI's cross-functional teams, from data scientists to domain experts, the Scrum Master fosters clear communication and timely feedback integration. Their role also emphasizes continuous learning, vital in the dynamic AI landscape.

For feedback loops it can be important to distinguish the severity level of errors by the AI model. Separate the kind of errors that are less critical than the ones that should never happen in your production setting. Then you can focus on improving your models under similar conditions (without overfitting). It can also help to establish (and update) critical error suites to check if your model should be put in production or not, based on the results for those test suites.

Reinforcement Learning from Human Feedback (RLHF) is crucial in refining AI models. By integrating human intuition into AI training, RLHF ensures that models align more closely with human values and preferences, especially in areas like natural language processing where outcomes are nuanced. Traditional reinforcement learning can falter when rewards are ambiguous or hard to define algorithmically. RLHF bridges this gap, making AI responses more relevant and trustworthy. It elegantly combines quantitative algorithmic learning with qualitative human judgment, enhancing the overall efficacy of AI systems.