Humans play a great role in data-driven products , especially that leverages deep tech and the power of Artificial Intelligence (AI). Not only being consumers of AI services , but people’s implicit feedback on a particular service and ratings does help a lot in improving the product.Human feedback provides an array of signals that can be used to teach the machine on how to improve itself.
Popular examples include a self-driving car getting better and better at making decisions while observing a human driver or a chess engine becoming more smarter while competing with more advanced players around the globe.
Advancements have been on a large scale in developing AI systems through usage of cloud computing , building more robust and fast architectures but understanding how technology should interact with people in human-in-the-loop systems is something that has not been developed at pace.
AI systems are sometimes trained on data that can be biased. Human feedback can detect the biases early and stop algorithms from making biased decisions , which in turn can reduce prejudiced , stereotyped and wrong judgements.
Having human-in-the loop can be used for data labelling , which in turn can create more employment. Getting access to quality and labelled data is difficult. Unless we have access to good quality labelled and diversified data , the AI will not be able to make good decisions. Since humans are needed to train many AI systems , AI-boom will create more employment in future.
AI systems when combined with human support can perform surprisingly well on certain tasks. Take for example doing a complex surgery or identifying breast cancer / diabetic retinopathy from images - AI combined with human feedback can speed up the process. The system becomes much more efficient , accurate and precise in it’s judgements.