Machine learning , as we all know it, relies on huge amounts of quality data and learns from human feedback. The Human-in-the-loop concept leverages both human and machine intelligence in tandem to manage the shortcomings of traditional ML. For example, in the case of scarce data, HITL expedites the labeling of tricky or novel data that a machine can’t process with confidence, thus reducing the potential for data-related errors or biases.
To build a fair system, we need to tackle biases in both AI and humans. Humans and machines need to work in tandem and augment each other to put this problem behind us. Just as humans are needed to manage bias in AI systems, AI can also work with humans on reducing their biases. AI systems can remove subjectivity arising due to irrelevant inputs, like gender, ethnicity and income.
As of 2021, pure AI is science fiction. For the foreseeable future, humans are going to be involved in different steps of machine learning, including dataset creation, algorithm development and final decision making in scenarios where the complexity of the decisions exceeds statistical encoding the inputs or when domain expertise is required.