What is human-in-the-loop?
Human-in-the-loop is a blend of supervised machine learning and active learning where humans are involved in both the training and testing stages of building an algorithm. This practice of uniting human and machine intelligence creates a continuous feedback loop that allows the algorithm to produce better results each time.
Human-in-the-loop can be used for any deep learning artificial intelligence (AI) project including natural language processing (NLP), computer vision and transcription. Additionally, human-in-the-loop can be used in conjunction with AI content moderation systems to quickly and effectively analyze user-generated content. This is referred to as human-in-the-loop decision-making, where content is flagged by the AI and human moderators review what has been flagged and confirm whether the machine was correct in order to enhance the algorithm's decision-making.
When it comes to human-in-the-loop AI training, there are three important stages:
- Data annotation: Human data annotators label the original data, which includes both input data and the corresponding expected output.
- Training: Human machine learning teams then input the correctly labeled data to train the algorithm. Based on this data, the algorithm can uncover insights, patterns and relationships within the dataset. The ultimate goal is for the algorithm to be able to make accurate decisions when later presented with new data.
- Testing and evaluation: In this stage, the human’s role is to correct any inaccurate results that the machine produced. Humans focus on correcting results where the algorithm is not confident about a judgment. This is known as active learning.
Benefits of human-in-the-loop
The harmonious relationship between people and artificial intelligence has a number of benefits, including:
- Ensuring accuracy: As humans continue to fine-tune the model’s responses to various cases, the algorithm becomes more accurate and more consistent. In the content moderation space, there are limitations to how much of the analysis can be automated. AI can miss content that should be flagged (a false positive), and they can also incorrectly flag content that may be harmless (a false negative). Humans are essential in the content moderation process as they are able to interpret things such as context, multilingual text and can take into consideration cultural, regional and socio-political nuances of local markets.
- Enhancing data collection: Machine learning models typically require large amounts of data to be successful. In situations where large datasets are not available, the algorithm may not have enough information to learn from which could produce unreliable results. Having a human-in-the-loop helps to create data and can ensure accuracy.
- Reducing bias: When AI programs are designed by humans and are based on historical data, they risk perpetuating inequalities. Having a human-in-the-loop can detect and correct bias early.
- Increasing efficiency: Machine intelligence can save significant time and costs by sifting through and pairing down large amounts of data. The task can then be passed on to humans to complete a final sort. While the entirety of the process cannot be automated, a significant portion of it is, saving time.