What is the concept of Human-involved Machine Learning?
Machine learning models are commonplace but often exhibit mistakes. Their predictions, which can directly impact people's real-world situations like loan approval processes, should ideally undergo some level of human scrutiny. Additionally, utilizing supervised learning can be challenging due to inadequate labeled data.
A strategy to solve this problem involves taking help from experts to label a portion of the data to prepare a model initially. The subsequent high-confidence predictions of this model are then used to label more data, whereas low-confidence predictions are set aside for human inspection. This iterative cycle usually sees improvements with each round.
In essence, Human-involved machine learning (HITL) relies on human input to augment the quality of data training the ML models. This approach involves labeling high-quality data, model training, and further data annotation.
Human-involved machine learning marries human intellect with machine intelligence, resulting in a cooperative amalgamation. It is applicable across deep learning AI projects, including transcription, computer vision, and Natural Language Processing (NLP). It is particularly useful:
- In roles where algorithmic errors can cost hugely, such as predicting medical diagnoses, prognoses, and treatments.
- In situations with a dearth of data records for sound decisions, humans step in till sufficient training and testing data comes in for machines to take over.
One of the star advantages of HITL is driving excellent results through precision. Since the efficiency of AI/ML models is highly connected with data quality, data labeling propels ML models to generate more accurate predictions.
Despite the necessity of data labeling, constructive feedback on HITL results sharpens the model accuracy and ensures top-quality outputs. Also, humans perform consistently better than AI when data is biased or incomplete. Therefore, human involvement is a vital factor that elevates HITL's accuracy.
HITL, despite being highly effective, is also labor-intense, time-consuming, and costly. Labeling data could involve tagging texts, images, or audio recordings with classifications, executed internally, externally, or through crowdsourcing, all of which come at a price.
Apart from labeling, HITL also incurs costs for software. While open-source platforms offer free software, it requires a dedicated IT team to handle and alter the code. Conversely, closed-source and in-house alternatives aren't free. Moreover, providing feedback for HITL is laborious, making it not cost-effective.