Why Quality Matters
In order to accelerate the adoption of AI for defense and intelligence applications, it is critical to develop decision-grade, high-quality AI.
In a report from Appen, it was found that over just 50% of companies reported that data accuracy for AI is critical.
Despite saying this, 78% of the same group reported that the typical accuracy percentage of their data ranges from 1 to 80% accuracy.
This massive gap in accuracy leaves much to be desired. It brings to mind: what avenues of quality improvement can companies engage in? What could they consider to boost the accuracy of their machine learning models? Although 97% of companies reported the need for data accuracy, only 6% of those companies get 91 to 100% accuracy. This disparity showcases the need for improved quality management within the AI sphere.
It Starts with Quality Controls for ML Training Data
In many cases, the lack of quality in machine learning models originates with the training data: Not enough, wrong, or low-quality training data. A lack of leveraging highly trained data specialists and enabling tight coordination with key stakeholders familiar with the use case and data types involved in the AI initiative often leads to low-quality machine learning (ML) models. To solve this, it is important to leverage a Human-in-the-Loop (HITL) approach, in addition to data labeling automation tools (i.e., pre-labeling and ML-assisted labeling). 97% of companies say HITL is a key component for accurate model performance.
There are a number of different methods we utilize at Figure Eight Federal to ensure high-quality results in all of our data labeling projects:
- Test Questions
What truths can be used to test contributors’ understanding of a job before they start working on it? How can they be used during the job to ensure continued understanding?
- Quality Assurance Workflow
Use highly qualified contributors to review and correct annotations within the workflow.
- Dynamic Judgements
Majority votes are used to determine high confidence annotations and minimize the number of required judgements per unit.
- ML Validation
Use ML model predictions to validate human results.
Our ML-Assisted Data Labeling Platform can determine what work the annotator/data specialist excels at and then route them tasks based on that expertise. When multiple annotators/data specialists review the same task, we can measure the distribution of answers and automatically detect anomalies. Doing this allows for us to determine a trust score for the data; as well, the workers allow us to assign a quality score against all outputs for automating quality review workflow assignments. Low confidence results can be routed for peer and/or management review to ensure high quality results through human-machine collaboration. We believe that having a diverse set of quality control features increases the flexibility to optimize the quality for different types of data types and use cases. This is crucial in improving the quality of your AI models.
Why Figure Eight Federal
With Figure Eight Federal’s strategic ML-Assisted HITL annotation process, clients gain 100 times faster access than traditional human-only approaches to high-quality ML training data. We are able to leverage automation without compromising the quality of the data that machine learning models will learn from. Our ML validation capability is able to reduce human error by 35%, as well as reduce the required manual review to ensure high-quality results.
For defense and intelligence applications, quality is critical for empowering the future warfighter’s competitive advantage.
Schedule a demo with Figure Eight Federal to discover how we can improve the quality of the training data feeding your ML models.