AI Degradation Prediction and Detection
by Melany Joy Beck
August 12, 2024
Eliminate or reduce the effects of AI degradation with insight into changes to ensure the consistent output of AI/ML systems.
Artificial intelligence (AI) models are dynamic entities, evolving as they process data. While adaptability can enhance model accuracy, it can also lead to negative changes. These negative or undesirable changes are known as AI degradation. If left unchecked, AI degradation can eventually lead to model collapse.
According to a recent study by researchers from Harvard, MIT, the Monterrey Institute of Technology and Higher Education, and the Whitehead Institute for Biomedical Research, the team observed model degradation in over 90 percent of cases.
“AI models do not remain static,” reports the study. “Even if they achieve high accuracy when initially deployed, and even when their data comes from seemingly stable processes.”
For today’s businesses, administrations, leadership boards, service providers, and laboratories, the impact of AI degradation on neural network outputs can be detrimental to the goals of the organization.
Monitor AI Degradation
Because of the “black box” nature of many AI/ML systems, maintaining an acceptable level of visibility or awareness has provided numerous challenges. Over time, AI degradation results in reduced functionality, accuracy, and trust.
“Authentrics.ai has developed tools that provide quantitative telemetry from within neural networks, allowing businesses to monitor and adjust the effects of specific data inputs on AI outputs,” said John Derrick CEO of Authentrics.ai. “This level of transparency is essential for addressing the ‘black box’ nature of AI and for ensuring that AI systems can be trusted to operate reliably and ethically.”
Monitoring for changes, particularly any degradation in the AI/ML output or values within the model itself, and proactively setting alerts for predefined thresholds can help maintain optimal functionality.
Current approaches are focused on monitoring the AI/ML output to alert when symptoms start to become detectable. As these functions are the result of shifts and changes internally, earlier detection is possible through a more thorough analysis of model evolution.
Gaining the ability to monitor AI degradation, including the establishment of alerts at predefined thresholds can ensure that output remains within an acceptable range.
Equipped with this insight into when changes exceed normal historical shifts, organizations can more closely tune their AI/ML systems to purpose.
AI Output Quality
To maintain AI model output quality, it is essential to use the right AI analysis tools to evaluate a model for performance degradation.
With Authentrics.ai solutions for degradation detection you can:
- Detect AI degradation or ML degradation within systems
- Assess AI and ML performance over time to compare changes against baselines
- Take snapshots of the AI instance and compare to previous versions of the model
- Identify KPIs and set thresholds to detect and alert on unexpected internal changes
- Assess impact of training content on output and performance
The goal is to improve output quality with AI diagnostics to detect any model drift, ML degradation, or unhealthy trends in the model’s evolution.
AI Degradation Detection and Appraisal
Once you have leveraged the resources to set up a custom neural network, it is critical to conduct an AI appraisal regularly to determine ongoing output integrity and internal health.
Artificial intelligence authentication and analysis tools are available to protect your investment. Start by setting up a cadence to run AI diagnostics using known variables to measure performance degradation over time.
When degradation is detected, actions can be taken to reestablish AI trust. For example, it may be necessary to identify and remove certain training content. An effective AI analysis solution can provide actionable insights into recent and longitudinal changes leading to AI degradation.
Learn how to detect degradation in the generated output of your AI/ML neural network by getting in touch with our AI content optimization experts today.
