The Cornerstone of Reliable AI: Estimating Model Competence

Building reliable and trustworthy AI systems requires more than high test accuracy; it demands an understanding of a model’s real-time competence, especially as it learns new information. Traditional methods often fail to provide this crucial, on-the-fly assessment. The research in my attached papers focuses on novel, semi-supervised techniques to accurately estimate an AI system’s performance, which is vital for safe and effective deployment in high-stakes environments like robotics.

Trust and Competence in Adaptive AI Systems

When deploying Artificial Intelligence, especially in scenarios involving a human partner—such as an assistant robot or an interactive diagnostic tool—the ability of the AI to assess its own competence is paramount. This internal self-evaluation is a key step toward building trust and ensuring reliable outcomes.

In incremental and active learning settings, AI models continuously update their knowledge. For instance, a robot that learns new objects from a human teacher must be able to accurately judge its learning progress to ensure it can successfully carry out a desired task. If the system’s estimated accuracy does not meet a required threshold, the task may need to be delegated or further, targeted training requested.

Standard accuracy estimation methods—such as k-fold cross-validation or relying solely on a small, labeled test set—are often too slow, label-intensive, or unreliable for real-time, adaptive applications.

Novel Methods for Efficient Performance Assessment

To address this challenge, my colleagues and I developed two novel, semi-supervised approaches for efficient accuracy estimation. These methods move beyond basic performance metrics to provide a robust, predictive assessment of a classifier’s competence on unseen data.

1. Configram Estimation (CGEM)

The Configram Estimation (CGEM) approach is a semi-supervised method designed to predict the accuracy of any classifier that delivers confidence scores for its decisions.

• It functions by calculating classification confidences for unlabeled samples and then training an auxiliary offline regression model to predict the system’s accuracy on novel data.
• CGEM has been investigated for applications in incremental object learning, demonstrating its applicability in a realistic setting with a cooperative inventory assistant robot. This method was shown to clearly outperform standard supervised approaches for accuracy estimation.

2. Distogram Estimation (DGE)

For instance-based classifiers, we introduced the Distogram Estimation (DGE) approach.

• This method calculates relative distances to samples, allowing an offline regression model to be trained to predict the classifier’s accuracy on unseen data.
• DGE requires only a few initial supervised samples for training, enabling it to be applied instantaneously on novel data afterwards. Evaluation, which included a robot object recognition task, showed that DGE clearly outperforms two baseline methods for both random and active selection of incremental training examples.

The Importance in Robotics and Beyond

The ability to accurately and efficiently estimate a model’s competence is fundamental to deploying professional AI solutions.

In Robotics, these methods are essential for optimizing the workload distribution between human and machine. By reliably estimating its learning progress, a robot can judge its competence and ensure safer, more efficient human-robot collaboration.

Beyond robotics, the principles have significant implications for other industries:

• Healthcare: Systems using incremental learning for diagnostics can self-assess their certainty before presenting a result on novel patient data.
• Finance: Models can dynamically evaluate their reliability in response to new, unexpected market shifts.
• Manufacturing: Predictive maintenance systems can assess the confidence of their failure warnings in real-time.

In all high-stakes applications, moving beyond static performance scores to integrate real-time, semi-supervised competence assessment is crucial for developing and maintaining truly reliable and effective AI.

All cited papers, including research on the Configram and Distogram Estimation approaches, are available for review under the “Publication” link https://climberg.de/publications.