Alternatives to confidence thresholds

Confidence thresholds are pre-defined levels of certainty that a system's prediction or decision must meet to be considered valid. They are used to filter out low-confidence outputs, preventing potentially erroneous actions and ensuring that only reliable information is used for subsequent processing or control.

At a glance

Executive summary

Confidence thresholds are a mechanism for determining when a system's output is reliable enough to be acted upon. They are crucial in applications where incorrect decisions can have significant consequences, such as in safety-critical systems or assistive robotics, to ensure robust and dependable operation.

TL;DR

If you need to ensure reliable decision-making based on system output, use confidence thresholds; if you need to quantify uncertainty or improve the accuracy of probability estimates, consider calibrated probabilities or calibration techniques.

Key points

Consider the criticality of the application and the potential impact of errors when setting thresholds.
Evaluate whether the system's output directly represents a probability or a score that needs interpretation.
Determine if the goal is to simply filter unreliable outputs or to actively improve the accuracy of probabilistic predictions.
Assess the need for explicit human-in-the-loop interaction or automated decision-making based on confidence.
Understand the specific domain requirements, such as those in assistive robotics or safety-critical systems, which may dictate stricter thresholding.

Our Take

## Our Take As the integration of assistive robotics into daily life becomes more prevalent, understanding the interplay between confidence thresholds and calibrated probabilities is crucial for enhancing user experience and safety. Confidence thresholds dictate the level of certainty required before a robotic system takes action, particularly in safety-critical applications such as assisting with Activities of Daily Living (ADLs). Research indicates that higher confidence thresholds can lead to more cautious behavior in robots, potentially limiting their effectiveness in dynamic environments (Klein et al., 2021). Calibrated probabilities, on the other hand, provide a framework for quantifying the uncertainty associated with robotic decisions. By employing calibration techniques, such as Platt scaling or isotonic regression, developers can ensure that the probabilities output by assistive robots accurately reflect real-world outcomes (Niculescu-Mizil & Caruana, 2005). This is particularly important in assistive control loops, where the robot must adapt to the user's needs while ensuring safety and reliability. The challenge lies in finding a balance between these two concepts. While a low confidence threshold may enable a robot to act more freely, it also increases the risk of errors in critical situations. Conversely, overly cautious robots may fail to assist users effectively, particularly in tasks that require prompt responses. A study by Kahn et al. (2022) suggests that dynamic adjustment of confidence thresholds based on real-time feedback can enhance both safety and user satisfaction. In conclusion, as we advance in the field of assistive robotics, a nuanced understanding of confidence thresholds and calibrated probabilities will be essential. By refining calibration techniques and ensuring robust assistive control loops, we can create systems that not only assist but also empower users in their daily lives.

Alternative	Difference	Papers (with confidence thresholds)	Avg viability
assistive robotics	—	1	—
calibrated probabilities	—	1	—
Activities of Daily Living	—	1	—
calibration techniques	—	1	—
safety-critical systems	—	1	—
assistive control loop	—

Alternative

Difference

Papers (with confidence thresholds)

Avg viability

assistive robotics

—

calibrated probabilities

—

Activities of Daily Living

—

calibration techniques

—

safety-critical systems

—

assistive control loop

—