Abstention Is a Product Feature

NOTE-004 / question

Central question

How should ML products behave when the model is not confident enough to make a safe decision?

NOTE-004 / key ideas

Abstention as maturity

The essay argues that knowing when not to decide is a core product capability.

Coverage as a KPI

Selective prediction should track not only accuracy but also the percentage of cases the model is willing to handle.

Review workflows

Human-in-the-loop queues, thresholding, calibration, and UX design determine whether abstention actually helps.

NOTE-004 / skills demonstrated

Model calibration

Shows understanding of confidence, thresholds, and the cost of uncertain predictions.

ML product design

Connects model behavior to review capacity, UX, and operational safety.

Evaluation beyond accuracy

Treats coverage, abstention, and human review as measurable system properties.

NOTE-004 / related work

Fake News Detector · Graph-RAG Engine

NOTE-004 / source

This page summarizes and positions the essay inside the honardoust.codes lab index. The full original essay is kept in its GitHub repository.