# Confident AI

arXiv: https://arxiv.org/abs/2202.05957 · HTML: https://mlsanity.com/paper/2202.05957/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision](https://mlsanity.com/paper/2109.08203/) (2021)
- [Optimal strategies for reject option classifiers](https://mlsanity.com/paper/2101.12523/) (2021)
- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [Dynamic-structured Semantic Propagation Network](https://mlsanity.com/paper/1803.06067/) (2018)
- [Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier](https://mlsanity.com/paper/2007.09898/) (2020)
- [Hierarchical Novelty Detection for Visual Object Recognition](https://mlsanity.com/paper/1804.00722/) (2018)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [On the Role of Dataset Quality and Heterogeneity in Model Confidence](https://mlsanity.com/paper/2002.09831/) (2020)
- [Posterior Adaptation With New Priors](https://mlsanity.com/paper/2007.01386/) (2020)

## Built on by (most cited first, 6 of 6)

- [MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models](https://mlsanity.com/paper/2501.15000/) (2025)
- [Enterprise Large Language Model Evaluation Benchmark](https://mlsanity.com/paper/2506.20274/) (2025)
- [Flowchart2Mermaid: A Vision-Language Model Powered System for Converting Flowcharts into Editable Diagram Code](https://mlsanity.com/paper/2512.02170/) (2025)
- [BEADs: Bias Evaluation Across Domains](https://mlsanity.com/paper/2406.04220/) (2024)
- [Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs](https://mlsanity.com/paper/2509.17314/) (2025)
- [Graph-Structured Rubrics: Compiling Rubrics into Typed Evaluation Graphs for LLM Judges](https://mlsanity.com/paper/2608.12097/) (2026)

## Abstract (arXiv)

In this paper, we propose "Confident AI" as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported results. The 4 basic tenets of Confident AI are Repeatability, Believability, Sufficiency, and Adaptability. Each of the tenets is used to explore fundamental issues in current AI/ML systems and together provide an overall approach to Confident AI.