# Speculations on Uncertainty and Humane Algorithms

arXiv: https://arxiv.org/abs/2408.06736 · HTML: https://mlsanity.com/paper/2408.06736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 91 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Is Power-Seeking AI an Existential Risk?](https://mlsanity.com/paper/2206.13353/) (2022)
- [Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis](https://mlsanity.com/paper/2210.04714/) (2022)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)
- [GPT Takes the Bar Exam](https://mlsanity.com/paper/2212.14402/) (2022)
- [Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models](https://mlsanity.com/paper/2305.13712/) (2023)
- [X-Risk Analysis for AI Research](https://mlsanity.com/paper/2206.05862/) (2022)
- [GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models](https://mlsanity.com/paper/2310.00737/) (2023)
- [Stability of clinical prediction models developed using statistical or machine learning methods](https://mlsanity.com/paper/2211.01061/) (2022)
- [Bias and unfairness in machine learning models: a systematic literature review](https://mlsanity.com/paper/2202.08176/) (2022)
- [A survey on bias in machine learning research](https://mlsanity.com/paper/2308.11254/) (2023)

## Abstract (arXiv)

The appreciation and utilisation of risk and uncertainty can play a key role in helping to solve some of the many ethical issues that are posed by AI. Understanding the uncertainties can allow algorithms to make better decisions by providing interrogatable avenues to check the correctness of outputs. Allowing algorithms to deal with variability and ambiguity with their inputs means they do not need to force people into uncomfortable classifications. Provenance enables algorithms to know what they know preventing possible harms. Additionally, uncertainty about provenance highlights the trustworthiness of algorithms. It is essential to compute with what we know rather than make assumptions that may be unjustified or untenable. This paper provides a perspective on the need for the importance of risk and uncertainty in the development of ethical AI, especially in high-risk scenarios. It argues that the handling of uncertainty, especially epistemic uncertainty, is critical to ensuring that algorithms do not cause harm and are trustworthy and ensure that the decisions that they make are humane.