# On Ensuring that Intelligent Machines Are Well-Behaved

arXiv: https://arxiv.org/abs/1708.05448 · HTML: https://mlsanity.com/paper/1708.05448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Risk-Aware Active Inverse Reinforcement Learning](https://mlsanity.com/paper/1901.02161/) (2019)
- [ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs](https://mlsanity.com/paper/2302.01275/) (2023)
- [A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting](https://mlsanity.com/paper/2208.08374/) (2022)

## Abstract (arXiv)

Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human performance on various tasks. Ensuring that they are well-behaved---that they do not, for example, cause harm to humans or act in a racist or sexist way---is therefore not a hypothetical problem to be dealt with in the future, but a pressing one that we address here. We propose a new framework for designing machine learning algorithms that simplifies the problem of specifying and regulating undesirable behaviors. To show the viability of this new framework, we use it to create new machine learning algorithms that preclude the sexist and harmful behaviors exhibited by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.