# (Machine) Learning to Be Like Thee? For Algorithm Education, Not Training

arXiv: https://arxiv.org/abs/2305.12157 · HTML: https://mlsanity.com/paper/2305.12157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 101 references that are in the map.
- First posted on arXiv 20 May 2023.

## Builds on (most cited first, 8 of 8)

- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Collective Intelligence for Deep Learning: A Survey of Recent Developments](https://mlsanity.com/paper/2111.14377/) (2021)
- [Representative & Fair Synthetic Data](https://mlsanity.com/paper/2104.03007/) (2021)
- [Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions (preprint)](https://mlsanity.com/paper/2108.06918/) (2021)
- [A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission](https://mlsanity.com/paper/2206.06279/) (2022)
- [The Golden Rule as a Heuristic to Measure the Fairness of Texts Using Machine Learning](https://mlsanity.com/paper/2111.00107/) (2021)

## Abstract (arXiv)

This paper argues that Machine Learning (ML) algorithms must be educated. ML-trained algorithms moral decisions are ubiquitous in human society. Sometimes reverting the societal advances governments, NGOs and civil society have achieved with great effort in the last decades or are yet on the path to be achieved. While their decisions have an incommensurable impact on human societies, these algorithms are within the least educated agents known (data incomplete, un-inclusive, or biased). ML algorithms are not something separate from our human idiosyncrasy but an enactment of our most implicit prejudices and biases. Some research is devoted to responsibility assignment as a strategy to tackle immoral AI behaviour. Yet this paper argues that the solution for AI ethical decision-making resides in algorithm education (as opposed to the training) of ML. Drawing from an analogy between ML and child education for social responsibility, the paper offers clear directions for responsible and sustainable AI design, specifically with respect to how to educate algorithms to decide ethically.