# P4AI: Approaching AI Ethics through Principlism

arXiv: https://arxiv.org/abs/2111.14062 · HTML: https://mlsanity.com/paper/2111.14062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Towards the Science of Security and Privacy in Machine Learning](https://mlsanity.com/paper/1611.03814/) (2016)
- [Generative Face Completion](https://mlsanity.com/paper/1704.05838/) (2017)
- [Reconsidering CO2 emissions from Computer Vision](https://mlsanity.com/paper/2104.08702/) (2021)

## Built on by (most cited first, 1 of 1)

- [KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art](https://mlsanity.com/paper/2210.04366/) (2022)

## Abstract (arXiv)

The field of computer vision is rapidly evolving, particularly in the context of new methods of neural architecture design. These models contribute to (1) the Climate Crisis - increased CO2 emissions and (2) the Privacy Crisis - data leakage concerns. To address the often overlooked impact the Computer Vision (CV) community has on these crises, we outline a novel ethical framework, textitP4AI: Principlism for AI, an augmented principlistic view of ethical dilemmas within AI. We then suggest using P4AI to make concrete recommendations to the community to mitigate the climate and privacy crises.