On Quantifying and Understanding the Role of Ethics in AI Research: A Historical Account of Flagship Conferences and Journals

14 pages•Published: September 17, 2018

Marcelo Prates, Pedro Avelar and Luis Lamb

Abstract

Recent developments in AI, Machine Learning and Robotics have raised concerns about the ethical consequences of both academic and industrial AI research. Leading academics, businessmen and politicians have voiced an increasing number of questions about the con- sequences of AI not only over people, but also on the large-scale consequences on the the future of work and employment, its social consequences and the sustainability of the planet. In this work, we analyse the use and the occurrence of ethics-related research in leading AI, machine learning and robotics venues. In order to do so we perform long term, historical corpus-based analyses on a large number of flagship conferences and journals. Our experiments identify the prominence of ethics-related terms in published papers and presents several statistics on related topics. Finally, this research provides quantitative evidence on the pressing ethical concerns of the AI community.

Keyphrases: artificial intelligence, ethics, machine bias, machine learning

In: Daniel Lee, Alexander Steen and Toby Walsh (editors). GCAI-2018. 4th Global Conference on Artificial Intelligence, vol 55, pages 188-201.

Links:	https://easychair.org/publications/paper/Z7D4
	https://doi.org/10.29007/74gj

BibTeX entry

@inproceedings{GCAI-2018:Quantifying_Understanding_Role_Ethics,
  author    = {Marcelo Prates and Pedro Avelar and Luis Lamb},
  title     = {On Quantifying and Understanding the Role of Ethics in AI Research: A Historical Account of Flagship Conferences and Journals},
  booktitle = {GCAI-2018. 4th Global Conference on Artificial Intelligence},
  editor    = {Daniel Lee and Alexander Steen and Toby Walsh},
  series    = {EPiC Series in Computing},
  volume    = {55},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/Z7D4},
  doi       = {10.29007/74gj},
  pages     = {188-201},
  year      = {2018}}

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