This is work in progress, most of the references are from 2019, 2020.

I believe that while working on AI projects, one should also be aware of the non-technical aspects of AI. While everything is digital and out of sight, AI actually has a profound impact on the environment and society. The ethical challenges of self-driving car accidents and the alarming energy consumption of deep learning algorithms are some examples. It is crucial to stay up to date with these challenges in order to have a holistic view of the consequences of AI research.

In this post, I suggest some very interesting references revolving around the impact of AI. Naturally, like all opinion pieces, some of them should be read with hindsight and a critical mind.

Table of Contents:

• Some resources on AI
• Some interesting reports on AI
• About the environment
• About ethics and social impact
• About bias and fake news
• About privacy
• About the data scientist job

• Data Analytics Post (French): "Nouveau média d’information et de réflexion autour des « data sciences » porté par le master MVA de l’ENS Paris-Saclay. Décryptage des problématiques de traitement et d'analyse des données."
• Financial Times
• Harvard Business Review
• McKinsey & Company
• MIT Technology Review
• The Economist
• The Guardian
• Le Monde (French)
• The New York Times
• Les Echos (French)

Some newsletters:

• The Algorithm by MIT Technology Review
• The Batch by deeplearning.ai
• Medium Daily Digest from Towards Data Science and Towards AI

• Francis Bach / Intelligence artificielle, la réalité derrière l'emballement / Académie des sciences / Nov. 5, 2020 (French)
  Details

  Great 3 pages review of the main current challenges surrounding AI, explained by a researcher at Inria in very simple words for a wide audience, holistic view.
• Nathan Benaich and Ian Hogarth / State of AI Report 2020 / Oct. 1, 2020
• The data economy, Mirror worlds / The Economist / Feb. 20, 2020
• Sam Ransbotham, Shervin Khodabandeh, Ronny Fehling, Burt LaFountain, and David Kiron / Winning With AI: Pioneers Combine Strategy, Organizational Behavior, and Technology / MIT Sloan Management Review in collaboration with BCG / Oct. 15th 2019
• Artificial Intelligence Special Report / The New York Times / Oct. 21, 2018
• Cédric Villani / For a Meaningful Artificial Intelligence: Towards a French and European Strategy / AI For Humanity / Mar. 29, 2018
  Follow-up YouTube video: Pascal Boniface avec Cédric Villani / Géopolitique de l'intelligence artificielle / Comprendre le monde / Dec. 2, 2020 (French)
• AI in business: GrAIt expectations / The Economist / Mar. 28, 2018

• Kamal Goyal / AI Computing Emits CO₂. We Started Measuring How Much. / BCG GAMMA - Medium / Nov. 30, 2020
• Tech must help combat climate change, says Sundar Pichai / The Economist / Nov. 17, 2020
• Pierre Vandeginste / L'IA s'interroge sur sa voracité énergétique / Data Analytics Post / Nov. 5, 2020 (French)
Details
Very exhautive and holistic view of the challenges of AI with regards to the environment.
• Edmund L. Andrews / AI's Carbon Footprint Problem / Stanford HAI / July 2, 2020
• Danny Hernandez and Tom B. Brown / Measuring the Algorithmic Efficiency of Neural Networks / OpenAI / May 5, 2020
  Details

  "We're releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet classification has been decreasing by a factor of 2 every 16 months. Compared to 2012, it now takes 44 times less compute to train a neural network to the level of AlexNet (by contrast, Moore’s Law would yield an 11x cost improvement over this period). Our results suggest that for AI tasks with high levels of recent investment, algorithmic progress has yielded more gains than classical hardware efficiency."
• Karen Hao / AI researchers need to stop hiding the climate toll of their work / MIT Technology Review / Aug. 2, 2019
• Martin Giles / Is AI the next big climate-change threat? We haven’t a clue / MIT Technology Review / July 29, 2019
• Jackie Snow / How artificial intelligence can tackle climate change / National Geographic / July 18, 2019
• Karen Hao / Training a single AI model can emit as much carbon as five cars in their lifetimes / MIT Technology Review / June 6, 2019

Also see Climate Change AI (CCAI), a group of volunteers from academia and industry who believe that tackling climate change requires concerted societal action, in which machine learning can play an impactful role.

• Henri Poulain et Julien Goetz / Invisibles - Les travailleurs du clic / France Télévisions / Feb. 12, 2020 (French)
• Ana Lucia Gonzalez / The 'microworkers' making your digital life possible / BBC News / Aug. 2, 2019
• Jean-Marc Vittori / Comment le numérique accroît les inégalités / Les Echos / 2 avril 2019 (French)
• Will Knight / One of the fathers of AI is worried about its future / MIT Technology Review / Nov. 17, 2018
• Karen Hao / Should a self-driving car kill the baby or the grandma? Depends on where you’re from. / MIT Technology Review / Oct. 24, 2018

• Will Knight / Facebook, Google, Twitter aren’t prepared for presidential deepfakes / MIT Technology Review / Aug. 6, 2019
• Alina Tugend / Exposing the Bias Embedded in Tech / The New York Times / June 17, 2019
• Seek and you shall find - Google rewards reputable reporting, not left-wing politics / The Economist / June 8, 2019
• Megan Specia / Siri and Alexa Reinforce Gender Bias, U.N. Finds / The New York Times / May 22, 2019

• Les stats, c’est moi - How to think about data in 2019 / The Economist / Dec. 22, 2018
• Baratunde Thurston / Find Out What Google and Facebook Know About You / Medium / June 4, 2018
• Smile, you’re on camera - There will be little privacy in the workplace of the future / The Economist / Mar. 31, 2018

• Jonny Brooks-Bartlett / Here’s why so many data scientists are leaving their jobs / Towards Data Science / Mar. 28, 2018
• Thomas H. Davenport and D.J. Patil / Data Scientist: The Sexiest Job of the 21st Century / Harvard Business Review / Oct. 2012