Robustly Beneficial Podcast

#TheSocialDilemma is a recent Netflix documentary on the concerning side effects of social medias and recommandation algorithms on mental health, political manipulation and misinformation, among other issues. We discuss the documentary, and our disagreements with the documentary's take. The documentary: https://www.netflix.com/watch/81254224 A 2020 philosophy paper on "Recommender systems and their ethical challenges", published at "AI and Society" by Silvia Milano, Mariarosaria Taddeo & Luciano Floridi. https://link.springer.com/article/10.1007/s00146-020-00950-y

In this episode, we discuss The Complexity of Agreement (https://arxiv.org/abs/cs/0406061), published by Scott Aaronson in the Symposium on the Theory of Computing, we also go beyond the paper to discuss the various forms several communities from game theory (social choice) and distributed computing (the study of Consens) tried to mathematically formalise the intractable question of agreement and communication.

Lê, Mahdi and Louis discuss a class proposal by Lê and Mahdi on computable philosophy. The video provides a brief overview of some of the contents of the class proposal, including the relation between laws and algorithms, the need for learning, probabilistic thinking, privacy and fairness.

Lê, Mahdi and Louis discuss information and disinformation related to vaccines on social media and what can be done to improve the current situation. Specifically focusing on the analysis and results from the paper "The online competition between pro- and anti-vaccination views" by Johnson & al. (https://www.nature.com/articles/s41586-020-2281-1.pdf)

In this episode, we discuss the entry on ethics of artificial intelligence and robotics in the Stanford encyclopaedia of philosophy: https://plato.stanford.edu/entries/ethics-ai/

Exposure to opposing views on social media can increase political polarization. Christopher A. Baila, Lisa P. Argyleb , Taylor W. Browna , John P. Bumpusa , Haohan Chenc , M. B. Fallin Hunzakerd , Jaemin Leea , Marcus Manna , Friedolin Merhouta , and Alexander Volfovsky, PNAS 18. https://www.pnas.org/content/115/37/9216

In this episode we discuss the philosophical aspect of computing and share what we learned from Scott Aaronson's essay: Why Philosopher Should Care About Computational Complexity (https://www.scottaaronson.com/papers/philos.pdf)

In this episode, we discuss a recent collaborative report on trustworthy artificial intelligence development. To read the report: https://www.towardtrustworthyai.com/

We discuss ideas presented on this blog post by Jürgen Schmidhuber, and beyond. http://people.idsia.ch/~juergen/ai-covid.html Timecodes : 1:55 Population-scale analysis 9:11 Individual risk assessment 22:11 Drug discovery 30:22 Recommender systems 43:44 Computational thinking

The cartoon by Nicky Case explaining digital contact tracing: https://ncase.me/contact-tracing/ The white paper explaining the DP-3T protocol app: https://github.com/DP-3T/documents/blob/master/DP3T%20White%20Paper.pdf

In this episode, we discuss the security and privacy challenges in machine learning. A Marauder's Map of Security and Privacy in Machine Learning | Nicolas Papernot https://arxiv.org/abs/1811.01134

We discuss the exploration-exploitation dilemma and near-optimal solutions found by mathematicians. Some relevant ressources include: Bayesian Adaptive Methods for Clinical Trials. CRC Press. Berry, Carlin, Lee & Muller (2010). https://www.crcpress.com/Bayesian-Adaptive-Methods-for-Clinical-Trials/Berry-Carlin-Lee-Muller/p/book/9781439825488 Bayesian adaptive clinical trials: a dream for statisticians only? Statistics in Medicine. Chrevret (2011). https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4363 Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges. Statistical Science. Villar, Bowden & Wason (2015). "Across this literature, the use of bandit models to optimally design clinical trials became a typical motivating application, yet little of the resulting theory has ever been used in the actual design and analysis of clinical trials." https://arxiv.org/pdf/1507.08025.pdf Machine learning applications in drug development. Computational and Structural Biotechnology Journal. Réda, Kaufmann & Delahaye-Duriez (2019). https://www.sciencedirect.com/science/article/pii/S2001037019303988 Rethinking the Gold Standard With Multi-armed Bandits: Machine Learning Allocation Algorithms for Experiments. Kaibel & Bieman (2019) https://journals.sagepub.com/doi/abs/10.1177/1094428119854153 Cancer specialists in disagreement about purpose of clinical trials. Journal of the National Cancer Institute (2012). https://www.eurekalert.org/pub_releases/2002-12/jotn-csi121202.php WHO launches global megatrial of the four most promising coronavirus treatments. Science Mag. Kupferschmidt & Cohen (2020). https://www.sciencemag.org/news/2020/03/who-launches-global-megatrial-four-most-promising-coronavirus-treatments

AI Safety via Debate: https://arxiv.org/pdf/1805.00899.pdf

In this episode, Lê Louis and El Mahdi discuss social media manipulation and the difficult question of misinformation spread on social media. We also comment a bit on the current coronavirus pandemic context. SmarterEveryday playlist on Social Media Manipulation: https://www.youtube.com/watch?v=MUiYglgGbos&list=PLjHf9jaFs8XVAQpJLdNNyA8tzhXzhpZHu

WeBuildAI: Participatory Framework for Algorithmic Governance. LKKKY+19 https://www.cs.cmu.edu/~akahng/papers/webuildai.pdf Find out more on the RB Wiki: https://robustlybeneficial.org/wiki/index.php?title=Social_choice https://robustlybeneficial.org/wiki/index.php?title=Interpretability

A Roadmap for Robust End-to-End Alignment. Lê Nguyên Hoang 18. https://arxiv.org/pdf/1809.01036 Find out more on the Robustly Beneficial Wiki: https://robustlybeneficial.org/wiki/index.php?title=ABCDE_roadmap Next week's paper is WeBuildAI: Participatory Framework for Algorithmic Governance. PACMHCI. LKKKY+19. https://www.cs.cmu.edu/~akahng/papers/webuildai.pdf

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. SAHSS+19. https://arxiv.org/abs/1911.08265 Find out more on the Robustly Beneficial Wiki: https://robustlybeneficial.org/wiki/index.php?title=Reinforcement_learning Next week's paper is: A Roadmap for Robust End-to-End Alignment. LN Hoang 18. https://arxiv.org/abs/1809.01036

Intelligent Autonomous Things on the Battlefield. AI for the Internet of Everything. A Kott and E Stump 19. https://arxiv.org/ftp/arxiv/papers/1902/1902.10086.pdf Slaughterbots. Future of life Institute 17. https://www.youtube.com/watch?v=HipTO_7mUOw The Future of War, and How It Affects YOU (Multi-Domain Operations). Smarter Every Day 211. https://www.youtube.com/watch?v=qOTYgcdNrXE Find out more on the Robustly Beneficial Wiki: https://robustlybeneficial.org/wiki/index.php?title=Robustly_beneficial https://robustlybeneficial.org/wiki/index.php?title=Robust_statistics Next week's paper is about MuZero. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. SAHSS+20. https://arxiv.org/abs/1911.08265

Preference learning from comparisons. Lucas Maystre 2018. EPFL PhD Thesis. https://infoscience.epfl.ch/record/255399/files/EPFL_TH8637.pdf Find out more on our Wiki: https://robustlybeneficial.org/wiki/index.php?title=Volition https://robustlybeneficial.org/wiki/index.php?title=Preference_learning_from_comparisons

Focusing on the Long-Term: It's Good for Users and Business. H Hohnhold, D O' Brien and D Tang. KDD 15. https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43887.pdf Find out more on the Robustly Beneficial Wiki: https://robustlybeneficial.org/wiki/index.php?title=Mental_health https://robustlybeneficial.org/wiki/index.php?title=YouTube Next week, we will discuss: Preference Learning from Comparisons. Lucas Maystre. PhD Thesis 18. https://infoscience.epfl.ch/record/255399/files/EPFL_TH8637.pdf

Experimental evidence of massive-scale emotional contagion through social networks. A Kramer, J Guillory and J Hancock. PNAS 14. https://www.pnas.org/content/pnas/111/24/8788.full.pdf Find out more on our Wiki! https://robustlybeneficial.org/wiki/index.php?title=Mental_health https://robustlybeneficial.org/wiki/index.php?title=YouTube https://robustlybeneficial.org/wiki/index.php?title=Online_polarization https://robustlybeneficial.org/wiki/index.php?title=Safe_exploration https://robustlybeneficial.org/wiki/index.php?title=Consequentialism Next week, we discuss the following paper. Focusing on the Long-term: It's Good for Users and Business. H Hohnhold, D O'brien and D Tang. KDD 15. https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43887.pdf

Recent Advances in Algorithmic High-Dimensional Robust Statistics by Ilias Diakonikolas and Daniel M. Kane (2019) https://arxiv.org/pdf/1911.05911.pdf Robust subgaussian estimation of a mean vector in nearly linear time by Jules Depersin and Guillaume Lecué (2019) https://arxiv.org/pdf/1906.03058 Find out more on our Wiki: https://robustlybeneficial.org/wiki/index.php?title=Robust_statistics https://robustlybeneficial.org/wiki/index.php?title=Robustly_beneficial

Algorithmic accountability reporting: On the investigation of black boxes by Nicholas Diakopoulos (2014). https://academiccommons.columbia.edu/doi/10.7916/D8TT536K/download Find out more (not all pages have been written yet): https://robustlybeneficial.org/wiki/index.php?title=Interpretability https://robustlybeneficial.org/wiki/index.php?title=YouTube https://robustlybeneficial.org/wiki/index.php?title=Bayesianism https://robustlybeneficial.org/wiki/index.php?title=Solomonoff-Kolmogorov_complexity https://robustlybeneficial.org/wiki/index.php?title=Adversarial_attacks https://robustlybeneficial.org/wiki/index.php?title=Robust_statistics Next week's paper is Recent Advances in Algorithmic High-Dimensional Robust Statistics by Ilias Diakonikolas and Daniel M. Kane (2019). https://arxiv.org/pdf/1911.05911