Prometheus unbound or Paradise regained: the concept of Causality in the contemporary AI-Data Science debate
Journal de la société française de statistique, Volume 161 (2020) no. 1, pp. 4-41.

This essay highlights some aspects, core themes and controversies regarding causality from a historical-philosophical perspective with special attention to their role in the AI-data science debate. Firstly, it outlines the contours of this debate and subsequently addresses the aporia of causality in statistics, AI and the philosophy and science. In view of the prevalent crisis some key themes and controversies are identified, and a frame of reference is proposed, that may clarify historical controversies and the current state of “agreeing to disagree” in science and philosophy. Secondly, the essay highlights the historical scope of the concept, outlines some early perspectives and “key moments”, that involved main conceptual shifts. Thirdly, the essay outlines the rise of statistics and its role in attempting to defuse the crises by entering a sort of progressing liaison with causality. Finally, it is shown how research in AI has further shaped the concept and how and why causality is about to play a crucial role in the current quest for responsible, explainable and transparent AI and data science.

Classification: 62A01, 68T01, 97R40
Keywords: Artificial Intelligence (AI), Causality, Data Science, Philosophy, Statistics
Starmans, Richard 1, 2

1 Utrecht University
2 Tilburg University
@article{JSFS_2020__161_1_4_0,
     author = {Starmans, Richard},
     title = {Prometheus unbound or {Paradise} regained: the concept of {Causality} in the contemporary {AI-Data} {Science} debate},
     journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique},
     pages = {4--41},
     publisher = {Soci\'et\'e fran\c{c}aise de statistique},
     volume = {161},
     number = {1},
     year = {2020},
     mrnumber = {4125247},
     zbl = {1444.62029},
     language = {en},
     url = {http://archive.numdam.org/item/JSFS_2020__161_1_4_0/}
}
TY  - JOUR
AU  - Starmans, Richard
TI  - Prometheus unbound or Paradise regained: the concept of Causality in the contemporary AI-Data Science debate
JO  - Journal de la société française de statistique
PY  - 2020
SP  - 4
EP  - 41
VL  - 161
IS  - 1
PB  - Société française de statistique
UR  - http://archive.numdam.org/item/JSFS_2020__161_1_4_0/
LA  - en
ID  - JSFS_2020__161_1_4_0
ER  - 
%0 Journal Article
%A Starmans, Richard
%T Prometheus unbound or Paradise regained: the concept of Causality in the contemporary AI-Data Science debate
%J Journal de la société française de statistique
%D 2020
%P 4-41
%V 161
%N 1
%I Société française de statistique
%U http://archive.numdam.org/item/JSFS_2020__161_1_4_0/
%G en
%F JSFS_2020__161_1_4_0
Starmans, Richard. Prometheus unbound or Paradise regained: the concept of Causality in the contemporary AI-Data Science debate. Journal de la société française de statistique, Volume 161 (2020) no. 1, pp. 4-41. http://archive.numdam.org/item/JSFS_2020__161_1_4_0/

[1] Abrams, M. H. The Mirror and the Lamp. Romantic Theory and the Critical Tradition, Oxford University Press, 1953

[2] Anjum, R. L.; Mumford, S. Causation in Science and the Methods of Scientific Discovery, Oxford University Press, 2018 | DOI

[3] Anderson, C. The End of Theory: the Data Deluge Makes the Scientific Method Obsolete, Wired Magazine, Volume 6 (2008) https://www.wired.com/2008/06/pb-theory/

[4] A., Hernán. M.; Robins, J. M. Causal Inference, Chapman & Hall/CRC, Boca Raton, 2019

[5] Berlin, I.; Hardy, H. The roots of Romanticism, Princeton University Press, 1999

[6] Brynjolfsson, E.; McAfee, A. The Second Machine Age: work, progress and prosperity in a time of brilliant technologies, 2015, Norton

[7] Bostrom, N. A History of Transhumanist Thought, Journal of Evolution and Technology, Volume 14 (2005) no. 1

[8] Bostrom, N Superintelligence: paths, dangers and strategies, Oxford University Press, USA, 2014

[9] Brockman, J. What to think about machines that think. Todays leading thinkers on the age of Machine Intelligence, Harper & Collins, New York, 2015

[10] Brooks, R. Intelligence without representation, Artificial Intelligence (1991), pp. 1-3

[11] Burgess, S.; Thompson, S. G. Mendelian Randomization, Methods for Using Genetic Variants in Causal Estimation, Taylor & Amp., 2015 | DOI

[12] Burks, B. On the inadequacy of the partial and multiple correlation technique, Journal of Educational Psychology (1928), pp. 532-540

[13] Burwick, F. The Language of Causality in Prometheus Unbound, The Keats and Shelley Journal (1982)

[14] Chambaz, A.; Drouet, I.; Memetea, S. Simpson’s paradox, a tale of causality, Journal de la Société Française de Statistique (2020) (Special issue Causalité) | MR | Zbl

[15] Chambaz, A.; Drouet, I.; Thalabard, J.-C. Causality, a trialogue, Journal of Causal Inference, Volume 2 (2014) no. 2, pp. 201-241 | DOI | MR

[16] Cheeseman, P. In Defense of Probability, Proceedings of the Ninth International Joint Conference on AI (IJCAI, 1983) (1985)

[17] Clark, A. Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence, Oxford University Press, 2003

[18] Domingus, P. The Master Algorithm How the Quest for the Ultimate Learning Machine Will Remake Our World, Basic Books, 2016

[19] Franklin, S. History, motivations, and core themes, The Cambridge handbook of Artificial Intelligence, Cambridge University Press (2014)

[20] Glymour, C. The Mind’s Arrows: Bayes Nets and Graphical Causal Models in Psychology, MIT Press, 2001 | DOI

[21] Glymour, C Review of: Rani Lill Anjum and Stephen Mumford, Causation in Science and the Methods of Scientific Discovery, Oxford University Press, 2018, Notre Dame Philosophical Reviews, an Electronic Journal (2019) https://ndpr.nd.edu/news/causation-in-science-and-the-methods-of-scientific-discovery

[22] Goethe, J. W. von Faust. Eine Tragödie, Reclam Verlag, Berlin, 1808 (Kapitel 4)

[23] Goethe, J. W. von Theory of Colors (1810), MIT Press, 1982

[24] Horkheimer, Max; Adorno, Theodor W Dialectic of Enlightenment, New York: Herder and Herder, 1972 (Translated by John Cumming)

[25] Habermas, J. Theory des Kommunikativen Handelns, Frankfurt Am Main: Suhrkamp Verlag, 1981

[26] Hacking, I. The emergence of probability, Oxford University Press, 1975 | MR

[27] Hacking, I. The Taming of Chance, Oxford University Press, 1989

[28] Haraway, D. A Cyborg Manifesto: Science, Technology, and Socialist-Feminism in the Late Twentieth Century, Social Review, Volume 80 (1985), pp. 65-108

[29] Haugeland, J. Artificial Intelligence: The Very Idea, MIT Press, 1985

[30] Hayes, P. The naïve physics manifesto, University of Essex, 1977

[31] Hayes, P. In Defense of Logic, Proceedings of the fifth international joint conference on Artificial Intelligence (IJCAI), Volume 1, Cambridge University Press (1978)

[32] Heidegger, M. Die Frage nach der Technik, Grin Verlag, 1954

[33] Halevy, A.; Norvig, P.; Pereira, F. The Unreasonable Effectiveness of Data, IEEE Intelligent Systems, Volume 24 (2009) no. 3, pp. 8-12 | DOI

[34] Illari, P.; Russo, F. Causality: Philosophical Theory meets Scientific Practice, Oxford University Press, 2016

[35] J., Copeland. Artificial Intelligence: A Philosophical Introduction, Blackwell, 1993

[36] Jacobs, J. D. Causal Powers, Oxford University Press, 2017 | DOI

[37] The Probabilistic Revolution, Two Volumes (Krüger, L.; Daston, L.; Heidelberger, M.; Gigerenzer, G.; Morgan, M. S., eds.), MIT Press, 1987 | MR

[38] The Probabilistic Revolution, Volume I: Ideas in History (Krüger, L.; Daston, L.; Heidelberger, M., eds.), MIT Press, 1981 | MR

[39] Kern, S. A Cultural History of Causality: Science, Murder Novels and Systems of Thought, Princeton University Press, 2014

[40] Lovejoy, A. O. The Great Chain of Being: the study of the history of an idea, Harvard University Press, 1938

[41] Mumford, S.; Anjum, R. L. Getting causes from powers, Oxford University Press, 2011 | DOI

[42] Mumford, S.; Anjum, R. L. Causality, a very short introduction, Oxford University Press, 2013 | DOI

[43] Mackie, J. L. The Cement of the Universe: a study of causation, Oxford University Press, 1980 | DOI

[44] McCulloch, W.; Pitts, W. A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, Volume 5 (1943) no. 115–133 | MR | Zbl

[45] Mayer-Schönberger, V.; Cukier, K. Big Data: A Revolution that will Transform how we Live, Work and Think, Houghton Mifflin Harcourt, 2013

[46] Macagno, F.; Walton, D.; Reed, C. Argumentation Schemes. History, Classifications, and Computational Applications, Journal of Logics and their Applications, Volume 4 (2017) no. 8, pp. 2493-2556

[47] O’ Neill, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Crown Publishing Group, 2016 | MR

[48] Pearl, J. Causality: Models, Reasoning and Inference, Cambridge University Press, 2000 | MR

[49] Pearson, K. The Grammar of Science (1892), Dover Publications, 2004

[50] Pearl, Judea The seven tools of causal inference, with reflections on machine learning, Communications of the Association for Computing Machinery, Volume 62 (2019) no. 3, pp. 54-60 | DOI

[51] Plessner, H. Die Stufen des Organischen und der Mensch: Einleitung in die philosophische Anthropologie, De Gruyter, Berlin, 1928 | DOI

[52] Pearl, J.; MacKenzie, D. The book of Why: the new science of cause and effect, Basic Books, New York, 2018 | MR

[53] Robinson, W. S. Ecological Correlations and the Behavior of Individuals, American Sociological Review, Volume 15 (1950) no. 3, pp. 351-357 | DOI

[54] Sejnowski, T. J. The Deep Learning Revolution, MIT Press, 2018 | DOI

[55] Shannon, C. E. Programming a Computer for Playing Chess, Philosophical Magazine, Volume 7 (1950) no. 41, p. 314 | MR | Zbl

[56] Snelders, HAM Negentiende-eeuwse theorieën over de materie, GEWINA/TGGNWT, Volume 4 (2012) no. 4, pp. 168-187

[57] Spencer, H. The Principles of Ethics, Liberty Fund, Indianapolis, 1879

[58] Starmans, R. J. C. M. Models, Inference and Truth: Probabilistic Reasoning in the Information Era, Targeted Learning: Causal Inference for Observational and Experimental Data, Springer Verlag (2011)

[59] Starmans, R. J. C. M. Wikipedia, Ariane’s Thread or the Devolution of Diderot’s Ideal (in Dutch), Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, Volume 21 (2011) no. 3

[60] Starmans, R. J. C. M. Contemporary Dystopias: from Icarus’ fall to the wrath of the machines (in Dutch), Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, Volume 25 (2015) no. 1

[61] Starmans, R. J. C. M. “And further away their evening-red storms and falls”: on Newton, Goethe and the Light (in Dutch), Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, Volume 28 (2018) no. 1

[62] Starmans, R. J. C. M. A contemporary Euthyphro dilemma: on Deep Learning and the columns of oracular language (in Dutch), Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, Volume 28 (2018) no. 3

[63] Starmans, R. J. C. M. Along the caves of morality: about ethics, statistics and data science (in Dutch), STAtOR, Volume 18 (2018) no. 1

[64] Starmans, R. J. C. M. Expedition Robinson: from ecological correlation toward multi level analysis, STAtOR, Volume 19 (2018) no. 2

[65] Starmans, R. J. C. M. Statistics and Causality: progress of a laborious dialogue (in Dutch), STAtOR, Volume 19 (2018) no. 4

[66] Starmans, R. J. C. M. The Predicament of Truth: on Statistics, Causality, Physics and the Philosophy of Science, Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies, Springer Verlag (2018) (Springer Series in Statistics)

[67] Starmans, R. J. C. M. Beyond apology: theory of probability and fallible thinking (in Dutch), STAtOR, Volume 20 (2019) no. 1

[68] Starmans, R. J. C. M. Cause and effect: considerations in contemporary thinking about Causality (in Dutch), Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, Volume 29 (2019) no. 3

[69] Stiegler, B. Per toeval filosoferen: een verzameling uitgeschreven radiointerwiews met Stiegler, Klement/Pelckmans, 2014 (Translated by Pieter Lemmens)

[70] Stiegler, B. La technique et le temps 1. La faute dՃpim̩th̩̩, Galil̩e, Paris, 1994

[71] Tacq, J. Causality in qualitative and quantitative research, Quality and Quantity, Volume 45 (2011) no. 2, pp. 263-291 | DOI

[72] Tversky, A.; Kahneman, D. Judgement under Uncertainty: Heuristics and Biases, Cambridge University Press, 1974

[73] Turing, A. M. Computing machinery and intelligence, Mind, Volume 59 (1950), pp. 433-460 | DOI | MR

[74] Targeted Learning: Causal Inference for Observational and Experimental Data (van der Laan, M. J.; Rose, S., eds.), Springer Series in Statistics, Springer Verlag, 2011 | DOI | MR

[75] Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (van der Laan, M. J.; Rose, S., eds.), Springer Series in Statistics, Springer Verlag, 2018 | MR

[76] Virgil Georgics, Oxford World’s Classics, 2009

[77] Waldmann, M. The Oxford Handbook on Causal Reasoning, Oxford University Press, 2017

[78] Wieringa, R. Design Science, Springer Verlag, 2015

[79] Williamson, J. Probabilistic Theories of Causality, The Oxford Handbook of Causation, Oxford University Press (2009), pp. 185-212