From Phoebus to witches to death clocks: why we are taking predictive technologies to the extreme

AI & SOCIETY - Trang 1-13 - 2024
Simona Chiodo1
1Politecnico di Milano, DAStU, Milan, Italy

Tóm tắt

In the following article, I shall focus on emerging technologies that increasingly try to predict our death, i.e. when we will die and from what cause. More precisely, I shall focus on the possible answer to the following philosophical question: why are we taking predictive technologies to the extreme? First, I shall reflect upon the results of recent empirical research. Second, I shall address the issue of taking predictive technologies to the extreme, i.e. predicting one’s death, through philosophical tools, from thought experiments to a philosophical perspective on the possible key reason why we are using emerging technologies’ unprecedented power of prediction to improve more and more our knowledge of when we will die and from what cause. The philosophical answer I propose is the following: even death clocks, together with other kinds of emerging technologies that gain an unprecedented power of prediction, may somehow save us by reactivating our sensemaking whenever our life is uncertain and demanding to the point that we cannot face our open future by planning and acting by ourselves. If it is true that the price we pay, i.e. a kind of automation of our own future, is extremely high, it is also true that, in our unprecedentedly uncertain and demanding time, autonomous sensemaking seems to scare us even more.

Tài liệu tham khảo

Aeschylus (1926) Prometheus bound (PB). Harvard University Press, Cambridge (Translated by H.W. Smith) Agrawal A, Gans J, Goldfarb A (2018) Prediction machines The simple economics of artificial intelligence. Harvard Business Review Press, Boston Agarwal A, Alomar A, Shah D (2020) TspDB. Time series predict DB. Proc Mach Learn Res 1:1–31 Agrawal A, Gans J, Goldfarb A (2022) Power and prediction. The disruptive economics of artificial intelligence. Harvard Business Review Press, Boston Andersen D, Ravn S, Thomson R (2020) Narrative sense-making and prospective social action. Methodological challenges and new directions. Int J Soc Res Methodol 23(4):367–375 Belsky DW et al (2020) Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. eLife 9:1–25 Bradley B (2009) Well-being and death. Oxford University Press, Oxford Bradley B, Feldman F, Johansson J (eds) (2013) The Oxford handbook of philosophy of death. Oxford University Press, Oxford Buck RC (1963) Reflexive predictions. Philos Sci 30(4):359–369 Callimachus (1921). In: Mair AW (ed) Hymn to Athena. Heinemann-G.P. Putnam’s sons, New York Cawthon RM et al (2003) Association between telomere length in blood and mortality in people aged 60 years or older. The Lancet 9355(361):393–395 Chen BH et al (2016) DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 9(8):1844–1865 Chiodo S (2020) Technology and anarchy. A reading of our era. Lexington Books-The Rowman & Littlefield Publishing Group, Lanham-Boulder-New York-London Chiodo S (2023a) Technology and the overturning of human autonomy. Springer, Cham Chiodo S (2023b) Trading human autonomy for technological automation. In: Lindgren S (ed) Handbook of critical studies of artificial intelligence. Edward Elgar Publishers, Cheltenham, pp 67–78 Cholbi M (ed) (2015) Immortality and the philosophy of death. Rowman and Littlefield, New York Corbyn Z (2022) “Morgan Levine: ‘Only 10–30& of our lifespan is estimated to be due to genetics’”, The guardian, https://www.theguardian.com/science/2022/may/07/morgan-levine-only-10-30-of-our-lifespan-is-estimated-to-be-due-to-genetics. Accessed 8 May 2023 Di Paolo EA (2021) Enactive becoming. Phenomenol Cogn Sci 20:783–809 Dougherty C (2006) Prometheus. Taylor & Francis, London Durt C, Fuchs T, Tewes C (2017) Embodiment, enaction, and culture. Investigating the constitution of the shared world. The MIT Press, Cambridge Eiriksdottir T et al (2021) Predicting the probability of death using proteomics. Commun Biol 758(4):1–11 Feldman F (1992) Confrontations with the reaper. Oxford University Press, New York Fischer JM (ed) (1993) The metaphysics of death. Stanford University Press, Stanford Fischer JM (2020) Death, immortality, and meaning in life. Oxford University Press, Oxford Fischer K et al (2014) Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality. An observational study of 17.345 persons. PLoS Med 11(2):1–12 Foreman KJ et al (2018) Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death. Reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet 10159(392):2052–2090 Gaille M et al (2020) Ethical and social implications of approaching death prediction in humans. When the biology of ageing meets existential issues. BMC Med Ethics 64(21):1–13 Gao X et al (2019) Comparative validation of an epigenetic mortality risk score with three aging biomarkers for predicting mortality risks among older adult males. Int J Epidemiol 48(6):1958–1971 Gartziou-Tatti A (2010) Blindness as punishment. In: Christopoulos M, Karakantza E, Levaniouk O (eds) Light and darkness in ancient Greek myth and religion. Lexington Books, Lanham Gigerenzer G, Garcia-Retamero R (2017) Cassandra’s regret. The psychology of not wanting to know. Psychol Rev 124(2):179–196 Glaessgen E, Stargel D (2012) “The digital twin paradigm for future NASA and US Air Force vehicles”, in 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, American Institute of Aeronautics and Astronautics, 1–14 Grieves M (2014) Digital twin. Manufacturing excellence through virtual factory replication. NASA, Washington Hamzelou J (2022) Aging clocks aim to predict how long you’ll live. MIT Technol Rev 125(4):14–15 Han B-C (2015) The burnout society. Stanford University Press, Stanford (Translated by E. Butler) Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:2–19 Horvath S, Raj K (2018) DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet 19:371–384 Kagan S (2012) Death. Yale University Press, New Haven Kahn AD (1970) Every art possessed by man comes from Prometheus. The Greek tragedians and science and technology. Technol Cult 11(2):133–162 Kamm FM (2021) Almost over. Aging, dying, dead. Oxford University Press, New York Kant I (1988, 1781) In: Guyer P, Wood A (eds) Critique of pure reason. Cambridge University Press, Cambridge-New York Levine ME (2013) Modelling the rate of senescence. Can estimated biological age predict mortality more accurately than chronological age? J Gerontol 68(6):667–674 Levine ME et al (2018) An epigenetic biomarker of aging for lifespan and healthspan. Aging 10(4):573–591 Lin Q et al (2016) DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging 8(2):394–401 Luper S (2009) The philosophy of death. Cambridge University Press, Cambridge Luper S (ed) (2014) The Cambridge companion to life and death. Cambridge University Press, Cambridge Marin L (2022) Enactive principles for the ethics of user interactions on social media. How to overcome systematic misunderstandings through shared meaning-making. Topoi 41:425–437 Marioni RE et al (2015) DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol 25(1):1–12 Merton RK (1948) The self-fulfilling prophecy. Antioch Rev 8(2):193–210 Nietzsche F (2001) In: Williams B (ed) The gay science. Cambridge University Press, Cambridge Overall C (2003) Aging, death, and human longevity. A philosophical inquiry. University of California Press, Berkeley Peters MJ et al (2015) The transcriptional landscape of age in human peripheral blood. Nat Commun 6:1–14 Pirracchio R et al (2015) Mortality prediction in intensive care units with the super ICU learner algorithm (SICULA). A population-based study. Lancet Respir Med 3(1):42–52 Plato (1966) In: Fowler HN (ed) Apology. Harvard University Press-Heinemann, Cambridge-London Popper K (1957) The poverty of historicism. Routledge, London-New York Robbins R (2017) “This company wants to analyse your saliva—try to predict when you’ll die”, Stat, https://www.statnews.com/2017/03/13/insurance-dna-death-prediction/. Accessed 8 May 2023 Robins Wahlin TB (2007) To know or not to know. A review of behaviour and suicidal ideation in preclinical Huntington’s disease. Patient Educ Couns 65(3):279–287 Romanos GD (1973) Reflexive predictions. Philos Sci 40(1):97–109 Rousseau JJ (1997, 1761) Julie, or, the new Heloise. Letters of two lovers who live in a small town at the foot of the Alps, translated by P. Stewart and J. Vaché, Hanover: University Press of New England Sanders JL, Newman AB (2013) Telomere length in epidemiology. A biomarker of aging, age-related disease, both, or neither? Epidemiol Rev 35(1):112–131 Sanders J et al (2012) Understanding the aging process using epidemiologic approaches. In: Newman A, Cauley J (eds) The epidemiology of aging. Springer, Dordrecht, pp 187–214 Schumacher B (2010) Death and mortality in contemporary philosophy. Cambridge University Press, Cambridge Shakespeare W (2013, 1623) The tragedy of Macbeth (Mac.), edited by B.A. Mowat and P. Werstine, New York: Folger Shakespeare Library Shelley M (1994, 1818 and 1831) Frankenstein, or, the modern Prometheus. Penguin Books, London Siegel E (2012) Predictive analytics. The power to predict who will click, buy, lie, or die. Wiley, Hoboken Solomon RC (1993) The passion. Emotions and the meaning of life. Hackettm, Indianapolis Sophocles (1887) In: Jebb R (ed) Oedipus tyrannus (OT). Cambridge University Press, Cambridge Taylor JS (ed) (2013) The metaphysics and ethics of death. New essays. Oxford University Press, Oxford The holy bible (1983) New international version. New York International Bible Society, East Brunswick Tulchinsky I (2022) The age of prediction. How data and technology are driving exponential change, World Economic Forum, Davos, https://www.weforum.org/agenda/2022/05/age-of-prediction/. Accessed 8 May 2023 Tulchinsky I, Mason CE (2023) The age of prediction. Algorithms, AI, and the shifting shadows of risk. The MIT Press, Cambridge Vörös S (2017) Wrestling with the absurd. Enaction meets non-sense. J Mind Behav 38(2):155–165 Weick K, Sutcliffe KM, Obstfeld D (2005) Organizing and the process of sensemaking. Organ Sci 16(4):409–421 Williams B (1973) Problems of the self. Philosophical papers 1956–1972. Cambridge University Press, Cambridge Williams B (1993) Shame and necessity. University of California Press, Berkeley Wittgenstein L (1963, 1921) In: Pears D, McGuinness B (eds) Tractatus logico-philosophicus. Routledge and Kegan Paul, London Zewe A (2022) “A tool for predicting the future. Researchers design a user-friendly interface that helps nonexperts make forecasts using data collected over time, MIT news. https://news.mit.edu/2022/tensor-predicting-future-0328. Accessed 8 May 2023 Zhang Y et al (2017) DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat Commun 8:1–11 Zhavoronkov A et al (2019) Artificial intelligence for aging and longevity research. Recent advances and perspectives. Ageing Res Rev 49:49–66