Artificial Tikkun Olam: AI Can Be Our Best Friend in Building an Open Human-Computer Society

Technological advances of virtually every kind pose risks to society including fairness and bias. We review a long-standing wisdom that a widespread practical deployment of any technology may produce adverse side effects misusing the knowhow. This includes AI but AI systems are not solely responsible for societal risks. We describe some of the common and AI specific risks in health industries and other sectors and propose both broad and specific solutions. Each technology requires very specialized and informed tracking, monitoring and creative solutions. We postulate that AI systems are uniquely poised to produce conceptual and methodological solutions to both fairness and bias in automated decision-making systems. We propose a simple intelligent system quotient that may correspond to their adverse societal impact and outline a multi-tier architecture for producing solutions of increasing complexity to these risks. We also propose that universities may consider forming interdisciplinary Study of Future Technology Centers to investigate and predict the fuller range of risks posed by technology and seek both common and AI specific solutions using computational, technical, conceptual and ethical analysis

[1]  Matt J. Kusner,et al.  Counterfactual Fairness , 2017, NIPS.

[2]  Simon Kasif,et al.  Protein Secondary-Structure Modeling with Probabilistic Networks , 1993, ISMB.

[3]  E. Rowland Theory of Games and Economic Behavior , 1946, Nature.

[4]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[5]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[6]  北村 聖 "The New England Journal of Medicine". , 1962, British medical journal.

[7]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[8]  Taha A. Kass-Hout,et al.  Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter , 2014, Drug Safety.

[9]  Deborah Lupton,et al.  'It's like having a physician in your pocket!' A critical analysis of self-diagnosis smartphone apps. , 2015, Social science & medicine.

[10]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[11]  Erez Shmueli,et al.  Algorithmic Fairness , 2020, ArXiv.

[12]  Rosalind W. Picard,et al.  Affective medicine: technology with emotional intelligence. , 2002, Studies in health technology and informatics.

[13]  L. Brakel A Universe of Consciousness: How Matter Becomes Imagination , 2001 .

[14]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[15]  Medicina Nei Secoli La Redazione No Abstract Available , 2005 .

[16]  Yuriy Brun,et al.  Preventing undesirable behavior of intelligent machines , 2019, Science.

[17]  David G. Rand,et al.  Static network structure can stabilize human cooperation , 2014, Proceedings of the National Academy of Sciences.

[18]  Regina Barzilay,et al.  Do Neural Information Extraction Algorithms Generalize Across Institutions? , 2019, JCO clinical cancer informatics.

[19]  John von Neumann,et al.  Theory of Games and Economic Behavior (60th-Anniversary Edition) , 2007 .

[20]  S. Merz Race after technology. Abolitionist tools for the new Jim Code , 2020, Ethnic and Racial Studies.

[21]  D. Meyer,et al.  Supporting Online Material Materials and Methods Som Text Figs. S1 to S6 References Evidence for a Collective Intelligence Factor in the Performance of Human Groups , 2022 .

[22]  M. Hernán,et al.  Win-win: Reconciling Social Epidemiology and Causal Inference. , 2019, American journal of epidemiology.

[23]  M. Nowak Five Rules for the Evolution of Cooperation , 2006, Science.

[24]  J. Brownstein,et al.  Digital disease detection--harnessing the Web for public health surveillance. , 2009, The New England journal of medicine.

[25]  Sebastian Ehrlichmann,et al.  The Economics of Discrimination , 2009 .

[26]  Xiaojin Zhu,et al.  Are Female Applicants Disadvantaged in National Institutes of Health Peer Review? Combining Algorithmic Text Mining and Qualitative Methods to Detect Evaluative Differences in R01 Reviewers' Critiques. , 2017, Journal of women's health.

[27]  Jeannette M. Wing An introduction to computer science for non-majors using principles of computation , 2007, SIGCSE.

[28]  Michael Y. Galperin,et al.  The COMBREX Project: Design, Methodology, and Initial Results , 2013, PLoS biology.

[29]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[30]  Simon Kasif,et al.  The art of gene function prediction , 2006, Nature Biotechnology.

[31]  Peter Szolovits,et al.  Genetic Misdiagnoses and the Potential for Health Disparities. , 2016, The New England journal of medicine.

[32]  J. Donnelly Universal Human Rights in Theory and Practice , 1992 .

[33]  Kevin K. Yang,et al.  Machine-learning-guided directed evolution for protein engineering , 2018, Nature Methods.

[34]  E. Shortliffe,et al.  Clinical Decision Support in the Era of Artificial Intelligence. , 2018, JAMA.

[35]  William L. Hamilton,et al.  Language from police body camera footage shows racial disparities in officer respect , 2017, Proceedings of the National Academy of Sciences.

[36]  N. McGlynn Thinking fast and slow. , 2014, Australian veterinary journal.

[37]  Polly S Nichols,et al.  Agreeing to disagree. , 2005, General dentistry.

[38]  Stuart J. Russell Artificial Intelligence and the Problem of Control , 2021, Perspectives on Digital Humanism.

[39]  Molly Carnes,et al.  Physicians and Implicit Bias: How Doctors May Unwittingly Perpetuate Health Care Disparities , 2013, Journal of General Internal Medicine.

[40]  Christos H. Papadimitriou,et al.  The Complexity of Fairness Through Equilibrium , 2013, ACM Trans. Economics and Comput..

[41]  Nicholas A Peppas,et al.  The future of open‐ and closed‐loop insulin delivery systems , 2008, The Journal of pharmacy and pharmacology.

[42]  Florence T. Bourgeois,et al.  Premarket Safety and Efficacy Studies for ADHD Medications in Children , 2014, PloS one.

[43]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[44]  R. Altman,et al.  The incidentalome: a threat to genomic medicine. , 2006, JAMA.

[45]  J. W. Davis,et al.  Article 7 , 2019, European Financial Services Law.

[46]  Joelle Pineau,et al.  Informing sequential clinical decision-making through reinforcement learning: an empirical study , 2010, Machine Learning.

[47]  Alex 'Sandy' Pentland,et al.  An interpretable approach for social network formation among heterogeneous agents , 2018, Nature Communications.

[48]  Jack Minker,et al.  Logic-Based Artificial Intelligence , 2000 .

[49]  Christopher H. Bryant,et al.  Functional genomic hypothesis generation and experimentation by a robot scientist , 2004, Nature.

[50]  L. Goldberg The Book of Why: The New Science of Cause and Effect† , 2019, Quantitative Finance.

[51]  A. Delcher,et al.  Protein secondary structure modelling with probabilistic networks. , 1993, Proceedings. International Conference on Intelligent Systems for Molecular Biology.

[52]  E. Vesterinen,et al.  Affective Computing , 2009, Encyclopedia of Biometrics.

[53]  Isaac S Kohane,et al.  Artificial Intelligence in Healthcare , 2019, Artificial Intelligence and Machine Learning for Business for Non-Engineers.

[54]  Emma J. Chory,et al.  A Deep Learning Approach to Antibiotic Discovery , 2020, Cell.

[55]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[56]  David M Nathan,et al.  Outpatient glycemic control with a bionic pancreas in type 1 diabetes. , 2014, The New England journal of medicine.

[57]  Sharad Goel,et al.  The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning , 2018, ArXiv.

[58]  Toniann Pitassi,et al.  Fairness through awareness , 2011, ITCS '12.

[59]  P. Spirtes,et al.  Review of Causal Discovery Methods Based on Graphical Models , 2019, Front. Genet..

[60]  D. Baker,et al.  Protein interaction networks revealed by proteome coevolution , 2019, Science.

[61]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[62]  J. Ioannidis Why Most Published Research Findings Are False , 2005 .

[63]  Terrence J. Sejnowski,et al.  The Computational Brain , 1996, Artif. Intell..

[64]  N. Christakis,et al.  Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study , 2008, BMJ : British Medical Journal.

[65]  N. Christakis,et al.  The Spread of Obesity in a Large Social Network Over 32 Years , 2007, The New England journal of medicine.

[66]  J. Steitz,et al.  Increasing gender diversity in the STEM research workforce , 2019, Science.

[67]  K. Borgwardt,et al.  Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.

[68]  K. Mandl,et al.  Analysis of Pediatric Clinical Drug Trials for Neuropsychiatric Conditions , 2013, Pediatrics.

[69]  D. Estrin,et al.  Open mHealth Architecture: An Engine for Health Care Innovation , 2010, Science.

[70]  Terrence J. Sejnowski,et al.  The Deep Learning Revolution , 2018 .

[71]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[72]  A. C. Scott,et al.  Evaluating the performance of a computer-based consultant. , 1979, Computer programs in biomedicine.

[73]  Hillol Kargupta,et al.  Graphical Models: Foundations of Neural Computation , 2016, Pattern Analysis and Applications.

[74]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[75]  Virgil D. Gligor On the foundations of trust in networks of humans and computers , 2012, CCS '12.

[76]  Ronald Fagin,et al.  Reasoning about knowledge , 1995 .

[77]  Jon Kleinberg,et al.  Analysis of large-scale social and information networks , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[78]  Deborah Lupton,et al.  Toward a manifesto for the ‘public understanding of big data’ , 2016, Public understanding of science.

[79]  Russ B Altman,et al.  Health-information altruists--a potentially critical resource. , 2005, The New England journal of medicine.

[80]  J. Pearl,et al.  Causal inference in statistics , 2016 .

[81]  Martin A. Nowak,et al.  Evolution of cooperation on large networks with community structure , 2019, Journal of the Royal Society Interface.

[82]  Bernhard Schölkopf,et al.  Avoiding Discrimination through Causal Reasoning , 2017, NIPS.

[83]  S. Kasif,et al.  Whole-genome annotation by using evidence integration in functional-linkage networks. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[84]  J. Pearl Causal inference in statistics: An overview , 2009 .

[85]  Mélanie Frappier,et al.  The Book of Why: The New Science of Cause and Effect , 2018, Science.

[86]  Geoffrey E. Hinton Deep Learning-A Technology With the Potential to Transform Health Care. , 2018, JAMA.

[87]  S. Galea,et al.  What matters, when, for whom? three questions to guide population health scholarship , 2017, Injury Prevention.

[88]  Heleen Riper,et al.  The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research. , 2016, American journal of preventive medicine.