An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020)

This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence, namely psychology and engineering, and from disciplines aiming to regulate AI innovations, namely AI ethics and law. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems. Relevant concepts are integrated into a matrix intended to help defining more precisely when and how computing tools (programs or devices) may be qualified as AI while highlighting critical features to serve a specific technical, ethical and legal assessment of challenges in AI development. Some adaptations of existing notions of AI characteristics are proposed. The matrix is a risk-based conceptual model designed to allow an empirical, flexible and scalable qualification of AI technologies in the perspective of benefit-risk assessment practices, technological monitoring and regulatory compliance: it offers a structured reflection tool for stakeholders in AI development that are engaged in responsible research and innovation.Pre-print version (achieved on May 2020)

[1]  Melissa Davidson,et al.  The Taxonomy of Learning , 2008, International anesthesiology clinics.

[2]  Andrew Johnson,et al.  Gardner's Theory of Multiple Intelligences , 2010 .

[3]  Sekta Lonir Oscarini,et al.  BLOOM'S TAXONOMY: ORIGINAL AND REVISED , 2010 .

[4]  Pavel Kraikivski,et al.  Seeding the Singularity for A.I , 2019, ArXiv.

[5]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[6]  Blog Post , 2020, Blog post Digital Object Group.

[7]  R. Cattell,et al.  Refinement and test of the theory of fluid and crystallized general intelligences. , 1966, Journal of educational psychology.

[8]  Jean-Loup Farges,et al.  Planning in Partially Observable Domains with Fuzzy Epistemic States and Probabilistic Dynamics , 2015, SUM.

[9]  K. Novak,et al.  DNA repair: The guardian , 2003, Nature Reviews Cancer.

[10]  D. Linden,et al.  The negative Flynn Effect: A systematic literature review , 2016 .

[11]  Gary L. Canivez,et al.  Challenges to the Cattell-Horn-Carroll Theory: Empirical, Clinical, and Policy Implications , 2019, Applied Measurement in Education.

[12]  N. Cowan What are the differences between long-term, short-term, and working memory? , 2008, Progress in brain research.

[13]  Gary Roberts,et al.  Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer , 2008 .

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

[15]  D. Premack,et al.  Does the chimpanzee have a theory of mind? , 1978, Behavioral and Brain Sciences.

[16]  Libor Preucil,et al.  European Robotics Symposium 2008 , 2008 .

[17]  Ravi B. Parikh,et al.  Addressing Bias in Artificial Intelligence in Health Care. , 2019, JAMA.

[18]  Witold Pedrycz,et al.  Genetic learning of fuzzy cognitive maps , 2005, Fuzzy Sets Syst..

[19]  James H. Moor,et al.  The Turing Test: The Elusive Standard of Artificial Intelligence , 1989, Computational Linguistics.

[20]  Benjamin S. Bloom,et al.  A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives , 2000 .

[21]  C. Frith,et al.  Functional imaging of ‘theory of mind’ , 2003, Trends in Cognitive Sciences.

[22]  M. D’Esposito Working memory. , 2008, Handbook of clinical neurology.

[23]  H. Gardner,et al.  Frames of Mind: The Theory of Multiple Intelligences , 1983 .

[24]  Chrysostomos D. Stylios,et al.  Modeling complex systems using fuzzy cognitive maps , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[25]  Ian Davidson,et al.  Towards Fluid Machine Intelligence: Can We Make a Gifted AI? , 2019, AAAI.

[26]  Cerna Collectif Éthique de la recherche en robotique , 2014 .

[27]  Colin Clark,et al.  The Conditions of Economic Progress. , 1941 .

[28]  B. Baars,et al.  Criteria for consciousness in humans and other mammals , 2005, Consciousness and Cognition.

[29]  Eric J. Johnson,et al.  Chapter 8 – Complementary Contributions of Fluid and Crystallized Intelligence to Decision Making Across the Life Span , 2015 .

[30]  Michael C. Pyryt Human cognitive abilities: A survey of factor analytic studies , 1998 .

[31]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[32]  Jim Parker,et al.  Learning in Artificial Intelligence: Does Bloom's Taxonomy Apply? , 2016 .

[33]  Stéphanie Clémençon,et al.  Algorithmes : Biais, Discrimination et Équité , 2019 .

[34]  M. Cannarsa Ethics Guidelines for Trustworthy AI , 2021, The Cambridge Handbook of Lawyering in the Digital Age.

[35]  Understanding consciousness , 2005, EMBO reports.

[36]  M. Velmans HOW TO DEFINE CONSCIOUSNESS—AND HOW NOT TO DEFINE CONSCIOUSNESS , 2009 .