Does AI Qualify for the Job?: A Bidirectional Model Mapping Labour and AI Intensities

In this paper we present a setting for examining the relation be-tween the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labourand AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples.

[1]  David Autor The “task approach” to labor markets: an overview , 2013, SSRN Electronic Journal.

[2]  Mark A. Przybocki,et al.  The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation , 2004, LREC.

[3]  José Hernández-Orallo,et al.  AI Extenders: The Ethical and Societal Implications of Humans Cognitively Extended by AI , 2019, AIES.

[4]  A. Manning,et al.  Job Polarization in Europe , 2009 .

[5]  José Hernández-Orallo,et al.  Accounting for the Neglected Dimensions of AI Progress , 2018, ArXiv.

[6]  Tom M. Mitchell,et al.  What Can Machines Learn, and What Does It Mean for Occupations and the Economy? , 2018 .

[7]  Michael P. Wellman,et al.  The 2001 trading agent competition , 2002, Electron. Mark..

[8]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[9]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[10]  Bruce G. Buchanan,et al.  A Virtual Archive for the History of AI , 2013, AI Mag..

[11]  José Hernández-Orallo,et al.  A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work , 2018, IJCAI 2018.

[12]  José Hernández-Orallo,et al.  Analysing Results from AI Benchmarks: Key Indicators and How to Obtain Them , 2018, ArXiv.

[13]  José Hernández-Orallo,et al.  The Measure of All Minds: Evaluating Natural and Artificial Intelligence , 2017 .

[14]  David Autor,et al.  Skills, Tasks and Technologies: Implications for Employment and Earnings , 2010 .

[15]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[16]  D. W. Fiske Consistency of the factorial structures of personality ratings from different sour sources. , 1949, Journal of abnormal psychology.

[17]  José Hernández-Orallo,et al.  Dual Indicators to Analyze AI Benchmarks: Difficulty, Discrimination, Ability, and Generality , 2020, IEEE Transactions on Games.

[18]  Gordon H. Hanson,et al.  Return of the Solow Paradox? It, Productivity, and Employment in U.S. Manufacturing , 2014, SSRN Electronic Journal.

[19]  M. Arntz,et al.  The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis , 2016 .

[20]  Michael A. Osborne,et al.  The future of employment: How susceptible are jobs to computerisation? , 2017 .

[21]  N. Dalkey,et al.  An Experimental Application of the Delphi Method to the Use of Experts , 1963 .

[22]  José Hernández-Orallo,et al.  Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement , 2017, Artificial Intelligence Review.

[23]  Edward W. Felten,et al.  A Method to Link Advances in Artificial Intelligence to Occupational Abilities , 2018 .