AI Extenders: The Ethical and Societal Implications of Humans Cognitively Extended by AI

Humans and AI systems are usually portrayed as separate sys- tems that we need to align in values and goals. However, there is a great deal of AI technology found in non-autonomous systems that are used as cognitive tools by humans. Under the extended mind thesis, the functional contributions of these tools become as essential to our cognition as our brains. But AI can take cognitive extension towards totally new capabil- ities, posing new philosophical, ethical and technical chal- lenges. To analyse these challenges better, we define and place AI extenders in a continuum between fully-externalized systems, loosely coupled with humans, and fully-internalized processes, with operations ultimately performed by the brain, making the tool redundant. We dissect the landscape of cog- nitive capabilities that can foreseeably be extended by AI and examine their ethical implications. We suggest that cognitive extenders using AI be treated as distinct from other cognitive enhancers by all relevant stakeholders, including developers, policy makers, and human users

Threshold Choice Methods: the Missing Link

Many performance metrics have been introduced for the evaluation of classification performance, with different origins and niches of application: accuracy, macro-accuracy, area under the ROC curve, the ROC convex hull, the absolute error, and the Brier score (with its decomposition into refinement and calibration). One way of understanding the relation among some of these metrics is the use of variable operating conditions (either in the form of misclassification costs or class proportions). Thus, a metric may correspond to some expected loss over a range of operating conditions. One dimension for the analysis has been precisely the distribution we take for this range of operating conditions, leading to some important connections in the area of proper scoring rules. However, we show that there is another dimension which has not received attention in the analysis of performance metrics. This new dimension is given by the decision rule, which is typically implemented as a threshold choice method when using scoring models. In this paper, we explore many old and new threshold choice methods: fixed, score-uniform, score-driven, rate-driven and optimal, among others. By calculating the loss of these methods for a uniform range of operating conditions we get the 0-1 loss, the absolute error, the Brier score (mean squared error), the AUC and the refinement loss respectively. This provides a comprehensive view of performance metrics as well as a systematic approach to loss minimisation, namely: take a model, apply several threshold choice methods consistent with the information which is (and will be) available about the operating condition, and compare their expected losses. In order to assist in this procedure we also derive several connections between the aforementioned performance metrics, and we highlight the role of calibration in choosing the threshold choice method

Stochastic tasks: difficulty and Levin search

We establish a setting for asynchronous stochastic tasks that account for episodes, rewards and responses, and, most especially, the computational complexity of the algorithm behind an agent solving a task. This is used to determine the difficulty of a task as the (logarithm of the) number of computational steps required to acquire an acceptable policy for the task, which includes the exploration of policies and their verification. We also analyse instance difficulty, task compositions and decompositions.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2010-21062-C02-02, PCIN-2013-037 and TIN 2013-45732-C4-1-P, and by Generalitat Valenciana PROMETEOII 2015/013.Hernández Orallo, J. (2015). Stochastic tasks: difficulty and Levin search. En Artificial General Intelligence. Springer International Publishing. 90-100. http://hdl.handle.net/10251/66686S9010

C-tests revisited: back and forth with complexity

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-21365-1_28We explore the aggregation of tasks by weighting them using a difficulty function that depends on the complexity of the (acceptable) policy for the task (instead of a universal distribution over tasks or an adaptive test). The resulting aggregations and decompositions are (now retrospectively) seen as the natural (and trivial) interactive generalisation of the C-tests.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2010-21062-C02-02, PCIN-2013-037 and TIN 2013-45732-C4-1-P, and by Generalitat Valenciana PROMETEOII 2015/013.Hernández Orallo, J. (2015). C-tests revisited: back and forth with complexity. En Artificial General Intelligence 8th International Conference, AGI 2015, AGI 2015, Berlin, Germany, July 22-25, 2015, Proceedings. Springer International Publishing. 272-282. https://doi.org/10.1007/978-3-319-21365-1_28S272282Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research 47, 253–279 (2013)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: Computational measures of information gain and reinforcement in inference processes. AI Communications 13(1), 49–50 (2000)Hernández-Orallo, J.: On the computational measurement of intelligence factors. In: Meystel, A. (ed.) Performance metrics for intelligent systems workshop, pp. 1–8. National Institute of Standards and Technology, Gaithersburg (2000)Hernández-Orallo, J.: AI evaluation: past, present and future (2014). arXiv preprint arXiv:1408.6908Hernández-Orallo, J.: On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems, 1–53 (2014). http://dx.doi.org/10.1007/s10458-014-9257-1Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Hernández-Orallo, J., Dowe, D.L., Hernández-Lloreda, M.V.: Universal psychometrics: Measuring cognitive abilities in the machine kingdom. Cognitive Systems Research 27, 50–74 (2014)Hernández-Orallo, J., Minaya-Collado, N.: A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proc. Intl. Symposium of Engineering of Intelligent Systems (EIS 1998), pp. 146–163. ICSC Press (1998)Hibbard, B.: Bias and no free lunch in formal measures of intelligence. Journal of Artificial General Intelligence 1(1), 54–61 (2009)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Li, M., Vitányi, P.: An introduction to Kolmogorov complexity and its applications, 3 edn. Springer-Verlag (2008)Schaul, T.: An extensible description language for video games. IEEE Transactions on Computational Intelligence and AI in Games PP(99), 1–1 (2014)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and control 7(1), 1–22 (1964