1,671 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (âAIâ) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics â and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatĂłlica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Temporally extended goal recognition in fully observable non-deterministic domain models
This work has been partially supported by the ERC-ADGWhiteMech (No. 834228), the EU ICT-48 2020 project TAILOR (No. 952215), the PRIN project RIPER (No. 20203FFYLK),and the PNRR MUR project FAIR (No. PE0000013).Peer reviewedPublisher PD
Thought Cloning: Learning to Think while Acting by Imitating Human Thinking
Language is often considered a key aspect of human thinking, providing us
with exceptional abilities to generalize, explore, plan, replan, and adapt to
new situations. However, Reinforcement Learning (RL) agents are far from
human-level performance in any of these abilities. We hypothesize one reason
for such cognitive deficiencies is that they lack the benefits of thinking in
language and that we can improve AI agents by training them to think like
humans do. We introduce a novel Imitation Learning framework, Thought Cloning,
where the idea is to not just clone the behaviors of human demonstrators, but
also the thoughts humans have as they perform these behaviors. While we expect
Thought Cloning to truly shine at scale on internet-sized datasets of humans
thinking out loud while acting (e.g. online videos with transcripts), here we
conduct experiments in a domain where the thinking and action data are
synthetically generated. Results reveal that Thought Cloning learns much faster
than Behavioral Cloning and its performance advantage grows the further out of
distribution test tasks are, highlighting its ability to better handle novel
situations. Thought Cloning also provides important benefits for AI Safety and
Interpretability, and makes it easier to debug and improve AI. Because we can
observe the agent's thoughts, we can (1) more easily diagnose why things are
going wrong, making it easier to fix the problem, (2) steer the agent by
correcting its thinking, or (3) prevent it from doing unsafe things it plans to
do. Overall, by training agents how to think as well as behave, Thought Cloning
creates safer, more powerful agents
Essays on behavioural economics: Uncovering drivers of altruistic behaviour
This Ph.D. thesis aims to study the economic foundations of prosocial behaviour in multiple significant ways. First, it emphases that altruistic behaviour can be the result of individual differences in people when using survey experiments, but also in nations when analysing information at an aggregate level. Second, it also states that altruism appears more prominently when more options are given for decision-makers to choose among alternatives to donate. For that purpose, I present a broad literature review and four empirical essays that provide new evidence on these particular topics. The first essay makes an overall analysis on altruism at a global scale using a database from The World Bank and The World Happiness report for the period 2020. The empirical analysis is conducted using cross-sectional country data from a sample of 128 worldwide countries in the 6 continents. The results suggest that nations which exhibit higher generosity levels are also quite distinct from the others, such as in the level of economic development, in some socio-demographic variables and cultural dimensions. The other three essays are based on the collection of experimental survey data aiming at identifying new factors that may explain generous behaviour in individuals. Specifically, the second tries to stablish a relationship between free will beliefs and giving, the third relates cognitive skills with strategic thinking abilities and the last one studies how the number of options available affects giving. The results suggest that higher free will beliefs have a statistically significant effect on generous concerns. Personal cognitive skills and strategic thinking abilities also have a relationship with giving. However, the former has a negative influence while the latter is positive. Finally, in the last essay, I observe that generosity increases when more recipient options are available and this effect is statistically significant, as well. This thesis contributes to our understanding of prosocial behaviour in terms of individual and country characteristics that are correlated with altruistic behaviour.Esta tese de doutoramento visa estudar as bases econĂłmicas do comportamento pro-social de vĂĄrias formas distintas: Em primeiro lugar, enfatiza que o comportamento altruĂsta pode ser o resultado de diferenças individuais em seres humanos, quando se recolhem dados atravĂ©s de inquĂ©ritos, mas tambĂ©m em paĂses, quando se analisa informação a um nĂvel agregado. Em segundo lugar, demonstra que o comportamento altruĂsta emerge de uma forma mais notĂłria quando sĂŁo dadas mais opçÔes Ă s pessoas para escolherem entre alternativas para doar. Para o efeito, apresento uma revisĂŁo da literatura generalizada e quatro ensaios empĂricos que sugerem novas evidĂȘncias sobre estes tĂłpicos, em particular. O primeiro ensaio faz uma anĂĄlise sobre o altruĂsmo Ă escala global utilizando dados do Banco Mundial e do relatĂłrio The World Happiness Report referente ao perĂodo de 2020. A anĂĄlise empĂrica Ă© conduzida utilizando dados de uma amostra de 128 paĂses em 6 continentes. Os resultados sugerem que as naçÔes que apresentam nĂveis de generosidade mais elevados sĂŁo tambĂ©m bastante distintas em relação Ă s restantes, nomeadamente ao nĂvel do desenvolvimento econĂłmico, na vertente sociodemogrĂĄfica e ainda culturalmente. Os outros trĂȘs ensaios baseiam-se na recolha de dados atravĂ©s de inquĂ©ritos com o objetivo de identificar novos fatores que possam explicar o comportamento pro-social em indivĂduos. Especificamente, o segundo tenta estabelecer uma relação entre crenças no livre-arbĂtrio e generosidade, o terceiro com capacidades cognitivas/estratĂ©gicas e o Ășltimo com o nĂșmero de opçÔes disponĂveis para doação. Os resultados sugerem que as pessoas que possuem crenças mais robustas no livre-arbĂtrio revelam tambĂ©m ter maiores tendĂȘncias generosas. Os resultados sugerem ainda que as competĂȘncias cognitivas e as capacidades de pensamento estratĂ©gico tĂȘm tambĂ©m uma relação com o altruĂsmo. No entanto, o primeiro fator tem uma influĂȘncia negativa enquanto o segundo positiva. Finalmente, no Ășltimo ensaio, foi observado que a
generosidade aumenta quando estĂŁo disponĂveis mais opçÔes para doar. Globalmente, esta tese contribui para aumentar a nossa compreensĂŁo do comportamento pro-social em termos das caracterĂsticas individuais que lhe estĂŁo correlacionadas
Investigating Digital Corporate Reporting from an Upper Echelons Theory Perspective: Evidence from the Arab Middle East
Utilising the insights of Upper Echelons Theory (UET) and bounded rationality assumption, this original study aimed to investigate the association between corporate leadersâ characteristics and both the extent and readability of Digital Corporate Reporting (DCR). Content analysis of corporate websites of 122 publicly listed Jordananian firms has been carried out. The logistics regression analysis revealed that maintaining a functioning corporate website is inversely associated with CEO age. This indicates that younger CEOs are more likely to retain a web presence for the firm than their older counterparts. The OLS regression analysis revealed that CEOsâ education and tenure were negatively associated with the extent of DCR. Moreover, it was found that Corporate Governance (CG) moderating variables hardly lessen this relationship. The results confirm the current thoughts regarding the rise of CEO effects in corporations with unique evidence from the Arab Middle East (AME). Building on the previous evidence, the study also aimed at uncovering the association between chairman characteristics and the readability of the digital version of the chairmanâs Letter to Shareholders (LTS). A hand-built dataset from a sample of 379 LTS from 101 publicly listed firms in 7 AME countries over five years (2014 â 2018) were employed to achieve this objective. Focusing on the clarity of DCR, the results of this second part of this study emphasizes the potential of UET to provide incremental plausible explanations of the variance in the levels of readability of LTS. The clustered regression results of the panel data demonstrate that older and less educated chairpersons are associated with more readable disclosures. Such findings on disclosure styles demonstrate the power of individuals in positions of authority as a consequence of higher education and tenure. Such findings contribute to the evolving inquiry on the significance of readability for enhancing corporate disclosure transparency and have implications for improving the DCR extent and readability
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific
method to answer questions about why natural phenomena occur and to make
testable models that explain the phenomena. Discovering equations, laws and
principles that are invariant, robust and causal explanations of the world has
been fundamental in physical sciences throughout the centuries. Discoveries
emerge from observing the world and, when possible, performing interventional
studies in the system under study. With the advent of big data and the use of
data-driven methods, causal and equation discovery fields have grown and made
progress in computer science, physics, statistics, philosophy, and many applied
fields. All these domains are intertwined and can be used to discover causal
relations, physical laws, and equations from observational data. This paper
reviews the concepts, methods, and relevant works on causal and equation
discovery in the broad field of Physics and outlines the most important
challenges and promising future lines of research. We also provide a taxonomy
for observational causal and equation discovery, point out connections, and
showcase a complete set of case studies in Earth and climate sciences, fluid
dynamics and mechanics, and the neurosciences. This review demonstrates that
discovering fundamental laws and causal relations by observing natural
phenomena is being revolutionised with the efficient exploitation of
observational data, modern machine learning algorithms and the interaction with
domain knowledge. Exciting times are ahead with many challenges and
opportunities to improve our understanding of complex systems.Comment: 137 page
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