1,409 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 (āAIā) 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 ā 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 CatĆ³lica 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
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
āThe United States, itās supposed to be where dreams come trueā : rhizomatic familias, nested policy contexts, and the attendant shaping of undocumented and mixed status studentsā lived experiences in North Carolina
This qualitative research study explores the nature of the nested contexts (historical, political, socio-cultural) within which migrant youth experience restrictive immigration policies in North Carolina, while also examining how these youth perceive and experience the enactment of these policies through an interpretive policy framework combined with a critical policy analysis lens. Spotlighting these experiences highlights not only the structural obstacles and challenges these youth and their families face both within educational settings and in their daily life, but, importantly, underscores their capacity for agency and function as policy actors with the ability to recreate policy meaning and effect transformative change. The findings of this study suggest a need for structural policy reform and a series of systemic reforms within K-12 educational settings in North Carolina to provide schools with institutional mechanisms of support to meet migrant studentsā needs. The study also develops a new conceptual framework, rhizomatic familias, on the basis of Deleuze and Guattariās (1987) concept of the rhizome to call attention to mixed status familiesā uniform experiences of illegality
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well
Collective agency:From philosophical and logical perspectives
People inhabit a vast and intricate social network nowadays. In addition to our own decisions and actions, we confront those of various groups every day. Collective decisions and actions are more complex and bewildering compared to those made by individuals. As members of a collective, we contribute to its decisions, but our contributions may not always align with the outcome. We may also find ourselves excluded from certain groups and passively subjected to their influences without being aware of the source. We are used to being in overlapping groups and may switch identities, supporting or opposing the claims of particular groups. But rarely do we pause to think: What do we talk about when we talk about groups and their decisions?At the heart of this dissertation is the question of collective agency, i.e., in what sense can we treat a group as a rational agent capable of its action. There are two perspectives we take: a philosophical and logical one. The philosophical perspective mainly discusses the ontological and epistemological issues related to collective agency, sorts out the relevant philosophical history, and argues that the combination of a relational view of collective agency and a dispositional view of collective intentionality provides a rational and realistic account. The logical perspective is associated with formal theories of groups, it disregards the psychological content involved in the philosophical perspective, establishes a logical system that is sufficiently formal and objective, and axiomatizes the nature of a collective
Human participants in AI research: Ethics and transparency in practice
In recent years, research involving human participants has been critical to
advances in artificial intelligence (AI) and machine learning (ML),
particularly in the areas of conversational, human-compatible, and cooperative
AI. For example, around 12% and 6% of publications at recent AAAI and NeurIPS
conferences indicate the collection of original human data, respectively. Yet
AI and ML researchers lack guidelines for ethical, transparent research
practices with human participants. Fewer than one out of every four of these
AAAI and NeurIPS papers provide details of ethical review, the collection of
informed consent, or participant compensation. This paper aims to bridge this
gap by exploring normative similarities and differences between AI research and
related fields that involve human participants. Though psychology,
human-computer interaction, and other adjacent fields offer historic lessons
and helpful insights, AI research raises several specific
concerns\unicode{x2014}namely, participatory design, crowdsourced dataset
development, and an expansive role of corporations\unicode{x2014}that
necessitate a contextual ethics framework. To address these concerns, this
paper outlines a set of guidelines for ethical and transparent practice with
human participants in AI and ML research. These guidelines can be found in
Section 4 on pp. 4\unicode{x2013}7
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
Reflective Artificial Intelligence
As Artificial Intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today's AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward
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