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3. Want Value from Big Data? Close the Gap between the C-Suite and the Server Room.

Author

O'Reilly, Kelley and Paper, David

Subjects
BIG data, DATA mining, DATABASES, PROBLEM solving, STOCKS (Finance)
Abstract

The author discusses the importance of big data for businesses. He cites an article which suggests that companies using data and business analytics to guide decision making are more productive and experience higher equity returns compared with companies that do not. He also notes the common pitfalls of big data initiatives.

Published
2012
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4. Towards a semantic indoor trajectory model: application to museum visits

Author

Claudia Marinica, Karine Zeitouni, Dimitris Kotzinos, Dan Vodislav, Alexandros Kontarinis, Données et algorithmes pour une ville intelligente et durable - DAVID (DAVID), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Horizon 2020 Framework Programme, H2020: 777695, This work was supported by the TRAJECTOIRES project funded by the Paris Seine Graduate School Humanities, Creation, Heritage, Investissement d’Avenir ANR-17-EURE-0021 – Foundation for Cultural Heritage Science., and Karine Zeitouni’s work in this paper was supported by the MASTER project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie-Slodowska Curie grant agreement N. 777695.

Subjects
Computer science, media_common.quotation_subject, Geography, Planning and Development, Big data, Museum visitor study, 02 engineering and technology, Space (commercial competition), Article, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Sequential Pattern Mining, Representation (mathematics), media_common, Information retrieval, business.industry, Semantic trajectory, Indoor trajectory, Analytics, Model application, Trajectory pattern mining, Trajectory, Conceptual model, 020201 artificial intelligence & image processing, Trajectory modeling, Louvre museum, business, Information Systems
Abstract

International audience; In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC’s IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre’s visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns.

Published
2021

5. Does Big Data Improve Financial Forecasting? The Horizon Effect

Author

Olivier Dessaint, Thierry Foucault, Laurent Frésard, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research Paper Series

Subjects
History, Polymers and Plastics, Process (engineering), Computer science, Big data, Industrial and Manufacturing Engineering, Social media, 0502 economics and business, Econometrics, 050207 economics, Business and International Management, Financial forecasting, Measure (data warehouse), 050208 finance, Forecasting horizon, business.industry, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G17 - Financial Forecasting and Simulation, 05 social sciences, Forecasts' informativeness, JEL: D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D84 - Expectations • Speculations, ComputingMilieux_GENERAL, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G14 - Information and Market Efficiency • Event Studies • Insider Trading, Financial analysts' forecasts, JEL: M - Business Administration and Business Economics • Marketing • Accounting • Personnel Economics/M.M4 - Accounting and Auditing/M.M4.M41 - Accounting, [SHS.GESTION]Humanities and Social Sciences/Business administration, business
Abstract

We study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts produce forecasts of short-term and long-term earnings and choose how much information to collect about each horizon to minimize their expected forecasting error, net of information acquisition costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information collection strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. Using a large sample of analysts' forecasts at various horizons and novel measures of their exposure to abundant data (e.g., social media data), we provide empirical support for this prediction, which implies that data abundance can impair the quality of long-term forecasts.

Published
2020

6. Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities

Author

Remy Kusters, Dusan Misevic, Hugues Berry, Antoine Cully, Yann Le Cunff, Loic Dandoy, Natalia Díaz-Rodríguez, Marion Ficher, Jonathan Grizou, Alice Othmani, Themis Palpanas, Matthieu Komorowski, Patrick Loiseau, Clément Moulin Frier, Santino Nanini, Daniele Quercia, Michele Sebag, Françoise Soulié Fogelman, Sofiane Taleb, Liubov Tupikina, Vaibhav Sahu, Jill-Jênn Vie, Fatima Wehbi, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Artificial Evolution and Computational Biology (BEAGLE), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Imperial College London, Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Performance analysis and optimization of LARge Infrastructures and Systems (POLARIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Nokia Bell Labs [Cambridge], TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Hub France IA, Nokia Bell Labs [Paris-Saclay], Scool (Scool), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Thanks to the Bettencourt Schueller Foundation long term partnership, the workshop that gave rise to this paper was partially supported by the funding from CRI Research Collaboratory, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )

Subjects
Big Data, 0301 basic medicine, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Political science, Health care, Computer Science (miscellaneous), auditability, education, lcsh:T58.5-58.64, Ai education, business.industry, lcsh:Information technology, interdisciplinary science, Precision medicine, artificial intelligence, Transparency (behavior), ethics, Variety (cybernetics), 030104 developmental biology, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Perspective, Artificial intelligence, business, interpretability, 030217 neurology & neurosurgery, Information Systems
Abstract

The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.

Published
2020

7. A Three-Tier Architecture of Large-Scale Wireless Sensor Networks for Big Data Collection

Author

Ousmane Thiare, Asside Christian Djedouboum, Abdelhak Mourad Gueroui, Alidou Mohamadou, Ado Adamou Abba Ari, Zibouda Aliouat, Laboratoire d'Informatique Parallélisme Réseaux Algorithmes Distribués (LI-PaRAD), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), University of Maroua (UMa), Université Gaston Berger de Saint-Louis Sénégal (UGB), University of Ferhat Abbes Setif 1 Department of Computer Science, 18MDU114, and This research was funded in part by the PHC-Tassili grant number 18MDU114. We like to thank the editor and the anonymous reviewers for their valuable remarks that helped us in better improving the content and presentation of the paper.

Subjects
data collection, architecture, Computer science, Big data, Context (language use), 02 engineering and technology, 7. Clean energy, 01 natural sciences, lcsh:Technology, lcsh:Chemistry, [SPI]Engineering Sciences [physics], 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Instrumentation, lcsh:QH301-705.5, Fluid Flow and Transfer Processes, business.industry, lcsh:T, Process Chemistry and Technology, Node (networking), Multitier architecture, 010401 analytical chemistry, General Engineering, 020206 networking & telecommunications, Energy consumption, lcsh:QC1-999, 0104 chemical sciences, Computer Science Applications, Large-Scale Wireless Sensor Networks, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, Models of communication, Sink (computing), business, lcsh:Engineering (General). Civil engineering (General), Wireless sensor network, lcsh:Physics, Computer network, clustering
Abstract

International audience; In recent years, technological advances and the ever-increasing power of embedded systems have seen the emergence of so-called smart cities. In these cities, application needs are increasingly calling for Large-Scale Wireless Sensor Networks (LS-WSN). However, the design and implementation of such networks pose several important and interesting challenges. These low-cost, low-power devices are characterized by limited computing, memory storage, communication, and battery power capabilities. Moreover, sensors are often required to cooperate in order to route the collected data to a single central node (or sink). The many-to-one communication model that governs dense and widely deployed Wireless Sensor Networks (WSNs) most often leads to problems of network overload and congestion. Indeed, it is easy to show that the closer a node is geographical to the sink, the more data sources it has to relay. This leads to several problems including overloading of nodes close to the sink, high loss rate in the area close to the sink, and poor distribution of power consumption that directly affects the lives of these networks. In this context, we propose a contribution to the problem of LS-WSN energy consumption. We designed a hierarchical 3-tier architecture of LS-WSNs coupled with a modeling of the activities of the different sensors in the network. This architecture that is based on clustering also includes a redeployment function to maintain the topology in case of coverage gaps. The results of the performed simulations show that our architecture maximizes the lifetime than compared solutions.

Published
2020

8. Machines and Masterpieces: Predicting Prices in the Art Auction Market

Author

Roman Kräussl, Gustavo Manso, Mathieu Aubry, Christophe Spaenjers, Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Department of Economics, Tilburg University [Netherlands], HEC Research Paper Series, and Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)

Subjects
asset valuation, History, Polymers and Plastics, Computer science, Big data, Machine learning, computer.software_genre, Industrial and Manufacturing Engineering, big data, 0502 economics and business, Common value auction, Price level, auctions, 050207 economics, Business and International Management, JEL: C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C50 - General, Valuation (finance), JEL: D - Microeconomics/D.D4 - Market Structure, Pricing, and Design/D.D4.D44 - Auctions, 050208 finance, Artificial neural network, Ex-ante, business.industry, 05 social sciences, experts, Hedonic pricing, TheoryofComputation_GENERAL, JEL: Z - Other Special Topics/Z.Z1 - Cultural Economics • Economic Sociology • Economic Anthropology/Z.Z1.Z11 - Economics of the Arts and Literature, machine learning, [SHS.GESTION]Humanities and Social Sciences/Business administration, Artificial intelligence, business, computer, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G12 - Asset Pricing • Trading Volume • Bond Interest Rates
Abstract

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.

Published
2020

9. The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence

Author

Paola Tubaro, Antonio A. Casilli, Marion Coville, Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institutions et Dynamiques Historiques de l'Économie et de la Société (IDHES), Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris Nanterre (UPN)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), Sociologie Information-Communication Design (SID), Institut interdisciplinaire de l’innovation de Telecom Paris (I3 SES), Télécom ParisTech-Institut interdisciplinaire de l’innovation (I3), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Télécom ParisTech-Institut interdisciplinaire de l’innovation (I3), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Département Sciences Economiques et Sociales (SES), Télécom ParisTech, Institut Polytechnique de Paris (IP Paris), Institut d'Administration des Entreprises (IAE) - Poitiers (IAE Poitiers), Université de Poitiers, CEntre de REcherche en GEstion - EA 1722 (CEREGE), Université de Poitiers-Université de Poitiers-Université de Poitiers-La Rochelle Université (ULR), This paper presents results of a larger study called Digital Platform Labour (DiPLab), co-funded by Maison des Sciences de l’Homme Paris-Saclay, Force Ouvrière, a workers’ union, as part of a grant from Institut de recherches économiques et sociales (IRES), and France Stratégie, a service of the French Prime Minister’s office. The platform Foule Factory offered logistical support, and Inria provided complementary funding. For more information: http://diplab.eu, Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Nanterre (UPN)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris 1 Panthéon-Sorbonne (UP1), Institut interdisciplinaire de l’innovation (I3, une unité mixte de recherche CNRS (UMR 9217)), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université de Poitiers-Université de Poitiers-Université de Poitiers-Université de La Rochelle (ULR), École polytechnique (X)-Télécom ParisTech-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), and École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris

Subjects
Information Systems and Management, Computer science, Trainer, Big data, 050801 communication & media studies, lcsh:A, Library and Information Sciences, micro-work, Digital platform labour, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0508 media and communications, datafied production processes, 0502 economics and business, 050207 economics, Focus (computing), [SHS.SOCIO]Humanities and Social Sciences/Sociology, business.industry, Communication, 05 social sciences, artificial intelligence, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Computer Science Applications, machine learning, Artificial intelligence, lcsh:General Works, business, Information Systems
Abstract

International audience; This paper sheds light on the role of digital platform labour in the development of today's artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad 'micro-workers', recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, 'artificial intelligence preparation', 'artificial intelligence verification' and 'artificial intelligence impersonation'. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes - not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.

Published
2020

10. Towards a European Strategy on Business-To-Government Data Sharing for the Public Interest

Author

Alemanno, Alberto, Haldemann, Antoine, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research Paper Series

Subjects
Data, data sharing, risk governance, JEL: K - Law and Economics/K.K2 - Regulation and Business Law/K.K2.K23 - Regulated Industries and Administrative Law, data philanthropy, data risk, JEL: I - Health, Education, and Welfare/I.I1 - Health/I.I1.I18 - Government Policy • Regulation • Public Health, data invisible, big data, JEL: K - Law and Economics/K.K3 - Other Substantive Areas of Law/K.K3.K32 - Environmental, Health, and Safety Law, JEL: K - Law and Economics/K.K4 - Legal Procedure, the Legal System, and Illegal Behavior/K.K4.K40 - General, [SHS.GESTION]Humanities and Social Sciences/Business administration, philanthropy, [SHS.GESTION] Humanities and Social Sciences/Business administration, data governance
Abstract

Data is increasingly regarded as an essential infrastructural resource for economic growth, innovation and the overall wellbeing of society. Thanks to our enhanced capability to collect, process and use data, we are able to know where the epicentre of an earthquake is, how to limit a pandemic such as Zika from spreading or even how to reduce pollution in cities.This data ‘revolution’ must be contextualised within a broader, 3-centurylong attempt to neutralise irrationality in human decisionmaking by gaining increased access to information. Yet, while virtually every organisation today, including small companies or grassroots movements, is a data entity, only a few of them (generally in the private sector) have collected vast amounts of data (be it personal or non-personal) and acquired a unique capability to make sense of such information.As a result, most of this much-prized data is in the hands of businesses, not of public authorities, with the latter lagging behind in embracing the power of data to inform their daily policies and service-delivery actions. Hence the challenge — entrusted by the European Commission to our expert group — to explore the creation of an enabling environment for privately held data to be shared with (or at least be accessible to) public authorities in complying with their public-interest missions. For the time being, most of the efforts focus on getting the supply-side ready (e.g. by developing data-sharing models, de-risking data sharing or re-skilling personnel) for B2G data sharing, but omit the need to sensitise and prepare the demand side (the public authorities). More critically, at a time in which reputation-based mechanisms transcend the mere financial rating and venture into state-driven ‘social credit’ scoring, the role that citizens play in the data-sharing equation remains limited and ancillary to most of the solutions at stake.This report, which has benefited from the experience and dedication of 23 experts coming from different walks of life, strives to address both of these systemic shortcomings. By carving out a role to play for both the public sector and citizens, it offers an initial set of ethically-aware paths the EU might chart to break new ground in advancing and accompanying B2G data sharing. The hope is that by following some of this expert advice the EU might become a global leader in fostering not only a market for B2G data access, but also a sensible, inclusive and participatory data culture through a set of viable, practicable and scalable welfare-enhancing solutions. Only a more cautious, humble and humanised approach to the unprecedented amount of data we produce and collect every day may pave the way for a future in which it is the human factor that defines our daily life experiences

Published
2020

11. Demand for Information, Macroeconomic Uncertainty, and the Response of U.S. Treasury Securities to News

Author

Hedi Benamar, Thierry Foucault, Clara Vega, Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS), and HEC Research Paper Series

Subjects
Public information, Media Coverage, business.industry, Big data, Measure (physics), Uncertainty, Macroeconomic News, JEL: D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D83 - Search • Learning • Information and Knowledge • Communication • Belief • Unawareness, Investors' Attention, Treasury, Bitly, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G14 - Information and Market Efficiency • Event Studies • Insider Trading, JEL: G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G21 - Banks • Depository Institutions • Micro Finance Institutions • Mortgages, Future interest, Path (graph theory), Econometrics, Economics, [SHS.GESTION]Humanities and Social Sciences/Business administration, Information Demand, U.S. Treasury futures, business, Information demand, Clickstream
Abstract

We use clickstream data to show that investors' demand for information about macroeconomic factors affecting the path of future interest rates is a measure of their uncertainty about this path. In particular, an increase in information demand ahead of influential economic announcements affecting investors' beliefs about future interest rates predicts a stronger reaction of U.S. Treasury note yields to these announcements, as it should if information demand covaries positively with uncertainty. This relationship does not vanish after using standard measures of uncertainty as predictors, suggesting that clickstream data contain unique information about investors' uncertainty.

Published
2018

12. Prioritized Norms in Formal Argumentation

Author

Serena Villata, Beishui Liao, Nir Oren, Leendert van der Torre, Zhejiang University, University of Aberdeen, Individual and Collective Reasoning (ICR), University of Luxembourg [Luxembourg], Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Centre National de la Recherche Scientifique (CNRS), The research reported in this paper was partially supported by the Fundamental Research Funds for the Central Universities of China for the project Big Data, Reasoning and Decision Making, the National Research Fund Luxembourg (FNR) under grant INTER/MOBILITY/14/8813732 for the project FMUAT: Formal Models for Uncertain Argumentation from Text, and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 690974 for the project MIREL: MIning and REasoning with Legal texts., and European Project: 690974,H2020 Pilier Excellent Science,H2020-MSCA-RISE-2015,MIREL(2016)

Subjects
FOS: Computer and information sciences, Logic, Computer Science - Artificial Intelligence, Big data, [SCCO.COMP]Cognitive science/Computer science, 0102 computer and information sciences, normative multiagent systems, 01 natural sciences, ComputingMethodologies_ARTIFICIALINTELLIGENCE, Theoretical Computer Science, Argumentation theory, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Arts and Humanities (miscellaneous), normative systems, media_common.cataloged_instance, European commission, European union, China, Mathematics, media_common, deontic logic, Computer science [C05] [Engineering, computing & technology], business.industry, Deontic logic, Public relations, Sciences informatiques [C05] [Ingénierie, informatique & technologie], 16. Peace & justice, TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, Artificial Intelligence (cs.AI), 010201 computation theory & mathematics, Hardware and Architecture, Normative systems, business, formal argumentation, Social psychology, norms, Software
Abstract

To resolve conflicts among norms, various nonmonotonic formalisms can be used to perform prioritized normative reasoning. Meanwhile, formal argumentation provides a way to represent nonmonotonic logics. In this paper, we propose a representation of prioritized normative reasoning by argumentation. Using hierarchical abstract normative systems, we define three kinds of prioritized normative reasoning approaches, called Greedy, Reduction, and Optimization. Then, after formulating an argumentation theory for a hierarchical abstract normative system, we show that for a totally ordered hierarchical abstract normative system, Greedy and Reduction can be represented in argumentation by applying the weakest link and the last link principles respectively, and Optimization can be represented by introducing additional defeats capturing the idea that for each argument that contains a norm not belonging to the maximal obeyable set then this argument should be rejected., Accepted by the Journal of Logic and Computation on November 2nd, 2017

Published
2017

13. False News, Informational Efficiency, and Price Reversals

Author

Thierry Foucault, Jérôme Dugast, Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS), and HEC Paris Research Paper Series

Subjects
Big Data, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G10 - General, Financial economics, Lag, Big data, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Asset (computer security), FinTech, Econometrics, news, high-frequency trading, price reversals, informational efficiency, mini-flash crashes, Contrarian and momentum trading, JEL: G - Financial Economics/G.G3 - Corporate Finance and Governance/G.G3.G30 - General, business.industry, Noise (signal processing), Stochastic game, Information processing, Information Processing, jel:G12, jel:G10, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G14 - Information and Market Efficiency • Event Studies • Insider Trading, jel:G14, JEL: M - Business Administration and Business Economics • Marketing • Accounting • Personnel Economics/M.M4 - Accounting and Auditing/M.M4.M41 - Accounting, JEL: D - Microeconomics/D.D6 - Welfare Economics/D.D6.D61 - Allocative Efficiency • Cost–Benefit Analysis, Value (economics), Asset Price Informativeness, [SHS.GESTION]Humanities and Social Sciences/Business administration, business, Markets for Information, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G12 - Asset Pricing • Trading Volume • Bond Interest Rates, News, High-Frequency Trading, Price Reversals, Informational Efficiency, Mini-Flash Crashes
Abstract

Speculators can discover whether a signal is true or false by processing it but this takes time. Hence they face a trade-off between trading fast on a signal (i.e., before processing it), at the risk of trading on a false positive, or trading after processing the signal, at the risk that prices already reflect their information. The number of speculators who choose to trade fast increases with news reliability and decreases with the cost of fast trading technologies. The authors derive testable implications for the effects of these variables on (i) the value of information, (ii) patterns in returns and trades, (iii) the frequency of price reversals in a stock, and (iv) informational efficiency. Cheaper fast trading technologies simultaneously raise informational efficiency and the frequency of "mini-flash crashes": large price movements that revert quickly.

Published
2014