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Díaz-Rodríguez N, Binkytė R, Bakkali W, Bookseller S, Tubaro P, Bacevičius A, Zhioua S, Chatila R. Gender and sex bias in COVID-19 epidemiological data through the lens of causality. Inf Process Manag 2023; 60:103276. [PMID: 36647369 PMCID: PMC9834203 DOI: 10.1016/j.ipm.2023.103276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 12/08/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
The COVID-19 pandemic has spurred a large amount of experimental and observational studies reporting clear correlation between the risk of developing severe COVID-19 (or dying from it) and whether the individual is male or female. This paper is an attempt to explain the supposed male vulnerability to COVID-19 using a causal approach. We proceed by identifying a set of confounding and mediating factors, based on the review of epidemiological literature and analysis of sex-dis-aggregated data. Those factors are then taken into consideration to produce explainable and fair prediction and decision models from observational data. The paper outlines how non-causal models can motivate discriminatory policies such as biased allocation of the limited resources in intensive care units (ICUs). The objective is to anticipate and avoid disparate impact and discrimination, by considering causal knowledge and causal-based techniques to compliment the collection and analysis of observational big-data. The hope is to contribute to more careful use of health related information access systems for developing fair and robust predictive models.
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Affiliation(s)
- Natalia Díaz-Rodríguez
- DaSCI Andalusian Institute in Data Science and Computational Intelligence, CITIC, Dpt. of Computer Science and Artificial Intelligence, University of Granada, Spain
| | | | - Wafae Bakkali
- Amazon Machine Learning Solutions Lab, Amazon Web Services, Paris, France
| | | | - Paola Tubaro
- LISN-TAU, CNRS, University Paris-Saclay, Inria, France
| | | | - Sami Zhioua
- INRIA, École Polytechnique, IPP, Paris, France
| | - Raja Chatila
- ISIR (Institute of Intelligent Systems and Robotics), Sorbonne University, Paris, France
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Angelotti G, Díaz-Rodríguez N. Towards a more efficient computation of individual attribute and policy contribution for post-hoc explanation of cooperative multi-agent systems using Myerson values. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Kaczmarek-Majer K, Casalino G, Castellano G, Dominiak M, Hryniewicz O, Kamińska O, Vessio G, Díaz-Rodríguez N. PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Kusters R, Misevic D, Berry H, Cully A, Le Cunff Y, Dandoy L, Díaz-Rodríguez N, Ficher M, Grizou J, Othmani A, Palpanas T, Komorowski M, Loiseau P, Moulin Frier C, Nanini S, Quercia D, Sebag M, Soulié Fogelman F, Taleb S, Tupikina L, Sahu V, Vie JJ, Wehbi F. Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Front Big Data 2020; 3:577974. [PMID: 33693418 PMCID: PMC7931862 DOI: 10.3389/fdata.2020.577974] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/28/2020] [Indexed: 11/25/2022] Open
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.
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Affiliation(s)
- Remy Kusters
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | - Dusan Misevic
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | | | | | | | - Loic Dandoy
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | - Natalia Díaz-Rodríguez
- Inria Flowers, Paris and Bordeaux, France
- ENSTA Paris, Institut Polytechnique Paris, Paris, France
| | - Marion Ficher
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | - Jonathan Grizou
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | - Alice Othmani
- Université Paris-Est, LISSI, Vitry sur Seine, France
| | - Themis Palpanas
- Université de Paris, France and French University Institute (IUF), Paris, France
| | | | - Patrick Loiseau
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, Grenoble, France
| | | | - Santino Nanini
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | | | - Michele Sebag
- TAU, LRI-CNRS–INRIA, Universite Paris-Saclay, France
| | | | - Sofiane Taleb
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | - Liubov Tupikina
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
- Nokia Bell Labs, Paris, France
| | - Vaibhav Sahu
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
| | | | - Fatima Wehbi
- INSERM U1284, Université de Paris, Center for Research and Interdisciplinarity (CRI), Paris, France
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Lesort T, Díaz-Rodríguez N, Goudou JF, Filliat D. State representation learning for control: An overview. Neural Netw 2018; 108:379-392. [DOI: 10.1016/j.neunet.2018.07.006] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 06/05/2018] [Accepted: 07/10/2018] [Indexed: 10/28/2022]
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Doncieux S, Filliat D, Díaz-Rodríguez N, Hospedales T, Duro R, Coninx A, Roijers DM, Girard B, Perrin N, Sigaud O. Open-Ended Learning: A Conceptual Framework Based on Representational Redescription. Front Neurorobot 2018; 12:59. [PMID: 30319388 PMCID: PMC6167466 DOI: 10.3389/fnbot.2018.00059] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 08/28/2018] [Indexed: 11/29/2022] Open
Abstract
Reinforcement learning (RL) aims at building a policy that maximizes a task-related reward within a given domain. When the domain is known, i.e., when its states, actions and reward are defined, Markov Decision Processes (MDPs) provide a convenient theoretical framework to formalize RL. But in an open-ended learning process, an agent or robot must solve an unbounded sequence of tasks that are not known in advance and the corresponding MDPs cannot be built at design time. This defines the main challenges of open-ended learning: how can the agent learn how to behave appropriately when the adequate states, actions and rewards representations are not given? In this paper, we propose a conceptual framework to address this question. We assume an agent endowed with low-level perception and action capabilities. This agent receives an external reward when it faces a task. It must discover the state and action representations that will let it cast the tasks as MDPs in order to solve them by RL. The relevance of the action or state representation is critical for the agent to learn efficiently. Considering that the agent starts with a low level, task-agnostic state and action spaces based on its low-level perception and action capabilities, we describe open-ended learning as the challenge of building the adequate representation of states and actions, i.e., of redescribing available representations. We suggest an iterative approach to this problem based on several successive Representational Redescription processes, and highlight the corresponding challenges in which intrinsic motivations play a key role.
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Affiliation(s)
| | - David Filliat
- U2IS, INRIA Flowers, ENSTA ParisTech, Palaiseau, France
| | | | - Timothy Hospedales
- Institute of Perception, Action and Behaviour, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - Diederik M. Roijers
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Hamari L, Kullberg T, Ruohonen J, Heinonen OJ, Díaz-Rodríguez N, Lilius J, Pakarinen A, Myllymäki A, Leppänen V, Salanterä S. Physical activity among children: objective measurements using Fitbit One ® and ActiGraph. BMC Res Notes 2017; 10:161. [PMID: 28427441 PMCID: PMC5397828 DOI: 10.1186/s13104-017-2476-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 04/01/2017] [Indexed: 11/18/2022] Open
Abstract
Background Self-quantification of health parameters is becoming more popular; thus, the validity of the devices requires assessments. The aim of this study was to evaluate the validity of Fitbit One step counts (Fitbit Inc., San Francisco, CA, USA) against Actigraph wActisleep-BT step counts (ActiGraph, LLC, Pensacola, FL, USA) for measuring habitual physical activity among children. Design The study was implemented as a cross-sectional experimental design in which participants carried two waist-worn activity monitors for five consecutive days. Methods The participants were chosen with a purposive sampling from three fourth grade classes (9–10 year olds) in two comprehensive schools. Altogether, there were 34 participants in the study. From these, eight participants were excluded from the analysis due to erroneous data. Primary outcome measures for step counts were Fitbit One and Actigraph wActisleep-BT. The supporting outcome measures were based on activity diaries and initial information sheets. Classical Bland–Altman plots were used for reporting the results. Results The average per-participant daily difference between the step counts from the two devices was 1937. The range was [116, 5052]. Fitbit One gave higher step counts for all but the least active participant. According to a Bland–Altman plot, the hourly step counts had a relative large mean bias across participants (161 step counts). The differences were partially explained by activity intensity: higher intensity denoted higher differences, and light intensity denoted lower differences. Conclusions Fitbit One step counts are comparable to Actigraph step counts in a sample of 9–10-year-old children engaged in habitual physical activity in sedentary and light physical activity intensities. However, in moderate-to-vigorous physical activity, Fitbit One gives higher step counts when compared to Actigraph.
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Affiliation(s)
- Lotta Hamari
- Department of Nursing Science, University of Turku, 20014, Turku, Finland. .,Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland.
| | - Tiina Kullberg
- Department of Information Technology, University of Turku, 20014, Turku, Finland
| | - Jukka Ruohonen
- Department of Information Technology, University of Turku, 20014, Turku, Finland
| | - Olli J Heinonen
- Paavo Nurmi Centre & Department of Physical Activity and Health, University of Turku, Kiinamyllynkatu 10, 20520, Turku, Finland.,Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland
| | - Natalia Díaz-Rodríguez
- Turku Centre for Computer Science (TUCS), Department of Information Technologies, Åbo Akademi University, Joukahaisenkatu 3-5 A, 20520, Turku, Finland
| | - Johan Lilius
- Turku Centre for Computer Science (TUCS), Department of Information Technologies, Åbo Akademi University, Joukahaisenkatu 3-5 A, 20520, Turku, Finland
| | - Anni Pakarinen
- Department of Nursing Science, University of Turku, 20014, Turku, Finland.,Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland
| | - Annukka Myllymäki
- Department of Nursing Science, University of Turku, 20014, Turku, Finland.,Health and Well-being Unit, Turku University of Applied Sciences, Ruiskatu 8, 20720, Turku, Finland
| | - Ville Leppänen
- Department of Information Technology, University of Turku, 20014, Turku, Finland
| | - Sanna Salanterä
- Department of Nursing Science, University of Turku, 20014, Turku, Finland.,Turku University Hospital, Kiinamyllynkatu 4-8, 20521, Turku, Finland
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Díaz-Rodríguez N, Cadahía OL, Cuéllar MP, Lilius J, Calvo-Flores MD. Handling real-world context awareness, uncertainty and vagueness in real-time human activity tracking and recognition with a fuzzy ontology-based hybrid method. Sensors (Basel) 2014; 14:18131-71. [PMID: 25268914 PMCID: PMC4239884 DOI: 10.3390/s141018131] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Revised: 09/18/2014] [Accepted: 09/18/2014] [Indexed: 11/16/2022]
Abstract
Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and Sensors 2014, 14 18132 high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.
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Affiliation(s)
- Natalia Díaz-Rodríguez
- Akademi University, Department of Information Technologies, Turku Centre for Computer Science (TUCS) - Joukahainengatan, 3-5, Turku FIN-20520, Finland.
| | - Olmo León Cadahía
- University of Granada, Department of Computer Science and Artificial Intelligence, E.T.S.I. Informática y de Telecomunicación -C/. Periodista Daniel Saucedo Aranda s.n., Granada 18071, Spain.
| | - Manuel Pegalajar Cuéllar
- University of Granada, Department of Computer Science and Artificial Intelligence, E.T.S.I. Informática y de Telecomunicación -C/. Periodista Daniel Saucedo Aranda s.n., Granada 18071, Spain
| | - Johan Lilius
- Akademi University, Department of Information Technologies, Turku Centre for Computer Science (TUCS) - Joukahainengatan, 3-5, Turku FIN-20520, Finland
| | - Miguel Delgado Calvo-Flores
- University of Granada, Department of Computer Science and Artificial Intelligence, E.T.S.I. Informática y de Telecomunicación -C/. Periodista Daniel Saucedo Aranda s.n., Granada 18071, Spain
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Díaz-Rodríguez N, Rodríguez-Lorenzo A, Castellano-Alarcón J, Molina-Martos E. Ecografía del muslo normal. Semergen 2008. [DOI: 10.1016/s1138-3593(08)71863-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Díaz-Rodríguez N, Rodríguez Lorenzo A, Castellano-Alarcón J, Antoral Arribas M. Ecografía del hombro normal. Semergen 2007. [DOI: 10.1016/s1138-3593(07)73932-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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