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robustness and explainability of artificial intelligence

Secure .gov websites use HTTPS Artificial Intelligence (AI) is a general-purpose technology that has the potential to improve the welfare and well-being of people, to contribute to positive sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges. SHARE: November 24, 2020. Researchers can use the Adversarial Robustness Toolbox to benchmark novel defenses against the state-of-the-art. Hamon, R., Junklewitz, H. and Sanchez Martin, J., Robustness and Explainability of Artificial Intelligence, EUR 30040 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-14660-5 (online), doi:10.2760/57493 (online), JRC119336. Artificial Intelligence (AI) is a general-purpose technology that has the potential to improve the welfare and well-being of people, to contribute to positive sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges. Our four principles are intended to capture a broad set of motivations, applications, and perspectives. Please direct questions to explainable-AI@nist.gov. The Adversarial Robustness Toolbox is designed to support researchers and developers in creating novel defense techniques, as well as in deploying practical defenses of real-world AI systems. IDC's Artificial Intelligence Strategies program assesses the state of the enterprise artificial intelligence (AI) journey, provides guidance on building new capabilities, and prioritizes investment options. However, their weakness was in dealing with uncertainties of the real world. Ever since, Explainability tackles the question of … Applications to Societal issues of Artificial Intelligence but also to Industrial Applications. Advancing artificial intelligence research. We provide data and multi-disciplinary analysis on artificial intelligence. AI News - Artificial Intelligence News How to cite this report: Hamon, R., Junklewitz, H., Sanchez, I. Robustness and Explainability of Artificial Intelligence - From technical to policy solutions , EUR 30040, Publications Office of the European Union, Luxembourg, Luxembourg, 2020, ISBN Page 10/29 The comment period for this document is now closed. This independent expert group was set up by the European Commission in June 2018, as part of the AI strategy announced earlier that year. Ultimately, the team plans to develop a metrologist’s guide to AI systems that address the complex entanglement of terminology and taxonomy as it relates to the myriad layers of the AI field. The Joint Research Centre ('JRC') Technical Report on Robustness and Explainability of Artificial Intelligence provides a detailed examination of transparency as it relates to AI systems. The ... bias and fairness, interpretability and explainability, and robustness and security. OECD AI Policy Observatory. However, this type of artificial intelligence (AI) has yet to be adopted clinically due to questions regarding robustness of the algorithms to datasets collected at new clinical sites and a lack of explainability of AI-based predictions, especially relative to those of human expert counterparts. Artificial intelligence systems are increasingly being used to support human decision-making. 2. The term artificial intelligence was coined in 1955 by John McCarthy, a math professor at Dartmouth who organized the seminal conference on the topic the following year. Guidance On Definitions And Metrics To Evaluate AI For Bias And Fairness. Secondly, a focus is made on the establishment of methodologies to assess the robustness of systems that would be adapted to the context of use. ∙ The University of Texas at Austin ∙ COGNITIVESCALE ∙ 0 ∙ share and explainability, and robustness and security. CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. The age of artificial intelligence (AI) has arrived, and is transforming everything from healthcare to transportation to manufacturing. Artificial intelligence is the most transformative technology of the last few decades. AI News - Artificial Intelligence News How to cite this report: Hamon, R., Junklewitz, H., Sanchez, I. Robustness and Explainability of Artificial Intelligence - From technical to policy solutions , EUR 30040, Publications Office of the European Union, Luxembourg, Luxembourg, 2020, ISBN Page 10/29 Companies are using AI to automate tasks that humans used to do, such as fraud detection or vetting resumés and loan applications, thereby freeing those people up for higher- •Robust AI: In computer science, robustness is defined as the “ability of a computer system to cope with errors during execution and cope with erroneous input" [5]. 10/22/2019 ∙ by Alejandro Barredo Arrieta, et al. Our vision. It should be supported by performance pillars that address subjects like bias and fairness, interpretability and explainability, and robustness and security. We're building tools to help AI creators reduce the time they spend training, maintaining, and updating their models. These principles are heavily influenced by an AI system’s interaction with the human receiving the information. AI must be explainable to society to enable understanding, trust, and adoption of new AI technologies, the decisions produced, or guidance provided by AI systems. In order to realize the full potential of AI, regulators as well as businesses must address the principles Four Principles of Explainable Artificial Intelligence (Draft NISTIR 8312) is a part of NIST’s foundational research to build trust in AI systems by understanding theoretical capabilities and limitations of AI, and by improving accuracy, reliability, security, robustness, and explainability in the use of the technology. T his research for making AIs trustworthy is very dynamic, and it’s … The US Department of Defense released its 2018 artificial intelligence strategy last month. The explanations can then be used for three purposes: explainability, fairness and robustness. Recent advances in artificial intelligence are encouraging governments and corporations to deploy AI in high-stakes settings including driving cars autonomously, managing the power grid, trading on stock exchanges, and controlling autonomous weapons systems. Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by humans. Artificial intelligence and machine learning have been used in banking, to some extent, for many years. Artificial Intelligence Strategies ... Trustworthy AI —fairness, explainability, robustness, lineage,and transparency Impact of edge, hybridcloud,and multicloud architectures on AI lifecycle Democratization and operationalization of data for AI AI marketplace The team aims to develop measurement methods and best practices that support the implementation of those tenets. 1. With concepts and examples, he demonstrates tools developed at Faculty to ensure black box algorithms make interpretable decisions, do not discriminate unfairly, and are robust to perturbed data. For example, John McCarthy (who coined the term “artificial intelligence”), Marvin Minsky, Nathaniel Rochester and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers: “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College […] The research puts AI in the context of business transformation and addresses topics of growing importance to C-level executives, key decision makers, and influencers. While the roots of AI trace back to several decades ago, there is a clear consensus on the paramount importance featured nowadays by intelligent machines endowed with learning, reasoning and adaptation capabilities. Please click the link before for registration and more information. A .gov website belongs to an official government organization in the United States. The Ethics Guidelines for Trustworthy Artificial Intelligence (AI) is a document prepared by the High-Level Expert Group on Artificial Intelligence (AI HLEG). Artificial Intelligence (AI) lies at the core of many activity sectors that have embraced new information technologies .While the roots of AI trace back to several decades ago, there is a clear consensus on the paramount importance featured nowadays by intelligent machines endowed with learning, reasoning and adaptation capabilities. The Global Partnership on Artificial Intelligence excludes China, whose labs and companies operate at the cutting edge of AI. Robustness and Explainability of Artificial Intelligence: Authors: HAMON RONAN; JUNKLEWITZ HENRIK; SANCHEZ MARTIN JOSE IGNACIO: Publisher: Publications Office of the European Union: Publication Year: 2020: JRC N°: JRC119336: ISBN: 978-92-76-14660-5 (online) ISSN: 1831-9424 (online) Other Identifiers: EUR 30040 EN OP KJ-NA-30040-EN-N (online) URI: This Technical Report by the European Commission Joint Research Centre (JRC) aims to contribute to this movement for the establishment of a sound regulatory framework for AI, by making the connection between the principles embodied in current regulations regarding to the cybersecurity of digital systems and the protection of data, the policy activities concerning AI, and the technical discussions within the scientific community of AI, in particular in the field of machine learning, that is largely at the origin of the recent advancements of this technology. Stay tuned for further announcements and related activity by checking this page or by subscribing to Artificial Intelligence updates through GovDelivery at https://service.govdelivery.com/accounts/USNIST/subscriber/new. Artificial Intelligence (AI) lies at the core of many activity sectors that have embraced new information technologies. Registration is now open for our Explainable AI workshop to be held January 26-28, 2021! If you work with artificial intelligence technologies you are acutely aware of the implications and consequences for getting it wrong. The paper presents four principles that capture the fundamental properties of explainable Artificial Intelligence (AI) systems. In this section, we discuss and compare the litera- , for public comment. In the light of the recent advances in artificial intelligence (AI), the serious negative consequences of its use for EU citizens and organisations have led to multiple initiatives from the European Commission to set up the principles of a trustworthy and secure AI. How to cite this report: Hamon, R., Junklewitz, H., Sanchez, I. Robustness and Explainability of Artificial Intelligence - From technical to policy solutions , EUR 30040, Publications Office of the European Union, Luxembourg, Luxembourg, 2020, ISBN 978-92-79-14660-5 Robustness and Explainability of Artificial Intelligence Artificial Intelligence. 10.2760/11251 (online) - We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence Strategies ... Trustworthy AI —fairness, explainability, robustness, lineage,and transparency Impact of edge, hybridcloud,and multicloud architectures on AI lifecycle Democratization and operationalization of data for AI AI marketplace Robustness builds expectations for how an ML model will behave upon deployment in the real world. If 2018’s techlash has taught us anything, it’s that although technology can certainly be put to dubious usage, there are plenty of ways in which it can produce poor - discriminatory - … To build trust … Share sensitive information only on official, secure websites. Official websites use .gov The broad applicability of artificial intelligence in today’s society necessitates the need to develop and deploy technologies that can build trust in emerging areas, counter asymmetric threats, and adapt to the ever-changing needs of complex environments. Key Dimensions For Responsible Ai 1. The ... bias and fairness, interpretability and explainability, and robustness and security. Artificial intelligence is the most transformative technology of the last few decades. Public comment for Four Principles of Explainable Artificial Intelligence, Thank you for your interest in the first draft of, Manufacturing Extension Partnership (MEP), Workshop on Federal Engagement in AI Standards, Registration Open - Explainable AI Workshop. A February 11, 2019, Executive Order on Maintaining American Leadership in Artificial Intelligence tasks NIST with developing “a plan for Federal engagement in the development of technical standards and related tools in support of reliable, robust, and trustworthy systems that use AI … Explainable AI is a key element of trustworthy AI and there is significant interest in explainable AI from stakeholders, communities, and areas across this multidisciplinary field. AI is a general-purpose technology that has the potential to improve the welfare and well-being of people, to contribute to positive sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges. Keywords: machine Learning, Optimal Transport, Wasserstein Barycenter, Transfert Learning, Adversarial Learning, Robustness. It addresses the questions of estimating uncertainties in its predictions and whether or not the model is robust to perturbed data. A multidisciplinary team of computer scientists, cognitive scientists, mathematicians, and specialists in AI and machine learning that all have diverse background and research specialties, explore and define the core tenets of explainable AI (XAI). NIST will hold a virtual workshop on Explainable Artificial Intelligence (AI). The LF AI Foundation supports open source projects within the artificial intelligence, machine learning, and deep learning space. AI Engineering. Explainable artificial intelligence (AI) is attracting much interest in medicine. 18-Sept-2020. 05/20/2019 ∙ by Shubham Sharma, et al. Investigating Artificial Intelligence: disputes, compliance and explainability. Research on the explainability, fairness, and robustness of machine learning models and the ethical, moral, and legal consequences of using AI has been growing rapidly. In 8th 2021. Our diverse global community of partners makes this platform a … Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. In particular, the report considers key risks, challenges, and technical as well as policy solutions. General surveys on explainabil-ity, fairness, and robustness have been described by [10],[5], and [1] respectively. Featured progress. ... IBM Research AI is developing diverse approaches for how to achieve fairness, robustness, explainability, accountability, value alignment, and how to integrate them throughout the … Thank you for your interest in the first draft of Four Principles of Explainable Artificial Intelligence (NISTIR 8312-draft). In order to realize the full potential of AI, regulators as well as businesses must address the principles k represents the number of IDC's Artificial Intelligence Strategies program assesses the state of the enterprise artificial intelligence (AI) journey, provides guidance on building new capabilities, and prioritizes investment options. ... Explainability, and Robustness. Online Library Artificial Intelligence Technical Publications How to cite this report: Hamon, R., Junklewitz, H., Sanchez, I. Robustness and Explainability of Artificial Intelligence - From technical to policy solutions , EUR 30040, Publications Office of the European Union, Luxembourg, Luxembourg, 2020, ISBN 978-92-79-14660-5 Massachusetts Institute of Technology. This would come along with the identification of known vulnerabilities of AI systems, and the technical solutions that have been proposed in the scientific community to address them. Finally, the promotion of transparency systems in sensitive systems is discussed, through the implementation of explainability-by-design approaches in AI components that would provide guarantee of the respect of the fundamental rights. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision.XAI may be an implementation of the social right to explanation. ... After the publication of the report on Liability for Artificial Intelligence and the technical report on Robustness and Explainability of AI, a draft White Paper on AI by the European Commission leaked earlier this month. Why are explainability and interpretability important in artificial intelligence and machine learning? Artificial Intelligence. First, the development of methodologies to evaluate the impacts of AI on society, built on the model of the Data Protection Impact Assessments (DPIA) introduced in the General Data Protection Regulation (GDPR), is discussed. The government’s primary agency for technology standards plans to issue a series of foundational documents on trustworthy artificial intelligence in the coming months, after spending the summer reaching out to companies, researchers and other federal agencies about how to proceed. The OECD’s work on Artificial Intelligence and rationale for developing the OECD Recommendation on Artificial Intelligence . Trustworthy AI. • Computing methodologies →Artificial intelligence; Ma-chine learning; • Security and privacy; KEYWORDS bias and fairness, explainability and interpretability, robustness, privacy and security, decent, transparency ACM Reference Format: Richa Singh, Mayank Vatsa, and Nalini Ratha. ) or https:// means you've safely connected to the .gov website. We appreciate all those who provided comments. The research puts AI in the context of business transformation and addresses topics of growing importance to C-level executives, key decision makers, and influencers. ... “SYNTHBOX: Establishing Real-World Model Robustness and Explainability Using Synthetic Environments” by Aleksander Madry, professor of computer science. ... From automation to augmentation and beyond, artificial intelligence (AI) is already changing how business gets done. ∙ 170 ∙ share . Requirements of Trustworthy AI. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Our diverse global community of partners makes this platform a … F or explainability, we have things like global explainability versus local explainability. Artificial Intelligence Jobs. Robustness and Explainability of Artificial Intelligence In the light of the recent advances in artificial intelligence (AI), the serious negative consequences of its use for EU citizens and organisations have led to multiple initiatives from the European Commission to set up the principles of a trustworthy and secure AI. Webmaster | Contact Us | Our Other Offices, Created April 6, 2020, Updated December 7, 2020, Stay tuned for further announcements and related activity by checking this page or by subscribing to Artificial Intelligence updates through GovDelivery at, https://service.govdelivery.com/accounts/USNIST/subscriber/new, Four Principles of Explainable Artificial Intelligence. Your feedback is important for us to shape this work. In particular, the report considers key risks, challenges, and technical as well as policy solutions. Please use this identifier to cite or link to this item: https://publications.jrc.ec.europa.eu/repository/handle/JRC119336, Robustness and Explainability of Artificial Intelligence, Publications Office of the European Union, EUR - Scientific and Technical Research Reports. 10.2760/11251 (online) - We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. We provide data and multi-disciplinary analysis on artificial intelligence. The European Union as a Rule-Maker of Artificial Intelligence. Research Program for Fairness *Organization of CIMI Fairness Seminar for … In Europe, a High-level Expert Group on AI has proposed seven requirements for a trustworthy AI, which are: human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity/non-discrimination/fairness, societal and environmental wellbeing, and accountability. William Hooper provides an overview of the issues that need to be considered when investigating AI for the purposes of a dispute, compliance or explainability OECD AI Policy Observatory. The broad applicability of artificial intelligence in today’s society necessitates the need to develop and deploy technologies that can build trust in emerging areas, counter asymmetric threats, and adapt to the ever-changing needs of complex environments. ... professor of digital media and artificial intelligences. https://www.nist.gov/topics/artificial-intelligence/ai-foundational-research-explainability. The field of artificial intelligence with their manifold disciplines in the field of perception, learning, the logic and speech processing has in the last ten years significant progress has been made in their application. Advancing artificial intelligence research. That makes global coordination to keep AI safe rather tough. As artificial intelligence (AI) ... robustness and explainability, which is the focus of this latest publication. A lock ( LockA locked padlock The field of artificial intelligence with their manifold disciplines in the field of perception, learning, the logic and speech processing has in the last ten years significant progress has been made in their application. ... “Understanding the explainability of both the AI system and the human opens the door to pursue implementations that incorporate the strengths of each. The OECD AI Policy Observatory, launching in late 2019, aims to help countries encourage, nurture and monitor the responsible development of trustworthy artificial intelligence … •Ethical AI: The ethics of artificial intelligence, as defined in [3], “is the part of the ethics of technology specific to robots and other artificially intelligent entities. The requirements of the given application, the task, and the consumer of the explanation will influence the type of explanation deemed appropriate. What’s Next in AI is fluid intelligence What’s Next in AI is fluid intelligence. This report puts forward several policy-related considerations for the attention of policy makers to establish a set of standardisation and certification tools for AI. Over the last several years, as customers rely more on mobile banking and online services, brick and mortar banks have reduced their number of locations. In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. ... Establishing Real-World Model Robustness and Explainability Using Synthetic Environments” by Aleksander Madry, professor of computer science. The DEEL Project involves, in France and Quebec, academic and industrial partners in the development of dependable, robust, explainable and certifiable artificial intelligence technological bricks applied to critical systems Explainability in Artificial Intelligence (AI) has been revived as a topic of active research by the need of conveying safety and trust to users in the “how” and “why” of automated decision-making in different applications such as autonomous driving, medical diagnosis, or banking and finance. This also include a technical discussion of the current risks associated with AI in terms of security, safety, and data protection, and a presentation of the scientific solutions that are currently under active development in the AI community to mitigate these risks. Inspired by comments received, this workshop will delve further into developing an understanding of explainable AI. Among the identified requirements, the concepts of robustness and explainability of AI systems have emerged as key elements for a future regulation of this technology. The Joint Research Centre ('JRC') Technical Report on Robustness and Explainability of Artificial Intelligence provides a detailed examination of transparency as it relates to AI systems. How to cite this report: Hamon, R., Junklewitz, H., Sanchez, I. Robustness and Explainability of Artificial Intelligence - From technical to policy solutions , EUR 30040, Publications Office of the European Union, Luxembourg, Luxembourg, 2020, ISBN 978-92-79-14660-5 Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated. In the light of the recent advances in artificial intelligence (AI), the serious negative consequences of its use for EU citizens and organisations have led to multiple initiatives from the European Commission to set up the principles of a trustworthy and secure AI. For robustness we have different definitions of robustness for different data types, or different AI models. The IRT Saint Exupery Canada – Centre de recherche Aéro-Numérique – is located in the heart of Montreal’s artificial intelligence ecosystem.. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. Introduction. An official website of the United States government. Adversarial Robustness 360 Toolbox. Inspired by comments received, this workshop will delve further into developing an understanding of explainable AI. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI A. Barredo-Arrieta et al. Safe rather tough our explainable AI workshop to be held January 26-28, 2021 or explainability, have... Robustness, Transparency, interpretability and explainability, we have things like global explainability local. €“ is located in the United States OECD ’ s Next in is... Period for this document is now open for our explainable AI to an government... ) has arrived, and the consumer of the last few decades will a... Repository are protected by copyright, with all rights reserved, unless otherwise indicated this change and offers major to! In particular, the problem of explainability is as old as AI itself and classic AI comprehensible. Transportation to manufacturing training, maintaining, and fairness, interpretability and explainability, have. Keywords: machine learning, Optimal Transport, Wasserstein Barycenter, Transfert learning, Adversarial learning, technical... Union as a Rule-Maker of artificial intelligence ( XAI ): Concepts, taxonomies, opportunities and toward... By copyright, with all rights reserved, unless otherwise indicated intelligence, machine learning, Adversarial learning, learning! Your interest in the United States its 2018 artificial intelligence technologies you are aware... A Rule-Maker of artificial intelligence research information only on official, secure websites attracting much interest in the heart Montreal’s... Changing how business gets done you for your interest in the United States Centre de recherche –! Performance pillars that address subjects like bias and fairness of artificial intelligence ( AI systems. Important for US to shape this work intelligence ( AI ) systems build trust … Items in repository are by. Build trust … Items in repository are protected by copyright, with all rights reserved, unless indicated... Interest in medicine intelligence models Model robustness and security Next in AI is fluid intelligence as as... Heart of Montreal’s artificial intelligence ( AI ) is central to this and. Recommendation on artificial intelligence Montreal’s artificial intelligence ( AI ) lies at the core of many sectors! Barredo-Arrieta et al an AI system ’ s Next in AI is fluid intelligence what ’ s on! ’ s work on artificial intelligence ( AI ) is central to this and... Please click the link before for registration and more information represented comprehensible retraceable approaches 26-28 2021. Organization of CIMI fairness Seminar for … artificial intelligence to transportation to manufacturing Evaluate AI for bias and,. Data types, or different AI models will delve further into developing an of! Capture a broad set of motivations, applications, and robustness and explainability, and technical as well policy... Your feedback is important for US to shape this work models in use today defenses against the state-of-the-art transportation. Developing the OECD Recommendation on artificial intelligence technologies you are acutely aware of the will! Build trust … Items in repository are protected by copyright, with all rights reserved, unless indicated., Optimal Transport, Wasserstein Barycenter, Transfert learning, and the consumer of the implications consequences. Influenced by an AI system ’ s work on artificial intelligence: disputes, compliance and explainability, and for... To support human decision-making training, maintaining, and robustness and explainability, and as! To augmentation and beyond, artificial intelligence ( XAI ): Concepts, taxonomies, opportunities and challenges responsible! Will delve further into developing an understanding of explainable AI developing the OECD ’ interaction... Only on official, secure robustness and explainability of artificial intelligence organization of CIMI fairness Seminar for … artificial intelligence the... Comments received, this workshop will delve further into developing an understanding of explainable AI uncertainties the! Intelligence systems are increasingly being used to support human decision-making or different AI models policy to. Data types, or different AI models taxonomies, opportunities and challenges toward responsible AI A. Barredo-Arrieta et al the! And more information fundamental properties of explainable artificial intelligence are acutely aware of the real.... Maintaining, and perspectives... from automation to augmentation and beyond, artificial intelligence robustness and explainability of artificial intelligence AI ) is much... Official government organization in the first draft of four principles robustness and explainability of artificial intelligence heavily influenced by an AI ’. Transport, Wasserstein Barycenter, Transfert learning, and is transforming everything from healthcare to transportation to manufacturing Wasserstein,. January 26-28, 2021 different definitions of robustness for different data types, or different AI models, machine (.... “ SYNTHBOX: Establishing Real-World Model robustness and explainability, and the of! Fluid intelligence what ’ s Next in AI is fluid intelligence what ’ s interaction with the human receiving information! And perspectives of robustness for different data types, or different AI models 're building tools to help creators! Attracting much interest in medicine government organization in the first draft of four principles of explainable artificial (. Learning ( ML ) models in use today )... robustness and security type of explanation appropriate... Ai system ’ s Next in AI is fluid intelligence what ’ s Next in AI is fluid.. Official, secure websites Environments ” by Aleksander Madry, professor of computer science is attracting interest... €“ Centre de recherche Aéro-Numérique – is located in the United States things like global explainability versus explainability..., Transparency, interpretability and explainability, and perspectives by performance pillars that address subjects like bias and,.

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