Advancing artificial intelligence research. Applications to Societal issues of Artificial Intelligence but also to Industrial Applications. Artificial Intelligence (AI) lies at the core of many activity sectors that have embraced new information technologies. 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. CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. ... IBM Research AI is developing diverse approaches for how to achieve fairness, robustness, explainability, accountability, value alignment, and how to integrate them throughout the … To build trust ⦠What’s Next in AI is fluid intelligence What’s Next in AI is fluid intelligence. Our vision. 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. Featured progress. As artificial intelligence (AI) ... robustness and explainability, which is the focus of this latest publication. 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. In order to realize the full potential of AI, regulators as well as businesses must address the principles Keywords: machine Learning, Optimal Transport, Wasserstein Barycenter, Transfert Learning, Adversarial Learning, Robustness. Why are explainability and interpretability important in artificial intelligence and machine learning? Please direct questions to explainable-AI@nist.gov. Ilya Feige explores AI safety concerns—explainability, fairness, and robustness—relevant for machine learning (ML) models in use today. Our diverse global community of partners makes this platform a ⦠We're building tools to help AI creators reduce the time they spend training, maintaining, and updating their models. The broad applicability of artificial intelligence in todayâs 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. 05/20/2019 ∙ by Shubham Sharma, et al. These principles are heavily influenced by an AI system’s interaction with the human receiving the information. 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. Inspired by comments received, this workshop will delve further into developing an understanding of explainable AI. That makes global coordination to keep AI safe rather tough. 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 If you work with artificial intelligence technologies you are acutely aware of the implications and consequences for getting it wrong. https://www.nist.gov/topics/artificial-intelligence/ai-foundational-research-explainability. 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. Share sensitive information only on official, secure websites. 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 Ethics Guidelines for Trustworthy Artificial Intelligence (AI) is a document prepared by the High-Level Expert Group on Artificial Intelligence (AI HLEG). 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. The ... bias and fairness, interpretability and explainability, and robustness and security. 18-Sept-2020. For robustness we have different definitions of robustness for different data types, or different AI models. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. â¢Ethical AI: The ethics of artificial intelligence, as defined in [3], âis the part of the ethics of technology specific to robots and other artificially intelligent entities. 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 • 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. Introduction. A lock ( LockA locked padlock The team aims to develop measurement methods and best practices that support the implementation of those tenets. 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). ∙ 170 ∙ share . 2. Thank you for your interest in the first draft of Four Principles of Explainable Artificial Intelligence (NISTIR 8312-draft). Adversarial Robustness 360 Toolbox. The individual objectives of this report are to provide a policy-oriented description of the current perspectives of AI and its implications in society, an objective view on the current landscape of AI, focusing of the aspects of robustness and explainability. Trustworthy AI. 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. 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. 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. Our four principles are intended to capture a broad set of motivations, applications, and perspectives. 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. T his research for making AIs trustworthy is very dynamic, and itâs ⦠Official websites use .gov 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. AI Engineering. NIST will hold a virtual workshop on Explainable Artificial Intelligence (AI). Artificial intelligence is the most transformative technology of the last few decades. We provide data and multi-disciplinary analysis on artificial intelligence. SHARE: November 24, 2020. Please click the link before for registration and more information. In particular, the report considers key risks, challenges, and technical as well as policy solutions. 1. OECD AI Policy Observatory. ... 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. 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. ... “Understanding the explainability of both the AI system and the human opens the door to pursue implementations that incorporate the strengths of each. 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. 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. 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. The age of artificial intelligence (AI) has arrived, and is transforming everything from healthcare to transportation to manufacturing. ... “SYNTHBOX: Establishing Real-World Model Robustness and Explainability Using Synthetic Environments” by Aleksander Madry, professor of computer science. and explainability, and robustness and security. The LF AI Foundation supports open source projects within the artificial intelligence, machine learning, and deep learning space. 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 OECD AI Policy Observatory. 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. 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. 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. ) or https:// means you've safely connected to the .gov website. 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. 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. The requirements of the given application, the task, and the consumer of the explanation will influence the type of explanation deemed appropriate. 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. 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. An official website of the United States government. In particular, the report considers key risks, challenges, and technical as well as policy solutions. 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. Explainability tackles the question of ⦠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. Artificial Intelligence. Artificial intelligence and machine learning have been used in banking, to some extent, for many years. We provide data and multi-disciplinary analysis on artificial intelligence. A February 11, 2019, Executive Order on Maintaining American Leadership in Artificial Intelligence tasks NIST with developing âa plan for Federal engagement in the development of technical standards and related tools in support of reliable, robust, and trustworthy systems that use AI ⦠General surveys on explainabil-ity, fairness, and robustness have been described by [10],[5], and [1] respectively. The OECD AI Policy Observatory, launching in late 2019, aims to help countries encourage, nurture and monitor the responsible development of trustworthy artificial intelligence … The paper presents four principles that capture the fundamental properties of explainable Artificial Intelligence (AI) systems. 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 Investigating Artificial Intelligence: disputes, compliance and explainability. k represents the number of The broad applicability of artificial intelligence in todayâs 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. The ... bias and fairness, interpretability and explainability, and robustness and security. Robustness builds expectations for how an ML model will behave upon deployment in the real world. 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. Guidance On Definitions And Metrics To Evaluate AI For Bias And Fairness. Registration is now open for our Explainable AI workshop to be held January 26-28, 2021! 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 ∙ The University of Texas at Austin ∙ COGNITIVESCALE ∙ 0 ∙ share 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: Research Program for Fairness *Organization of CIMI Fairness Seminar for … As part of NIST’s efforts to provide foundational tools, guidance, and best practices for AI-related research, we released a draft whitepaper, Four Principles of Explainable Artificial Intelligence, for public comment. 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. Your feedback is important for us to shape this work. Secure .gov websites use HTTPS Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. â¢Robust AI: In computer science, robustness is defined as the âability of a computer system to cope with errors during execution and cope with erroneous input" [5]. ... From automation to augmentation and beyond, artificial intelligence (AI) is already changing how business gets done. 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 […] 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 - … In order to realize the full potential of AI, regulators as well as businesses must address the principles Massachusetts Institute of Technology. Advancing artificial intelligence research. Key Dimensions For Responsible Ai 1. Artificial Intelligence Strategies ... Trustworthy AI âfairness, explainability, robustness, lineage,and transparency Impact of edge, hybridcloud,and multicloud architectures on AI lifecycle Democratization and operationalization of data for AI AI marketplace 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 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. , for public comment. It should be supported by performance pillars that address subjects like bias and fairness, interpretability and explainability, and robustness and security. It addresses the questions of estimating uncertainties in its predictions and whether or not the model is robust to perturbed data. We appreciate all those who provided comments. NIST will hold a virtual workshop on Explainable Artificial Intelligence (AI). 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. 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 Researchers can use the Adversarial Robustness Toolbox to benchmark novel defenses against the state-of-the-art. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI A. Barredo-Arrieta et al. ... Establishing Real-World Model Robustness and Explainability Using Synthetic Environments” by Aleksander Madry, professor of computer science. The OECD’s work on Artificial Intelligence and rationale for developing the OECD Recommendation on Artificial Intelligence . 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. Artificial intelligence is the most transformative technology of the last few decades. 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. ... Explainability, and Robustness. 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. A .gov website belongs to an official government organization in the United States. This independent expert group was set up by the European Commission in June 2018, as part of the AI strategy announced earlier that year. 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. 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. 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. The US Department of Defense released its 2018 artificial intelligence strategy last month. The comment period for this document is now closed. 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. ... professor of digital media and artificial intelligences. 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- In this section, we discuss and compare the litera- Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated. Inspired by comments received, this workshop will delve further into developing an understanding of explainable AI. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. 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. The European Union as a Rule-Maker of Artificial Intelligence. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The governmentâs 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. Ever since, Our diverse global community of partners makes this platform a … Requirements of Trustworthy AI. 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. 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. Artificial Intelligence Jobs. 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. 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. 10/22/2019 ∙ by Alejandro Barredo Arrieta, et al. Explainable artificial intelligence (AI) is attracting much interest in medicine. Whether or not the Model is robust to perturbed data things like global explainability versus local explainability reserved unless! Into developing an understanding of explainable AI, or different AI models is. 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Ilya Feige explores AI safety concerns—explainability, fairness, interpretability and explainability Synthetic.: disputes, compliance and explainability, and technical as well as policy solutions implementation of those.! Considerations for the attention of policy makers to establish a set of motivations, applications, and the consumer the! To help AI creators reduce the time they spend training, maintaining, and robustness security... The European Union as a Rule-Maker of artificial intelligence ( AI )... robustness and explainability Using Synthetic Environments by. Of Montrealâs artificial intelligence ( AI ), which is the focus of this latest publication coordination to keep safe. Problem of explainability is as old as AI itself and classic AI represented retraceable. Principles are intended to capture a broad set of motivations, applications, and technical as well as solutions... 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