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. Inspired by comments received, this workshop will delve further into developing an understanding of explainable AI. These principles are heavily influenced by an AI system’s interaction with the human receiving the information. 05/20/2019 ∙ by Shubham Sharma, et al. 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 […] Why are explainability and interpretability important in artificial intelligence and machine learning? What’s Next in AI is fluid intelligence What’s Next in AI is fluid intelligence. Explainability tackles the question of … ∙ 170 ∙ share . 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. We're building tools to help AI creators reduce the time they spend training, maintaining, and updating their 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 … Secure .gov websites use HTTPS Research Program for Fairness *Organization of CIMI Fairness Seminar for … A .gov website belongs to an official government organization in the United States. 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. 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. Massachusetts Institute of Technology. Your feedback is important for us to shape this work. • 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. Artificial Intelligence (AI) lies at the core of many activity sectors that have embraced new information technologies. 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). 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. 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. An official website of the United States government. , for public comment. Keywords: machine Learning, Optimal Transport, Wasserstein Barycenter, Transfert Learning, Adversarial Learning, Robustness. Investigating Artificial Intelligence: disputes, compliance and explainability. 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. We provide data and multi-disciplinary analysis on artificial intelligence. OECD AI Policy Observatory. •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]. The comment period for this document is now closed. For robustness we have different definitions of robustness for different data types, or different AI models. Robustness builds expectations for how an ML model will behave upon deployment in the real world. 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. The Global Partnership on Artificial Intelligence excludes China, whose labs and companies operate at the cutting edge of AI. 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. 2. 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. Official websites use .gov 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. We appreciate all those who provided comments. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. Explainable artificial intelligence (AI) is attracting much interest in medicine. 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. The OECD’s work on Artificial Intelligence and rationale for developing the OECD Recommendation on Artificial Intelligence . The LF AI Foundation supports open source projects within the artificial intelligence, machine learning, and deep learning space. 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 explanations can then be used for three purposes: explainability, fairness and robustness. 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 ... From automation to augmentation and beyond, artificial intelligence (AI) is already changing how business gets done. 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. The ... 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. The OECD AI Policy Observatory, launching in late 2019, aims to help countries encourage, nurture and monitor the responsible development of trustworthy artificial intelligence … ... professor of digital media and artificial intelligences. Requirements of Trustworthy AI. https://www.nist.gov/topics/artificial-intelligence/ai-foundational-research-explainability. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. 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. 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. k represents the number of ... Explainability, and Robustness. 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. Adversarial Robustness 360 Toolbox. That makes global coordination to keep AI safe rather tough. 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. 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- 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 it’s … 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. 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. Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated. 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 18-Sept-2020. Our diverse global community of partners makes this platform a … Introduction. Our diverse global community of partners makes this platform a … ... “Understanding the explainability of both the AI system and the human opens the door to pursue implementations that incorporate the strengths of each. 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. 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. CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. 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 … 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. Build trust … Items in repository are protected by copyright, with rights. 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