Bibliografías temáticas / Fair Machine Learning

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Literatura académica sobre el tema "Fair Machine Learning"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Fair Machine Learning"

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Basu Roy Chowdhury, Somnath, and Snigdha Chaturvedi. "Sustaining Fairness via Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6797–805. http://dx.doi.org/10.1609/aaai.v37i6.25833.

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Machine learning systems are often deployed for making critical decisions like credit lending, hiring, etc. While making decisions, such systems often encode the user's demographic information (like gender, age) in their intermediate representations. This can lead to decisions that are biased towards specific demographics. Prior work has focused on debiasing intermediate representations to ensure fair decisions. However, these approaches fail to remain fair with changes in the task or demographic distribution. To ensure fairness in the wild, it is important for a system to adapt to such change
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Perello, Nick, and Przemyslaw Grabowicz. "Fair Machine Learning Post Affirmative Action." ACM SIGCAS Computers and Society 52, no. 2 (2023): 22. http://dx.doi.org/10.1145/3656021.3656029.

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The U.S. Supreme Court, in a 6-3 decision on June 29, effectively ended the use of race in college admissions [1]. Indeed, national polls found that a plurality of Americans - 42%, according to a poll conducted by the University of Massachusetts [2] - agree that the policy should be discontinued, while 33% support its continued use in admissions decisions. As scholars of fair machine learning, we ponder how the Supreme Court decision shifts points of focus in the field. The most popular fair machine learning methods aim to achieve some form of "impact parity" by diminishing or removing the cor
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Rance, Joseph, and Filip Svoboda. "Can Private Machine Learning Be Fair?" Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20121–29. https://doi.org/10.1609/aaai.v39i19.34216.

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We show that current SOTA methods for privately and fairly training models are unreliable in many practical scenarios. Specifically, we (1) introduce a new type of adversarial attack that seeks to introduce unfairness into private model training, and (2) demonstrate that the use of methods for training on private data that are robust to adversarial attacks often leads to unfair models, regardless of the use of fairness-enhancing training methods. This leads to a dilemma when attempting to train fair models on private data: either (A) we use a robust training method which may introduce unfairne
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Oneto, Luca. "Learning fair models and representations." Intelligenza Artificiale 14, no. 1 (2020): 151–78. http://dx.doi.org/10.3233/ia-190034.

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Machine learning based systems and products are reaching society at large in many aspects of everyday life, including financial lending, online advertising, pretrial and immigration detention, child maltreatment screening, health care, social services, and education. This phenomenon has been accompanied by an increase in concern about the ethical issues that may rise from the adoption of these technologies. In response to this concern, a new area of machine learning has recently emerged that studies how to address disparate treatment caused by algorithmic errors and bias in the data. The centr
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Kim, Yun-Myung. "Data and Fair use." Korea Copyright Commission 141 (March 30, 2023): 5–53. http://dx.doi.org/10.30582/kdps.2023.36.1.5.

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Data collection and use are the beginning and end of machine learning. Looking at ChatGPT, data is making machines comparable to human capabilities. Commercial purposes are not naturally rejected in the judgment of fair use of the process of producing or securing data for system learning. The UK, Germany, and the EU are also introducing copyright restrictions for data mining for non-profit purposes such as research studies, and Japan is more active. Japan’s active legislation is the reason why there are no comprehensive fair use regulations like Korea and the United States, but it shows its wi
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Kim, Yun-Myung. "Data and Fair use." Korea Copyright Commission 141 (March 30, 2023): 5–53. http://dx.doi.org/10.30582/kdps.2023.36.1.5.

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Data collection and use are the beginning and end of machine learning. Looking at ChatGPT, data is making machines comparable to human capabilities. Commercial purposes are not naturally rejected in the judgment of fair use of the process of producing or securing data for system learning. The UK, Germany, and the EU are also introducing copyright restrictions for data mining for non-profit purposes such as research studies, and Japan is more active. Japan’s active legislation is the reason why there are no comprehensive fair use regulations like Korea and the United States, but it shows its wi
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Zhang, Xueru, Mohammad Mahdi Khalili, and Mingyan Liu. "Long-Term Impacts of Fair Machine Learning." Ergonomics in Design: The Quarterly of Human Factors Applications 28, no. 3 (2019): 7–11. http://dx.doi.org/10.1177/1064804619884160.

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Machine learning models developed from real-world data can inherit potential, preexisting bias in the dataset. When these models are used to inform decisions involving human beings, fairness concerns inevitably arise. Imposing certain fairness constraints in the training of models can be effective only if appropriate criteria are applied. However, a fairness criterion can be defined/assessed only when the interaction between the decisions and the underlying population is well understood. We introduce two feedback models describing how people react when receiving machine-aided decisions and ill
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Zhu, Yunlan. "The Comparative Analysis of Fair Use of Works in Machine Learning." SHS Web of Conferences 178 (2023): 01015. http://dx.doi.org/10.1051/shsconf/202317801015.

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Before generative AI outputs the content, it copies a large amount of text content. This process is machine learning. For the development of artificial intelligence technology and cultural prosperity, many countries have included machine learning within the scope of fair use. However, China’s copyright law currently does not legislate the fair use of machine learning works. This paper will construct a Chinese model of fair use of machine learning works through comparative analysis of the legislation of other countries. This is a fair use model that balances the flexibility of the United States
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JEONG, JIN KEUN. "Will the U.S. Court Judge TDM for Artificial Intelligence Machine Learning as Fair Use?" Korea Copyright Commission 144 (December 31, 2023): 215–50. http://dx.doi.org/10.30582/kdps.2023.36.4.215.

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A representative debate is whether TDM (Text and Data Mining) in the machine learning process, which occurs when AI uses other people’s copyrighted works by unauthorized means such as copying, is in accordance with the fair use principle or not. The issue is whether one can be exempted from copyright infringement.
 In this regard, Korean scholar’s attitude starts from the optimistic perspective that U.S. courts will view AI TDM or AI machine learning as fair use based on the fair use principle.
 Nevertheless, there is no direct basis for the claim that US courts will exempt AI TDM or
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Redko, Ievgen, and Charlotte Laclau. "On Fair Cost Sharing Games in Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4790–97. http://dx.doi.org/10.1609/aaai.v33i01.33014790.

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Machine learning and game theory are known to exhibit a very strong link as they mutually provide each other with solutions and models allowing to study and analyze the optimal behaviour of a set of agents. In this paper, we take a closer look at a special class of games, known as fair cost sharing games, from a machine learning perspective. We show that this particular kind of games, where agents can choose between selfish behaviour and cooperation with shared costs, has a natural link to several machine learning scenarios including collaborative learning with homogeneous and heterogeneous so
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Tesis sobre el tema "Fair Machine Learning"

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Vo, Thi Quynh Trang. "Algorithms and Machine Learning for fair and classical combinatorial optimization." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0035.

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L'optimisation combinatoire est un domaine des mathématiques dans lequel un problème consiste à trouver une solution optimale dans un ensemble fini d'objets. Elle a des applications cruciales dans de nombreux domaines. Le branch-and-cut est l'un des algorithmes les plus utilisés pour résoudre exactement des problèmes d'optimisation combinatoire. Dans cette thèse, nous nous concentrons sur les aspects informatiques du branch-and-cut et plus particulièrement, sur deux aspects importants de l'optimisation combinatoire: l'équité des solutions et l'intégration de l'apprentissage automatique. Dans l
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Schildt, Alexandra, and Jenny Luo. "Tools and Methods for Companies to Build Transparent and Fair Machine Learning Systems." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279659.

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AI has quickly grown from being a vast concept to an emerging technology that many companies are looking to integrate into their businesses, generally considered an ongoing “revolution” transforming science and society altogether. Researchers and organizations agree that AI and the recent rapid developments in machine learning carry huge potential benefits. At the same time, there is an increasing worry that ethical challenges are not being addressed in the design and implementation of AI systems. As a result, AI has sparked a debate about what principles and values should guide its developmen
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Dalgren, Anton, and Ylva Lundegård. "GreenML : A methodology for fair evaluation of machine learning algorithms with respect to resource consumption." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159837.

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Impressive results can be achieved when stacking deep neural networks hierarchies together. Several machine learning papers claim state-of-the-art results when evaluating their models with different accuracy metrics. However, these models come at a cost, which is rarely taken into consideration. This thesis aims to shed light on the resource consumption of machine learning algorithms, and therefore, five efficiency metrics are proposed. These should be used for evaluating machine learning models, taking accuracy, model size, and time and energy consumption for both training and inference into
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Gordaliza, Pastor Paula. "Fair learning : une approche basée sur le transport optimale." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30084.

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L'objectif de cette thèse est double. D'une part, les méthodes de transport optimal sont étudiées pour l'inférence statistique. D'autre part, le récent problème de l'apprentissage équitable est considéré avec des contributions à travers le prisme de la théorie du transport optimal. L'utilisation généralisée des applications basées sur les modèles d'apprentissage automatique dans la vie quotidienne et le monde professionnel s'est accompagnée de préoccupations quant aux questions éthiques qui peuvent découler de l'adoption de ces technologies. Dans la première partie de cette thèse, nous motivon
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Grari, Vincent. "Adversarial mitigation to reduce unwanted biases in machine learning." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS096.

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Ces dernières années, on a assisté à une augmentation spectaculaire de l’intérêt académique et sociétal pour l’apprentissage automatique équitable. En conséquence, des travaux significatifs ont été réalisés pour inclure des contraintes d’équité dans les algorithmes d’apprentissage automatique. Le but principal est de s’assurer que les prédictions des modèles ne dépendent d’aucun attribut sensible comme le genre ou l’origine d’une personne par exemple. Bien que cette notion d’indépendance soit incontestable dans un contexte général, elle peut théoriquement être définie de manière totalement dif
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Berisha, Visar. "AI as a Threat to Democracy : Towards an Empirically Grounded Theory." Thesis, Uppsala universitet, Statsvetenskapliga institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-340733.

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Artificial intelligence has in recent years taken center stage in the technological development. Major corporations, operating in a variety of economic sectors, are investing heavily in AI in order to stay competitive in the years and decades to come. What differentiates this technology from traditional computing is that it can carry out tasks previously limited to humans. As such it contains the possibility to revolutionize every aspect of our society. Until now, social science has not given the proper attention that this emerging technological phenomena deserves, a phenomena which, according
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Sitruk, Jonathan. "Fais Ce Qu'il Te Plaît... Mais Fais Le Comme Je L'aime : Amélioration des performances en crowdfunding par l’utilisation des catégories et des récits." Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR0018.

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Cette thèse vise à fournir aux entrepreneurs une meilleure compréhension de la façon d'améliorer leur performance lors de la collecte de fonds auprès d’investisseurs. Les entrepreneurs ont des difficultés notoires à accéder aux ressources financières et au capital parce qu'ils souffrent d'un aléa de la nouveauté. Cette condition inhérente est due à leur manque de légitimité dans leur marché cible et conduit les investisseurs à les considérer comme intrinsèquement risqués. Les moyens de financement des entrepreneurs ont traditionnellement été l'épargne personnelle, la famille et les amis, les b
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Muriithi, Paul Mutuanyingi. "A case for memory enhancement : ethical, social, legal, and policy implications for enhancing the memory." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/a-case-for-memory-enhancement-ethical-social-legal-and-policy-implications-for-enhancing-the-memory(bf11d09d-6326-49d2-8ef3-a40340471acf).html.

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The desire to enhance and make ourselves better is not a new one and it has continued to intrigue throughout the ages. Individuals have continued to seek ways to improve and enhance their well-being for example through nutrition, physical exercise, education and so on. Crucial to this improvement of their well-being is improving their ability to remember. Hence, people interested in improving their well-being, are often interested in memory as well. The rationale being that memory is crucial to our well-being. The desire to improve one’s memory then is almost certainly as old as the desire to
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Azami, Sajjad. "Exploring fair machine learning in sequential prediction and supervised learning." Thesis, 2020. http://hdl.handle.net/1828/12098.

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Algorithms that are being used in sensitive contexts such as deciding to give a job offer or giving inmates parole should be accurate as well as being non-discriminatory. The latter is important especially due to emerging concerns about automatic decision making being unfair to individuals belonging to certain groups. The machine learning literature has seen a rapid evolution in research on this topic. In this thesis, we study various problems in sequential decision making motivated by challenges in algorithmic fairness. As part of this thesis, we modify the fundamental framework of prediction
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Allabadi, Swati. "Algorithms for Fair Clustering." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5709.

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Many decisions today are taken by various machine learning algorithms, hence it is crucial to accommodate fairness in such algorithms to remove/reduce any kind of bias in the decision. We incorporate fairness in the problem of clustering. Clustering is a classical machine learning problem in which the task is to partition the data points into various groups such that the data points belonging to one group are more similar to each other than the data points belonging to some other group in the partition. In our model, each data point belongs to one or more number of categories. We define
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Libros sobre el tema "Fair Machine Learning"

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Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines. O'Reilly Media, Incorporated, 2022.

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Drago Plečko and Elias Bareinboim. Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning. Now Publishers, 2024.

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Chatterjee, Sharmistha. Platform and Model Design for Responsible AI: Design and Build Resilient, Private, Fair, and Transparent Machine Learning Models. Packt Publishing, Limited, 2023.

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Chatterjee, Sharmistha. Platform and Model Design for Responsible AI: Design and Build Resilient, Private, Fair, and Transparent Machine Learning Models. de Gruyter GmbH, Walter, 2023.

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Molak, Aleksander. Interpretable Machine Learning with Python: Build Explainable, Fair, and Robust High-Performance Models with Hands-on, Real-world Examples. de Gruyter GmbH, Walter, 2023.

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Masis, Serg. Interpretable Machine Learning with Python: Build Explainable, Fair, and Robust High-Performance Models with Hands-On, Real-World Examples. Packt Publishing, Limited, 2022.

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Vallor, Shannon, and George A. Bekey. Artificial Intelligence and the Ethics of Self-Learning Robots. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190652951.003.0022.

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The convergence of robotics technology with the science of artificial intelligence is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors. Recent advances in machine learning techniques have produced artificial agents that can acquire highly complex skills formerly thought to be the exclusive province of human intelligence. These developments raise a host of new ethical concerns about the responsible design, manufacture, and use of robots enabled with artificial intelligence—particularly those equipped with self-learning capacities. While the pot
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Capítulos de libros sobre el tema "Fair Machine Learning"

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Pérez-Suay, Adrián, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, and Gustau Camps-Valls. "Fair Kernel Learning." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71249-9_21.

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Freitas, Alex, and James Brookhouse. "Evolutionary Algorithms for Fair Machine Learning." In Handbook of Evolutionary Machine Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3814-8_17.

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Van, Minh-Hao, Wei Du, Xintao Wu, and Aidong Lu. "Poisoning Attacks on Fair Machine Learning." In Database Systems for Advanced Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00123-9_30.

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Van, Minh-Hao, Wei Du, Xintao Wu, and Aidong Lu. "Poisoning Attacks on Fair Machine Learning." In Database Systems for Advanced Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00123-9_30.

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Wu, Yongkai, Lu Zhang, and Xintao Wu. "Fair Machine Learning Through the Lens of Causality." In Machine Learning for Causal Inference. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35051-1_6.

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Abdollahi, Behnoush, and Olfa Nasraoui. "Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems." In Human and Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90403-0_2.

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Hridi, Anurata Prabha, and Benjamin Watson. "Are Fair Machine Learning Models More Useful?" In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76827-9_3.

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Lappas, Theodoros, and Evimaria Terzi. "Toward a Fair Review-Management System." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23783-6_19.

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Zhang, Mingwu, Xiao Chen, Gang Shen, and Yong Ding. "A Fair and Efficient Secret Sharing Scheme Based on Cloud Assisting." In Machine Learning for Cyber Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30619-9_25.

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Rančić, Sanja, Sandro Radovanović, and Boris Delibašić. "Investigating Oversampling Techniques for Fair Machine Learning Models." In Lecture Notes in Business Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73976-8_9.

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Actas de conferencias sobre el tema "Fair Machine Learning"

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Mitra, Purbesh, and Sennur Ulukus. "A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks." In 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN). IEEE, 2024. http://dx.doi.org/10.1109/icmlcn59089.2024.10624766.

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Priyadarshini, Amisha, and Sergio Gago-Masague. "Fair Evaluator: An Adversarial Debiasing-based Deep Learning Framework in Student Admissions." In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2024. https://doi.org/10.1109/cogmi62246.2024.00029.

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Oikonomou, Foteini, Eleftherios Bailis, Sotiris Bentos, et al. "Towards Fair Recidivism Prediction: Addressing Bias in Machine Learning for the Greek Prison System." In 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025. https://doi.org/10.1109/iraset64571.2025.11008007.

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Zhai, Fuqiang. "Training Method for a Fair Machine Learning Model Based on a Multi-Objective Evolutionary Algorithm." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673611.

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V, Malavika, Nikita Mabel M, Chantelle I. Dmonte, Mohammed Zaid, and Lenish Pramiee J. "Fair Price Prediction for Farmers: Leveraging Freshness of Perishable Goods Through Machine Learning and Sustainable Practices." In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025. https://doi.org/10.1109/csnt64827.2025.10968584.

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Perrier, Elija. "Quantum Fair Machine Learning." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. ACM, 2021. http://dx.doi.org/10.1145/3461702.3462611.

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Kearns, Michael. "Fair Algorithms for Machine Learning." In EC '17: ACM Conference on Economics and Computation. ACM, 2017. http://dx.doi.org/10.1145/3033274.3084096.

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Dai, Jessica, Sina Fazelpour, and Zachary Lipton. "Fair Machine Learning Under Partial Compliance." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. ACM, 2021. http://dx.doi.org/10.1145/3461702.3462521.

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Liu, Lydia T., Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. "Delayed Impact of Fair Machine Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/862.

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Static classification has been the predominant focus of the study of fairness in machine learning. While most models do not consider how decisions change populations over time, it is conventional wisdom that fairness criteria promote the long-term well-being of groups they aim to protect. This work studies the interaction of static fairness criteria with temporal indicators of well-being. We show a simple one-step feedback model in which common criteria do not generally promote improvement over time, and may in fact cause harm. Our results highlight the importance of temporal modeling in the e
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Wang, Haoyu, Hanyu Hu, Mingrui Zhuang, and Jiayi Shen. "Integrating Machine Learning into Fair Inference." In The International Conference on New Media Development and Modernized Education. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011908000003613.

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Informes sobre el tema "Fair Machine Learning"

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Strinzel, Michaela, Gabriel Okasa, Anne Jorstad, et al. Data Management Plan (DMP): A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports. Swiss National Science Foundation, 2024. http://dx.doi.org/10.46446/dmp-peer-review-assessment-ml.

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The Data Management Plan for the study “A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports” by a research team at the University College Dublin in collaboration with the Swiss National Science outlines the collection, processing, sharing and storage of the data used and generated in the study, following the FAIR Data principles.
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Nickerson, Jeffrey, Kalle Lyytinen, and John L. King. Automated Vehicles: A Human/Machine Co-learning Perspective. SAE International, 2022. http://dx.doi.org/10.4271/epr2022009.

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Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are
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Busch, Ella, and Jacob Ware. The Weaponization of Deepfakes: Digital Deception on the Far-Right. ICCT, 2023. http://dx.doi.org/10.19165/2023.2.07.

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In an ever-evolving technological landscape, digital disinformation is on the rise, as are its political consequences. In this paper, we explore the creation and distribution of synthetic media by malign actors, specifically a form of artificial intelligence-machine learning (AI/ML) known as the deepfake. Individuals looking to incite political violence are increasingly turning to deepfakes–specifically deepfake video content–in order to create unrest, undermine trust in democratic institutions and authority figures, and elevate polarised political agendas. We present a new subset of individua
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Adegoke, Damilola, Natasha Chilambo, Adeoti Dipeolu, Ibrahim Machina, Ade Obafemi-Olopade, and Dolapo Yusuf. Public discourses and Engagement on Governance of Covid-19 in Ekiti State, Nigeria. African Leadership Center, King's College London, 2021. http://dx.doi.org/10.47697/lab.202101.

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Numerous studies have emerged so far on Covid-19 (SARS-CoV-2) across different disciplines. There is virtually no facet of human experience and relationships that have not been studied. In Nigeria, these studies include knowledge and attitude, risk perception, public perception of Covid-19 management, e-learning, palliatives, precautionary behaviours etc.,, Studies have also been carried out on public framing of Covid-19 discourses in Nigeria; these have explored both offline and online messaging and issues from the perspectives of citizens towards government’s policy responses such as palliat
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5

Belcher, Stephen. Weather and climate science services in a changing world: research and innovation strategy. Met Office, 2022. https://doi.org/10.62998/crmi4887.

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Weather and climate science and services have never been more important. The risks from high-impact weather events, and how they might change in our changing climate, rank high in many national and corporate risk registers. Better forecasts, with longer lead times, tailored to impacts help to minimise the damage and realise the opportunities. At the same time enabling technology is changing at an ever-increasing pace: novel supercomputers promise enormous power if harnessed effectively; public sector cloud-based technology offers profoundly new ways of analysing data; and data sciences and art
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