Ecological debt [[electronic resource] ] : global warming and the wealth of nations / / Andrew Simms

• Record Nr.: UNINA9910456472103321
• Autore: Simms Andrew
• Pubbl/distr/stampa: London, : Pluto Press, 2009
• ISBN: 1-78371-059-4; 1-84964-377-6
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (336 p.)
• Disciplina: 333.7
• Soggetti: Environmental economics; Environmental protection - Citizen participation; Consumption (Economics) - Environmental aspects; Global warming; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references (p. 190-204) and index.
• Sommario/riassunto: --New edition of this highly acclaimed guide-- 'Creative and compelling.' Guardian'Essential reading.' Head of the IPCC 'A new phrase has entered the language.' Anita RoddickThis is the second edition of Andrew Simms's highly regarded guide to ecological

Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent / / edited by Jianlong Zhou, Fang Chen

• Record Nr.: UNINA9910299284403321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
• ISBN: 3-319-90403-5
• Edizione: [1st ed. 2018.]
• Descrizione fisica: 1 online resource (XXIII, 482 p. 140 illus., 114 illus. in color.)
• Collana: Human–Computer Interaction Series, , 2524-4477
• Disciplina: 006.31
• Soggetti: User interfaces (Computer systems); Human-computer interaction; Artificial intelligence; Pattern recognition systems; User Interfaces and Human Computer Interaction; Artificial Intelligence; Automated Pattern Recognition
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Part I Transparency in Machine Learning -- Part II Visual Explanation of Machine Learning Process -- Part III Algorithmic Explanation of Machine Learning Models -- Part IV User Cognitive Responses in ML-Based Decision Making -- Part V Human and Evaluation of Machine Learning -- Part VI Domain Knowledge in Transparent Machine Learning Applications.
• Sommario/riassunto: With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML ; processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.