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1. |
Record Nr. |
UNINA9910254115303321 |
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Autore |
Correia Dantas Eustogio Wanderley |
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Titolo |
Coastal Geography in Northeast Brazil : Analyzing Maritimity in the Tropics / / by Eustogio Wanderley Correia Dantas |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (77 p.) |
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Collana |
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SpringerBriefs in Latin American Studies, , 2366-763X |
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Disciplina |
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Soggetti |
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Cultural geography |
Urban geography |
Tourism |
Management |
Cultural Geography |
Urban Geography / Urbanism (inc. megacities, cities, towns) |
Tourism Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Chapter 1. Modern Maritime Practices In The Tropics -- Chapter 2. Tropical Coastal Maritime Cities.- Chapter 3. Tourism Development Policies In The Brazilian Northeast -- Chapter 4 -- Tropism, The Biggest Myth Of Tourism In The Tropics -- Chapter 5. Final Considerations. |
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Sommario/riassunto |
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This book studies the transformation of modern maritimity practices in coastal areas (such as swimming, navigation and tourism) and their implications to the development of Brazilian coastal cities, with an emphasis on the Northeast part of the country. It is a reflection on coastal geography in the tropics and the contemporary valorization of coastal cities from a socioeconomic, technological and symbolical point of view. The book highlights local fluxes on a regional and local scale, showing the incorporation of beach zones to spaces which were previously associated with so called traditional coastal practices (fishing activities and as harboring points). This book is dedicated to geography researchers and students. |
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2. |
Record Nr. |
UNINA9910741137403321 |
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Autore |
Duke Toju |
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Titolo |
Building Responsible AI Algorithms : A Framework for Transparency, Fairness, Safety, Privacy, and Robustness / / by Toju Duke |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (196 pages) |
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Disciplina |
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Soggetti |
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Machine learning |
Technology - Moral and ethical aspects |
Artificial intelligence |
Machine Learning |
Ethics of Technology |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Part I. Foundation -- 1. Responsibility -- 2. AI Principles -- 3. Data -- Part II. Implementation -- 4. Fairness -- 5. Safety -- 6. Humans in the Loop -- 7. Explainability -- 8. Privacy -- 9. Robustness -- Part III. Ethical Considerations -- 10. Ethics of AI and ML -- Appendix A: References. |
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Sommario/riassunto |
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This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of |
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responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will Learn Build AI/ML models using Responsible AI frameworks and processes Document information on your datasets and improve data quality Measure fairness metrics in ML models Identify harms and risks per task and run safety evaluations on ML models Create transparent AI/ML models Develop Responsible AI principles and organizational guidelines. |
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