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008			210913s2021 sz | s |||| 0|eng d
020			_a9783030768607
024	7		_a10.1007/978-3-030-76860-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ336 _b2021 EB
100	1		_aAgarwal, Sray _eautor _9681532
245	1	0	_aResponsible AI : _bImplementing Ethical and Unbiased Algorithms _cby Sray Agarwal, Shashin Mishra
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XIX, 177 páginas) _b143 ilustraciones, 132 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aIntroduction -- Fairness and proxy features -- Bias in data -- Explainability -- Remove bias from ML model -- Remove bias from ML output -- Accountability in AI -- Data & Model privacy -- Conclusion.
520	3		_aThis book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination. The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter - providing the details that enable the business analysts and the data scientists to implement these fundamentals. AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it. Hands-on approach to ensure easy practical implementation of the concepts discussed Most of the techniques covered are new, with only a few that refer to existing packages. For the techniques covered, the book goes deep into the subject matter and includes code to help the product teams implement these techniques for their products Also addresses the contribution that product owners and the business analysts make to the product being fair and explainable, explaining every topic in detail, including the math involved Covers the end-to-end view of what any software product team needs to do to be able to create a robust, successful and fair AI-driven product Most of the chapters include notes sections throughout to cover the topic in progress for all audiences. Non-technical readers will also benefit by the introductions and conclusions for the book and in each of the chapters.
988			_aSpringer_Computer_2021
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aMishra, Shashin _eautor
776	0	8	_iPrinted edition: _z9783030768591
776	0	8	_iPrinted edition: _z9783030768614
776	0	8	_iPrinted edition: _z9783030769772
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-76860-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eu _zSI