000			04643nam a22006135i 4500
001			978-3-030-76860-7
003			DE-He213
005			20240423125304.0
007			cr nn 008mamaa
008			210913s2021 sz | s |||| 0|eng d
020			_a9783030768607 _9978-3-030-76860-7
024	7		_a10.1007/978-3-030-76860-7 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aAgarwal, Sray. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aResponsible AI _h[electronic resource] : _bImplementing Ethical and Unbiased Algorithms / _cby Sray Agarwal, Shashin Mishra.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aXIX, 177 p. 143 illus., 132 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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			_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.
650		0	_aArtificial intelligence.
650		0	_aMachine learning.
650		0	_aTechnology _xMoral and ethical aspects.
650		0	_aComputers and civilization.
650		0	_aArtificial intelligence _xData processing.
650		0	_aComputers.
650	1	4	_aArtificial Intelligence.
650	2	4	_aMachine Learning.
650	2	4	_aEthics of Technology.
650	2	4	_aComputers and Society.
650	2	4	_aData Science.
650	2	4	_aComputing Milieux.
700	1		_aMishra, Shashin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030768591
776	0	8	_iPrinted edition: _z9783030768614
776	0	8	_iPrinted edition: _z9783030769772
856	4	0	_uhttps://doi.org/10.1007/978-3-030-76860-7
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c176328 _d176328