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AI Fairness: Designing Equal Opportunity Algorithms by Leben
US $78,99
CircaEUR 67,85
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“Used book in good condition. Shows typical wear. Quick shipping. Satisfaction guaranteed!”
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- Compralo SubitoAI Fairness: Designing Equal Opportunity Algorithms by Leben
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Spedizione:
US $6,99 (circa EUR 6,00) USPS Media MailTM.
Oggetto che si trova a: Multiple Locations, Stati Uniti
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Consegna prevista tra il sab 30 ago e il sab 6 set a 94104
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Numero oggetto eBay:146739387798
Specifiche dell'oggetto
- Condizione
- Buone condizioni
- Note del venditore
- “Used book in good condition. Shows typical wear. Quick shipping. Satisfaction guaranteed!”
- Narrative Type
- Intelligence & Semantics
- Type
- Book
- Intended Audience
- N/A
- ISBN
- 9780262552363
Informazioni su questo prodotto
Product Identifiers
Publisher
MIT Press
ISBN-10
0262552361
ISBN-13
9780262552363
eBay Product ID (ePID)
27070933181
Product Key Features
Book Title
Ai Fairness : Designing Equal Opportunity Algorithms
Number of Pages
240 Pages
Language
English
Publication Year
2025
Topic
Programming / Algorithms, Ethics & Moral Philosophy, Intelligence (Ai) & Semantics, General
Illustrator
Yes
Genre
Computers, Philosophy, Science
Format
Trade Paperback
Dimensions
Item Height
0.6 in
Item Weight
10.2 Oz
Item Length
9 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Trade
LCCN
2024-023740
Dewey Edition
23
Dewey Decimal
006.3019
Table Of Content
Introduction Chapter 1. The Problem Chapter 2. Fairness Chapter 3. AI Chapter 4. A Theory of Algorithmic Justice Chapter 5. Equal Treatment Chapter 6. Relevance Chapter 7. Equal Impact Chapter 8. Prices and Wages Chapter 9. The Cost of Fairness Epilogue: Sacrifices Not Worth Making
Synopsis
A theory of justice for AI models making decisions about employment, lending, education, criminal justice, and other important social goods., A theory of justice for AI models making decisions about employment, lending, education, criminal justice, and other important social goods. Decisions about important social goods like education, employment, housing, loans, health care, and criminal justice are all becoming increasingly automated with the help of AI. But because AI models are trained on data with historical inequalities, they often produce unequal outcomes for members of disadvantaged groups. In AI Fairness , Derek Leben draws on traditional philosophical theories of fairness to develop a framework for evaluating AI models, which can be called a theory of algorithmic justice--a theory inspired by the theory of justice developed by the American philosopher John Rawls. For several years now, researchers who design AI models have investigated the causes of inequalities in AI decisions and proposed techniques for mitigating them. It turns out that in most realistic conditions it is impossible to comply with all metrics simultaneously. Because of this, companies using AI systems will have to choose which metric they think is the correct measure of fairness, and regulators will need to determine how to apply currently existing laws to AI systems. Leben provides a detailed set of practical recommendations for companies looking to evaluate their AI systems and regulators thinking about laws around AI systems, and he offers an honest analysis of the costs of implementing fairness in AI systems--as well as when these costs may or may not be acceptable., A theory of justice for AI models making decisions about employment, lending, education, criminal justice, and other important social goods. Decisions about important social goods like education, employment, housing, loans, health care, and criminal justice are all becoming increasingly automated with the help of AI. But because AI models are trained on data with historical inequalities, they often produce unequal outcomes for members of disadvantaged groups. In AI Fairness , Derek Leben draws on traditional philosophical theories of fairness to develop a framework for evaluating AI models, which can be called a theory of algorithmic justice-a theory inspired by the theory of justice developed by the American philosopher John Rawls. For several years now, researchers who design AI models have investigated the causes of inequalities in AI decisions and proposed techniques for mitigating them. It turns out that in most realistic conditions it is impossible to comply with all metrics simultaneously. Because of this, companies using AI systems will have to choose which metric they think is the correct measure of fairness, and regulators will need to determine how to apply currently existing laws to AI systems. Leben provides a detailed set of practical recommendations for companies looking to evaluate their AI systems and regulators thinking about laws around AI systems, and he offers an honest analysis of the costs of implementing fairness in AI systems-as well as when these costs may or may not be acceptable.
LC Classification Number
Q334.7.L43 2025
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