Ethics of data and analytics: concepts and cases
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
CRC Press, an imprint of the Taylor & Francis Group
2022
|
| Ausgabe: | First edition |
| Schriftenreihe: | An Auerbach Book
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xvii, 473 Seiten Illustrationen, Diagramme |
| ISBN: | 9781032217314 9781032062938 |
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| adam_text | Contents Introduction.............................................................................................. 1 xi Value-Laden Biases in Data Analytics..................................................................................... 1 Summary of Readings............... ................. 2 Related Cases............................................................................................. 4 Notes............................................... ;............................................................................................. 4 1.1 This Is the Stanford Vaccine Algorithm That Left out Frontline Doctors...........6 EILEEN GUO AND KAREN HAO 1.2 Racial Bias in a Medical Algorithm Favors White Patients over Sicker Black Patients........................................................................................................ 10 CAROLYN Y. JOHNSON 1.3 Excerpt from Do Artifacts Have Politics?................................................................. 13 LANGDON WINNER 1.4 Excerpt from Bias in Computer Systems.................................................................... 20 BATYA FRIEDMAN AND HELEN NISSENBAUM 1.5 Excerpt from Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning............................................................................................... 27 GABBRIELLE Μ. JOHNSON 1.6 Algorithmic Bias and Corporate Responsibility: How Companies Hide behind the False Veil of the Technological Imperative................................. 36 KIRSTEN MARTIN 2 Ethical Theories and Data
Analytics.................................................................................... 51 Summary of Readings................................................................................................................ 53 Virtue Ethics................. 53 Critical Approaches, Ethics, and Power..................... 53 Related Cases....................................................... 54 Notes............................................................................................................................................55 2.1 Language Models Like GPT-3 Could Herald a New Type of Search Engine.....57 WILL DOUGLAS HEAVEN v
vi ■ Contents 2.2 How to Make a Chatbot That Isn’t Racist or Sexist............................................... 60 WILL DOUGLAS HEAVEN 2.3 This Facial Recognition Website Can Turn Anyone into a Cop—or a Stalker....... 63 DREW HARWELL 2.4 Excerpt from Technology and the Virtues։ A Philosophical Guide toa Future Worth Wanting........................................................................................ 68 SHANNON VALLOR 2.5 Ethics of Care as Moral Grounding for AI................................ 78 CAROLINA VILLEGAS-GALAVIZ 2.6 Excerpt from Operationalizing Critical Race Theory in the Marketplace..........84 SONJA MARTIN POOLE, SONYA A. GRIER, KEVIN D. THOMAS, FRANCESCA SOBANDE, AKON E. ΕΚΡΟ, LEZ TRUJILLO TORRES, LYNN A. ADDINGTON,,MELINDA WEEKES-LAIDLOW, AND GERALDINE ROSA HENDERSON 3 Privacy, Data, and Shared Responsibility........................................................................... 93 Summary of Readings - Privacy......................................................................................... 95 Related Cases - Privacy......................................... 96 Summary of Readings - Questions for Data....................................................... 96 Related Cases - Questions for Data...........................................................................................97 Notes............................................................................................................................................ 97 3.1 Finding Consumers, No Matter Where They Hide: Ad Targeting and Location
Data................................ ................... 99 KIRSTEN MARTIN 3.2 How a Company You’ve Never Heard of Sends You Letters about Your Medical Condition........ ..................................................................................... 107 SURYA MATTU AND KASHMIR HILL « 3.3 Excerpt from A Contextual Approach to Privacy Online.................................... 112 HELEN NISSENBAUM 3.4 Excerpt from Understanding Privacy Online: Development of a Social Contract Approach to Privacy.......................................................................... 119 KIRSTEN MARTIN 3.5 Privacy Law for Business Decision-Makers in the United States....................... 129 CLARISSA WILBUR BERGER 3.6 Wrongfully Accused by an Algorithm................................................................ 138 KASHMIR HILL 3.7 Facial Recognition Is Accurate, IfYou’re a White Guy......................................... 143 STEVE LOHR 3.8 Excerpt from Datasheets for Datasets........................... TIMNIT GEBRU, JAMIE MORGENSTERN, BRIANA VECCHIONE, JENNIFER WORTMAN VAUGHAN, HANNA WALLACH, HAL DAUMÉ III, AND KATE CRAWFORD 148
Contents ■ vii 4 Surveillance and Power....................................... 157 Summary of Readings........................... .................................................................................. 158 Related Cases......................................... ·................................................................................. 159 Notes.................... 160 4.1 Twelve Million Phones, One Dataset, Zero Privacy............................................... 161 STUART A. THOMPSON AND CHARLIE WARZEL 4.2 The Secretive Company That Might End Privacy as We Know It....................... 170 KASHMIR HILL 4.3 Excerpt from Big Brother to Electronic Panopticon.............................................. 178 DAVID LYON 4.4 Excerpt from Privacy, Visibility, Transparency, and Exposure...........................188 JULIE E. COHEN 5 The Purpose of the Corporation and Data Analytics..................................................... 195 Summary of Readings.............................................................................. ................................ 196 Related Cases........................................................ .................................................................... 198 Notes.......................................................................................................................................... 199 5.1 The Quiet Growth of Race-Detection Software Sparks Concerns over Bias....201 PARMY OLSON 5.2 A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the
Job............................................................................................. 206 DREW HARWELL 5.3 Excerpt from Managing for Stakeholders.............................................................. 212 R. EDWARD FREEMAN 5.4 Excerpt from The Problem of Corporate Purpose................................................. 217 LYNN A. STOUT 5.5 Recommending an Insurrection: Facebook and Recommendation Algorithms................................ 225 KIRSTEN MARTIN 5.6 Excerpt from Can Socially Responsible Firms Survive in a Competitive Environment?...................................................................................................... 240 ROBERT H. FRANK 6 Fairness and Justice in Data Analytics........................і..................................................... 249 Summary of Readings..................................................... 250 Related Cases..................................................... 251 Notes..........................................................................................................................................252 6.1 Machine Bias..................................................................................................................254 JULIA ANGWIN, JEFF LARSON, SURYA MATTU, AND LAUREN KIRCHNER 6.2 Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say.................................................................... JULIA ANGWIN AND JEFF LARSON 265
viii ■ Contents 6.3 Major Universities Are Using Race as a “High Impact Predictor” of Student Success.................................................................................................. 268 TODD FEATHERS 6.4 Excerpt from Distributive Justice........................................................ 274 ROBERT NOZICK 6.5 Excerpt from Justice as Fairness................................................................................277 JOHN RAWLS 6.6 Excerpt from Tyranny and Complex Equality........................................................ 282 MICHAEL WALZER 7 Discrimination and Data Analytics........................................................... 291 Summary of Readings.............................................................................................................. 292 Related Cases.............................................................................................................................294 Notes....................................... л,............................................................................................... 294 7.1 Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women.................................................................................................. 296 JEFFREY DASTIN 7.2 Bias Isn’t the Only Problem with Credit Scores—and No, AI Can’t Help...... 300 WILL DOUGLAS HEAVEN 7.3 Excerpt from Big Data’s Disparate Impact............................................................ 303 SOLON BAROCAS AND ANDREW D. SELBST 7.4 Excerpt from Where Fairness Fails: Data, Algorithms,
and the Limits of Antidiscrimination Discourse..... .................................................................... 319 ANNA LAUREN HOFFMAN 8 Creating Outcomes and Accuracy in Data Analytics.......................................................329 Summary of Readings................................................. 332 Related Cases................................................................................................................ ľ.......... 333 Notes....................................................................................... 333 8.1 Pasco’s Sheriff Uses Grades and Abuse Histories to Label Schoolchildren Potential Criminals: The Kids and Their Parents Don’t Know................335 NEIL BEDI AND KATHLEEN MCGORY 8.2 Excerpt from Reliance on Metrics is a Fundamental Challenge for AI............ 342 RACHEL L. THOMAS AND DAVID UMINSKY 8.3 Excerpt from Designing Ethical Algorithms.......................................................... 350 KIRSTEN MARTIN 9 Gamification, Manipulation, and Data Analytics............................................................ 357 Summary of Readings................................................................................ .............................. 359 Related Cases.................................................................................... 360 Notes............................................................................................... 360
Contents ■ ix 9.1 How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons.................. 362 NOAM SCHEIBER 9.2 How Deepfakes Could Change Fashion Advertising........................................ 372 KATI CHITRAKORN 9.3 Excerpt from Ethics of Gamification.................................................................. 375 TAE WAN KIM AND KEVIN WERBACH 9.4 Excerpt from Manipulation, Privacy, and Choice............................................... 386 KIRSTEN MARTIN 9.5 Excerpt from Ethics of the Attention Economy: The Problem of Social Media Addiction........................................................................................... 391 VIKRAM R. BHARGAVA AND MANUEL VELASQUEZ 10 Transparency and Accountability in Data Analytics................................................... 403 Summary of Readings................. 405 Related Cases........... ........................... 405 Notes......................................................................................................................................... 406 10.1 Houston Teachers to Pursue Lawsuit over Secret Evaluation System........... 408 SHELBY WEBB 10.2 Cheating-Detection Companies Made Millions During the Pandemic. Now Students Are Fighting back............................................................ 410 DREW HARWELL 10.3 When Algorithms Mess Up, the Nearest Human Gets the Blame.................. 418 KAREN HAO 10.4 Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions................................ 420 DANIEL N.
KLUTTZ, NITIN KOHLI, AND DEIRDRE K. MULLIGAN 11 Ethics, AI, Research, and Corporations....................................................................... 429 Summary of Readings...................................... 431 Related Cases............................................................................................ 432 Notes......................................................................................................................................... 432 11.1 Google Research: Who Is Responsible for Ethics of AI?.................................. 434 KIRSTEN MARTIN 11.2 The Scientist Qua Scientist Makes Value Judgments....................................... 447 RICHARD RUDNER 11.3 Excerpt from Ethical Implications and Accountability ofAlgorithms......... 453 KIRSTEN MARTIN Index..................................................................... 463
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| adam_txt |
Contents Introduction. 1 xi Value-Laden Biases in Data Analytics. 1 Summary of Readings. . 2 Related Cases. 4 Notes. ;. 4 1.1 This Is the Stanford Vaccine Algorithm That Left out Frontline Doctors.6 EILEEN GUO AND KAREN HAO 1.2 Racial Bias in a Medical Algorithm Favors White Patients over Sicker Black Patients. 10 CAROLYN Y. JOHNSON 1.3 Excerpt from Do Artifacts Have Politics?. 13 LANGDON WINNER 1.4 Excerpt from Bias in Computer Systems. 20 BATYA FRIEDMAN AND HELEN NISSENBAUM 1.5 Excerpt from Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning. 27 GABBRIELLE Μ. JOHNSON 1.6 Algorithmic Bias and Corporate Responsibility: How Companies Hide behind the False Veil of the Technological Imperative. 36 KIRSTEN MARTIN 2 Ethical Theories and Data
Analytics. 51 Summary of Readings. 53 Virtue Ethics. 53 Critical Approaches, Ethics, and Power. 53 Related Cases. 54 Notes.55 2.1 Language Models Like GPT-3 Could Herald a New Type of Search Engine.57 WILL DOUGLAS HEAVEN v
vi ■ Contents 2.2 How to Make a Chatbot That Isn’t Racist or Sexist. 60 WILL DOUGLAS HEAVEN 2.3 This Facial Recognition Website Can Turn Anyone into a Cop—or a Stalker. 63 DREW HARWELL 2.4 Excerpt from Technology and the Virtues։ A Philosophical Guide toa Future Worth Wanting. 68 SHANNON VALLOR 2.5 Ethics of Care as Moral Grounding for AI. 78 CAROLINA VILLEGAS-GALAVIZ 2.6 Excerpt from Operationalizing Critical Race Theory in the Marketplace.84 SONJA MARTIN POOLE, SONYA A. GRIER, KEVIN D. THOMAS, FRANCESCA SOBANDE, AKON E. ΕΚΡΟ, LEZ TRUJILLO TORRES, LYNN A. ADDINGTON,,MELINDA WEEKES-LAIDLOW, AND GERALDINE ROSA HENDERSON 3 Privacy, Data, and Shared Responsibility. 93 Summary of Readings - Privacy. 95 Related Cases - Privacy. 96 Summary of Readings - Questions for Data. 96 Related Cases - Questions for Data.97 Notes. 97 3.1 Finding Consumers, No Matter Where They Hide: Ad Targeting and Location
Data. . 99 KIRSTEN MARTIN 3.2 How a Company You’ve Never Heard of Sends You Letters about Your Medical Condition. . 107 SURYA MATTU AND KASHMIR HILL « 3.3 Excerpt from A Contextual Approach to Privacy Online. 112 HELEN NISSENBAUM 3.4 Excerpt from Understanding Privacy Online: Development of a Social Contract Approach to Privacy. 119 KIRSTEN MARTIN 3.5 Privacy Law for Business Decision-Makers in the United States. 129 CLARISSA WILBUR BERGER 3.6 Wrongfully Accused by an Algorithm. 138 KASHMIR HILL 3.7 Facial Recognition Is Accurate, IfYou’re a White Guy. 143 STEVE LOHR 3.8 Excerpt from Datasheets for Datasets. TIMNIT GEBRU, JAMIE MORGENSTERN, BRIANA VECCHIONE, JENNIFER WORTMAN VAUGHAN, HANNA WALLACH, HAL DAUMÉ III, AND KATE CRAWFORD 148
Contents ■ vii 4 Surveillance and Power. 157 Summary of Readings. . 158 Related Cases. ·. 159 Notes. 160 4.1 Twelve Million Phones, One Dataset, Zero Privacy. 161 STUART A. THOMPSON AND CHARLIE WARZEL 4.2 The Secretive Company That Might End Privacy as We Know It. 170 KASHMIR HILL 4.3 Excerpt from Big Brother to Electronic Panopticon. 178 DAVID LYON 4.4 Excerpt from Privacy, Visibility, Transparency, and Exposure.188 JULIE E. COHEN 5 The Purpose of the Corporation and Data Analytics. 195 Summary of Readings. . 196 Related Cases. . 198 Notes. 199 5.1 The Quiet Growth of Race-Detection Software Sparks Concerns over Bias.201 PARMY OLSON 5.2 A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the
Job. 206 DREW HARWELL 5.3 Excerpt from Managing for Stakeholders. 212 R. EDWARD FREEMAN 5.4 Excerpt from The Problem of Corporate Purpose. 217 LYNN A. STOUT 5.5 Recommending an Insurrection: Facebook and Recommendation Algorithms. 225 KIRSTEN MARTIN 5.6 Excerpt from Can Socially Responsible Firms Survive in a Competitive Environment?. 240 ROBERT H. FRANK 6 Fairness and Justice in Data Analytics.і. 249 Summary of Readings. 250 Related Cases. 251 Notes.252 6.1 Machine Bias.254 JULIA ANGWIN, JEFF LARSON, SURYA MATTU, AND LAUREN KIRCHNER 6.2 Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say. JULIA ANGWIN AND JEFF LARSON 265
viii ■ Contents 6.3 Major Universities Are Using Race as a “High Impact Predictor” of Student Success. 268 TODD FEATHERS 6.4 Excerpt from Distributive Justice. 274 ROBERT NOZICK 6.5 Excerpt from Justice as Fairness.277 JOHN RAWLS 6.6 Excerpt from Tyranny and Complex Equality. 282 MICHAEL WALZER 7 Discrimination and Data Analytics. 291 Summary of Readings. 292 Related Cases.294 Notes. л,. 294 7.1 Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women. 296 JEFFREY DASTIN 7.2 Bias Isn’t the Only Problem with Credit Scores—and No, AI Can’t Help. 300 WILL DOUGLAS HEAVEN 7.3 Excerpt from Big Data’s Disparate Impact. 303 SOLON BAROCAS AND ANDREW D. SELBST 7.4 Excerpt from Where Fairness Fails: Data, Algorithms,
and the Limits of Antidiscrimination Discourse. . 319 ANNA LAUREN HOFFMAN 8 Creating Outcomes and Accuracy in Data Analytics.329 Summary of Readings. 332 Related Cases. ľ. 333 Notes. 333 8.1 Pasco’s Sheriff Uses Grades and Abuse Histories to Label Schoolchildren Potential Criminals: The Kids and Their Parents Don’t Know.335 NEIL BEDI AND KATHLEEN MCGORY 8.2 Excerpt from Reliance on Metrics is a Fundamental Challenge for AI. 342 RACHEL L. THOMAS AND DAVID UMINSKY 8.3 Excerpt from Designing Ethical Algorithms. 350 KIRSTEN MARTIN 9 Gamification, Manipulation, and Data Analytics. 357 Summary of Readings. . 359 Related Cases. 360 Notes. 360
Contents ■ ix 9.1 How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons. 362 NOAM SCHEIBER 9.2 How Deepfakes Could Change Fashion Advertising. 372 KATI CHITRAKORN 9.3 Excerpt from Ethics of Gamification. 375 TAE WAN KIM AND KEVIN WERBACH 9.4 Excerpt from Manipulation, Privacy, and Choice. 386 KIRSTEN MARTIN 9.5 Excerpt from Ethics of the Attention Economy: The Problem of Social Media Addiction. 391 VIKRAM R. BHARGAVA AND MANUEL VELASQUEZ 10 Transparency and Accountability in Data Analytics. 403 Summary of Readings. 405 Related Cases. . 405 Notes. 406 10.1 Houston Teachers to Pursue Lawsuit over Secret Evaluation System. 408 SHELBY WEBB 10.2 Cheating-Detection Companies Made Millions During the Pandemic. Now Students Are Fighting back. 410 DREW HARWELL 10.3 When Algorithms Mess Up, the Nearest Human Gets the Blame. 418 KAREN HAO 10.4 Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions. 420 DANIEL N.
KLUTTZ, NITIN KOHLI, AND DEIRDRE K. MULLIGAN 11 Ethics, AI, Research, and Corporations. 429 Summary of Readings. 431 Related Cases. 432 Notes. 432 11.1 Google Research: Who Is Responsible for Ethics of AI?. 434 KIRSTEN MARTIN 11.2 The Scientist Qua Scientist Makes Value Judgments. 447 RICHARD RUDNER 11.3 Excerpt from Ethical Implications and Accountability ofAlgorithms. 453 KIRSTEN MARTIN Index. 463 |
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| id | DE-604.BV048290576 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:03:29Z |
| indexdate | 2024-07-10T09:34:20Z |
| institution | BVB |
| isbn | 9781032217314 9781032062938 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033670587 |
| oclc_num | 1311568059 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | xvii, 473 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press, an imprint of the Taylor & Francis Group |
| record_format | marc |
| series2 | An Auerbach Book |
| spelling | Martin, Kirsten Verfasser aut Ethics of data and analytics concepts and cases Kirsten Martin First edition Boca Raton CRC Press, an imprint of the Taylor & Francis Group 2022 xvii, 473 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier An Auerbach Book Includes bibliographical references and index Ethik (DE-588)4015602-3 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data mining / Moral and ethical aspects / Textbooks Big data / Moral and ethical aspects / Textbooks Artificial intelligence / Moral and ethical aspects / Textbooks Business ethics / Textbooks Datenanalyse (DE-588)4123037-1 s Data Mining (DE-588)4428654-5 s Künstliche Intelligenz (DE-588)4033447-8 s Ethik (DE-588)4015602-3 s DE-604 Erscheint auch als Online-Ausgabe 978-1-003-27829-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033670587&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Martin, Kirsten Ethics of data and analytics concepts and cases Ethik (DE-588)4015602-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4015602-3 (DE-588)4033447-8 (DE-588)4428654-5 (DE-588)4123037-1 |
| title | Ethics of data and analytics concepts and cases |
| title_auth | Ethics of data and analytics concepts and cases |
| title_exact_search | Ethics of data and analytics concepts and cases |
| title_exact_search_txtP | Ethics of data and analytics concepts and cases |
| title_full | Ethics of data and analytics concepts and cases Kirsten Martin |
| title_fullStr | Ethics of data and analytics concepts and cases Kirsten Martin |
| title_full_unstemmed | Ethics of data and analytics concepts and cases Kirsten Martin |
| title_short | Ethics of data and analytics |
| title_sort | ethics of data and analytics concepts and cases |
| title_sub | concepts and cases |
| topic | Ethik (DE-588)4015602-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Ethik Künstliche Intelligenz Data Mining Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033670587&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT martinkirsten ethicsofdataandanalyticsconceptsandcases |