Socially responsible AI: theories and practices
"In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. In response, researchers...
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo
World Scientific Publishing Co. Pte. Ltd.
[2023]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. In response, researchers and organizations have been working to publish principles and develop AI regulations for the responsible use of AI in consequential application domains. However, these theoretically formulated principles and regulations also need to be turned into actionable algorithms to materialize AI for good. This book introduces a unified perspective of Socially Responsible AI to help bridge conceptual AI principles to responsible AI practice. It begins with an interdisciplinary definition of socially responsible AI and the AI responsibility pyramid. Existing efforts seeking to materialize the mainstream responsible AI principles are then presented. The book also discusses how to leverage advanced AI techniques to address the challenging societal issues through Protecting, Informing, and Preventing, and concludes with open problems and challenges. This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges of socially responsible AI, and to identify how their areas of expertise can contribute to making AI socially responsible"-- |
| Beschreibung: | xv, 179 Seiten Illustrationen (teilweise farbig) 24 cm |
| ISBN: | 9789811266621 9780000991188 |
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| 520 | 3 | |a "In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. In response, researchers and organizations have been working to publish principles and develop AI regulations for the responsible use of AI in consequential application domains. However, these theoretically formulated principles and regulations also need to be turned into actionable algorithms to materialize AI for good. This book introduces a unified perspective of Socially Responsible AI to help bridge conceptual AI principles to responsible AI practice. It begins with an interdisciplinary definition of socially responsible AI and the AI responsibility pyramid. Existing efforts seeking to materialize the mainstream responsible AI principles are then presented. The book also discusses how to leverage advanced AI techniques to address the challenging societal issues through Protecting, Informing, and Preventing, and concludes with open problems and challenges. This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges of socially responsible AI, and to identify how their areas of expertise can contribute to making AI socially responsible"-- | |
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Datensatz im Suchindex
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| adam_text | Contents vii Preface About the Authors ix Acknowledgments xi 1. 2. Defining Socially Responsible AI 1 1.1 1.2 1.3 1.4 1.5 Why NOW........................................................................ What is Socially Responsible AI................................... The AI Responsibility Pyramid................................... Socially Responsible AI Algorithms . . ....................... What Could Go Wrong?................................................ 1.5.1 Formalization.................................................... 1.5.2 Measuring Errors.............................................. 1.5.3 Bias....................................................................... 1.5.4 Data Misuse....................................................... 1.5.5 Dependence versus Causality........................... 1.6 Concluding Remarks...................................................... 1.6.1 Summary.............................................................. 1.6.2 Additional Readings ........................................ 1 3 4 7 8 9 9 10 11 12 13 13 13 Theories in Socially Responsible AI 15 2.1 Fairness ............................................................................ 16 2.1.1 Different Fairness Notions .............................. 16 2.1.2 Mitigating Unwanted Bias.............................. 22 2.1.3 Discussion............................................................... 29 xiii
Socially Responsible AI: Theories and Practices xiv 2.2 Interpretability ...................................................................30 2.2.1 Different Forms of Explanations......................... 31 2.2.2 Taxonomy of AI Interpretability......................... 31 2.2.3 Techniques for AI Interpretability...................... 33 2.2.4 Discussion...............................................................45 2.3 Privacy.................................................................................. 46 2.3.1 Traditional Privacy Models...................................47 2.3.2 Privacy for Social Graphs.................................. 53 2.3.3 Graph Anonymization ..................................... 59 2.3.4 Discussion.............................................................. 65 2.4 Distribution Shift........................................................... 66 2.4.1 Different Types of Distribution Shifts............ 67 2.4.2 Mitigating Distribution Shift via Domain Adaptation............................................... 70 2.4.3 Mitigating Distribution Shift via Domain Generalization........................................ 76 2.4.4 Discussion.......................................................... 84 2.5 Concluding Remarks..................................................... 85 2.5.1 Summary............................................................. 85 2.5.2 Additional Readings ....................................... 85 3. Practices of Socially Responsible AI 3.1 89 Protecting.......................................................................
89 3.1.1 A Multi-Modal Approach for Cyberbullying Detection.............................................................. 90 3.1.2 A Deep Learning Approach for Social Bot Detection.................................................... 96 3.1.3 A Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes........................................................... 99 3.2 Informing .......................................................................... 103 3.2.1 An Approach for Explainable Fake News Detection................................................................ 104 3.2.2 Causal Understanding of Fake News Dissemination on Social Media........................... 107 3.3 Preventing.......................................................................... 112 3.3.1 Mitigating Gender Bias in Word Embeddings.......................................................... 112 3.3.2 Debiasing CyberbullyingDetection................. 116
Contents 3.4 4. xv Concluding Remarks....................................................... 120 3.4.1 Summary................................................................. 120 3.4.2 Additional Readings ............................................121 Challenges of Socially Responsible AI 123 Causality and Socially Responsible AI ....................... 123 4.1.1 Causal Inference 101 ............................................ 124 4.1.2 Causality-based Fairness Notions andBias Mitigation............................................................. 128 4.1.3 Causality and Interpretability............................ 137 4.2 How Context Can Help................................................... 141 4.2.1 A Sequential Bias Mitigation Approach.... 142 4.2.2 A Multidisciplinary Approach for Context-Specific Interpretability........................ 147 4.3 The Trade-offs: Can’t We have Them All?............... 149 4.3.1 The Fairness-Utility Trade-off............................ 149 4.3.2 The Interpretability-Utility Trade-off.............. 152 4.3.3 The Privacy-Utility Trade-off............................ 154 4.3.4 Trade-offs among Fairness, Interpretability, and Privacy.......................................................... 157 4.4 Concluding Remarks...................................................... 158 4.4.1 Summary................................................................ 158 4.4.2 Additional Readings ........................................... 159 4.1 Bibliography 161 Index 177
|
| adam_txt |
Contents vii Preface About the Authors ix Acknowledgments xi 1. 2. Defining Socially Responsible AI 1 1.1 1.2 1.3 1.4 1.5 Why NOW. What is Socially Responsible AI. The AI Responsibility Pyramid. Socially Responsible AI Algorithms . . . What Could Go Wrong?. 1.5.1 Formalization. 1.5.2 Measuring Errors. 1.5.3 Bias. 1.5.4 Data Misuse. 1.5.5 Dependence versus Causality. 1.6 Concluding Remarks. 1.6.1 Summary. 1.6.2 Additional Readings . 1 3 4 7 8 9 9 10 11 12 13 13 13 Theories in Socially Responsible AI 15 2.1 Fairness . 16 2.1.1 Different Fairness Notions . 16 2.1.2 Mitigating Unwanted Bias. 22 2.1.3 Discussion. 29 xiii
Socially Responsible AI: Theories and Practices xiv 2.2 Interpretability .30 2.2.1 Different Forms of Explanations. 31 2.2.2 Taxonomy of AI Interpretability. 31 2.2.3 Techniques for AI Interpretability. 33 2.2.4 Discussion.45 2.3 Privacy. 46 2.3.1 Traditional Privacy Models.47 2.3.2 Privacy for Social Graphs. 53 2.3.3 Graph Anonymization . 59 2.3.4 Discussion. 65 2.4 Distribution Shift. 66 2.4.1 Different Types of Distribution Shifts. 67 2.4.2 Mitigating Distribution Shift via Domain Adaptation. 70 2.4.3 Mitigating Distribution Shift via Domain Generalization. 76 2.4.4 Discussion. 84 2.5 Concluding Remarks. 85 2.5.1 Summary. 85 2.5.2 Additional Readings . 85 3. Practices of Socially Responsible AI 3.1 89 Protecting.
89 3.1.1 A Multi-Modal Approach for Cyberbullying Detection. 90 3.1.2 A Deep Learning Approach for Social Bot Detection. 96 3.1.3 A Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes. 99 3.2 Informing . 103 3.2.1 An Approach for Explainable Fake News Detection. 104 3.2.2 Causal Understanding of Fake News Dissemination on Social Media. 107 3.3 Preventing. 112 3.3.1 Mitigating Gender Bias in Word Embeddings. 112 3.3.2 Debiasing CyberbullyingDetection. 116
Contents 3.4 4. xv Concluding Remarks. 120 3.4.1 Summary. 120 3.4.2 Additional Readings .121 Challenges of Socially Responsible AI 123 Causality and Socially Responsible AI . 123 4.1.1 Causal Inference 101 . 124 4.1.2 Causality-based Fairness Notions andBias Mitigation. 128 4.1.3 Causality and Interpretability. 137 4.2 How Context Can Help. 141 4.2.1 A Sequential Bias Mitigation Approach. 142 4.2.2 A Multidisciplinary Approach for Context-Specific Interpretability. 147 4.3 The Trade-offs: Can’t We have Them All?. 149 4.3.1 The Fairness-Utility Trade-off. 149 4.3.2 The Interpretability-Utility Trade-off. 152 4.3.3 The Privacy-Utility Trade-off. 154 4.3.4 Trade-offs among Fairness, Interpretability, and Privacy. 157 4.4 Concluding Remarks. 158 4.4.1 Summary. 158 4.4.2 Additional Readings . 159 4.1 Bibliography 161 Index 177 |
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| index_date | 2024-07-03T22:41:54Z |
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| institution | BVB |
| isbn | 9789811266621 9780000991188 |
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| spelling | Cheng, Lu Verfasser (DE-588)1308101058 aut Socially responsible AI theories and practices Lu Cheng (University of Illinois at Chicago, USA), Huan Liu (Arizona State University, USA) Socially responsible artificial intelligence New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo World Scientific Publishing Co. Pte. Ltd. [2023] © 2023 xv, 179 Seiten Illustrationen (teilweise farbig) 24 cm txt rdacontent n rdamedia nc rdacarrier "In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. In response, researchers and organizations have been working to publish principles and develop AI regulations for the responsible use of AI in consequential application domains. However, these theoretically formulated principles and regulations also need to be turned into actionable algorithms to materialize AI for good. This book introduces a unified perspective of Socially Responsible AI to help bridge conceptual AI principles to responsible AI practice. It begins with an interdisciplinary definition of socially responsible AI and the AI responsibility pyramid. Existing efforts seeking to materialize the mainstream responsible AI principles are then presented. The book also discusses how to leverage advanced AI techniques to address the challenging societal issues through Protecting, Informing, and Preventing, and concludes with open problems and challenges. This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges of socially responsible AI, and to identify how their areas of expertise can contribute to making AI socially responsible"-- Soziale Verantwortung (DE-588)4055737-6 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Verantwortungsethik (DE-588)4236475-9 gnd rswk-swf Artificial intelligence / Social aspects Artificial intelligence / Moral and ethical aspects Künstliche Intelligenz (DE-588)4033447-8 s Soziale Verantwortung (DE-588)4055737-6 s Verantwortungsethik (DE-588)4236475-9 s DE-604 Liu, Huan 1958- Verfasser (DE-588)138749736 aut Äquivalent Druck-Ausgabe, Paperback 978-0-000-99118-8 Erscheint auch als Online-Ausgabe 9789811266638 Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034576111&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cheng, Lu Liu, Huan 1958- Socially responsible AI theories and practices Soziale Verantwortung (DE-588)4055737-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Verantwortungsethik (DE-588)4236475-9 gnd |
| subject_GND | (DE-588)4055737-6 (DE-588)4033447-8 (DE-588)4236475-9 |
| title | Socially responsible AI theories and practices |
| title_alt | Socially responsible artificial intelligence |
| title_auth | Socially responsible AI theories and practices |
| title_exact_search | Socially responsible AI theories and practices |
| title_exact_search_txtP | Socially responsible AI theories and practices |
| title_full | Socially responsible AI theories and practices Lu Cheng (University of Illinois at Chicago, USA), Huan Liu (Arizona State University, USA) |
| title_fullStr | Socially responsible AI theories and practices Lu Cheng (University of Illinois at Chicago, USA), Huan Liu (Arizona State University, USA) |
| title_full_unstemmed | Socially responsible AI theories and practices Lu Cheng (University of Illinois at Chicago, USA), Huan Liu (Arizona State University, USA) |
| title_short | Socially responsible AI |
| title_sort | socially responsible ai theories and practices |
| title_sub | theories and practices |
| topic | Soziale Verantwortung (DE-588)4055737-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Verantwortungsethik (DE-588)4236475-9 gnd |
| topic_facet | Soziale Verantwortung Künstliche Intelligenz Verantwortungsethik |
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