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Book Synopsis

Chapter 1: Introduction.- 1.1 Overview.- 1.2 Motivation and Challenges.- 1.3 Contributions.- Chapter 2: Social Intelligence Applications and Backgrounds.- 2.1 Social Intelligence: the emergence of human intelligence and AI.- 2.2 Enabling Technologies for Social Intelligence.- 2.3 Interdisciplinary Nature of Social Intelligence.- 2.4 Emerging Social Intelligence Applications.- Chapter 3: Data Heterogeneity.- 3.1 The Data Heterogeneity Problem in Social Intelligence.- 3.2 A Multimodal Approach: DuoGen and ContrastFaux.- 3.4 Real-world Case Studies.- 3.5 Discussion.- Chapter 4: Data Sparsity and Model Generality.- 4.1 The Data Sparsity and Model Generality Problem in Social Intelligence.- 4.2 Robust and General Social Intelligence: CrowdAdapt and CollabGeneral.- 4.3 Real-world Case Studies.- 4.4 Discussion.- Chapter 5: Explainable AI (XAI) in Social Intelligence.- 4.1 A Collaborative Explanation for AI.- 4.2 Social XAI: CrowdGraph and CEA-COVID.- 4.3 Real-world Case Studies.- 4.4 Discussion.- Chapter 6: Fusing Crowd Wisdom and AI.- 6.1 Integrating Crowd-based Human Intelligence and AI.- 6.2 A Crowd-AI Co-Design: CrowdNAS and CrowdHPO.- 6.4 Real-world Case Studies.- 6.5 Discussion.- Chapter 7: Fairness and Bias Issue.- 7.1 The Fairness and Bias Issue in Social Intelligence.- 7.2 Fair Social AI Solution: FairCrowd and DebiasEdu.- 7.3 Real-world Case Studies.- 7.4 Discussion.- Chapter 8: Privacy Issue.- 8.1 Understanding Privacy in Social Intelligence.- 8.2 Privacy-aware Crowd-AI Approach: CoviDKG and FaceCrowd.- 8.3 Real-world Case Studies.- 8.4 Discussion.- Chapter 9: Further Readings.-  9.1 Human-centered AI.- 9.2 AI for Social Good.- 9.3 Fairness and Privacy in Social Intelligence.- 9.4 Ethics and Policies in Social Intelligence.- Chapter 10: Conclusions and Remaining Challenges.

Social Intelligence

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    A Hardback by Dong Wang

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      View other formats and editions of Social Intelligence by Dong Wang

      Publisher: Springer
      Publication Date: 29/05/2025
      ISBN13: 9783031900792, 978-3031900792
      ISBN10:
      Also in:
      Computing Artificial intelligence (AI)

      Description

      Book Synopsis

      Chapter 1: Introduction.- 1.1 Overview.- 1.2 Motivation and Challenges.- 1.3 Contributions.- Chapter 2: Social Intelligence Applications and Backgrounds.- 2.1 Social Intelligence: the emergence of human intelligence and AI.- 2.2 Enabling Technologies for Social Intelligence.- 2.3 Interdisciplinary Nature of Social Intelligence.- 2.4 Emerging Social Intelligence Applications.- Chapter 3: Data Heterogeneity.- 3.1 The Data Heterogeneity Problem in Social Intelligence.- 3.2 A Multimodal Approach: DuoGen and ContrastFaux.- 3.4 Real-world Case Studies.- 3.5 Discussion.- Chapter 4: Data Sparsity and Model Generality.- 4.1 The Data Sparsity and Model Generality Problem in Social Intelligence.- 4.2 Robust and General Social Intelligence: CrowdAdapt and CollabGeneral.- 4.3 Real-world Case Studies.- 4.4 Discussion.- Chapter 5: Explainable AI (XAI) in Social Intelligence.- 4.1 A Collaborative Explanation for AI.- 4.2 Social XAI: CrowdGraph and CEA-COVID.- 4.3 Real-world Case Studies.- 4.4 Discussion.- Chapter 6: Fusing Crowd Wisdom and AI.- 6.1 Integrating Crowd-based Human Intelligence and AI.- 6.2 A Crowd-AI Co-Design: CrowdNAS and CrowdHPO.- 6.4 Real-world Case Studies.- 6.5 Discussion.- Chapter 7: Fairness and Bias Issue.- 7.1 The Fairness and Bias Issue in Social Intelligence.- 7.2 Fair Social AI Solution: FairCrowd and DebiasEdu.- 7.3 Real-world Case Studies.- 7.4 Discussion.- Chapter 8: Privacy Issue.- 8.1 Understanding Privacy in Social Intelligence.- 8.2 Privacy-aware Crowd-AI Approach: CoviDKG and FaceCrowd.- 8.3 Real-world Case Studies.- 8.4 Discussion.- Chapter 9: Further Readings.-  9.1 Human-centered AI.- 9.2 AI for Social Good.- 9.3 Fairness and Privacy in Social Intelligence.- 9.4 Ethics and Policies in Social Intelligence.- Chapter 10: Conclusions and Remaining Challenges.

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