Algorithms, Humans, and Interactions. How Do Algorithms Interact with People? Designing Meaningful AI Experiences

Cover
Half Title
Title
Copyright
Contents
Preface
Acknowledgments
Author
Endorsements
Introduction
	Sociotechnical Perspective for Algorithms
Chapter 1 Algorithmic Experience
	1.1 Interacting with Algorithms: How People Perceive, Cognize, and Engage with Algorithms
	1.2 The Functions and Acceptance of Algorithms
	1.3 Heuristic–Systematic Process
	1.4 The Algorithm Acceptance Model: How People Accept Algorithms
		1.4.1 FAccT (Fairness, Accountability, and Transparency)
		1.4.2 Trust and Utility
		1.4.3 Personalization and Accuracy
	1.5 Dynamics of Algorithmic Culture
	1.6 Implications: What You Sow so Shall You Reap
	1.7 Concluding Remarks
Chapter 2 Algorithmic Awareness
	2.1 Why is User Awareness Critical in Algorithms?
	2.2 Knowing Algorithms
	2.3 Algorithmic Sensemaking
	2.4 Algorithmic Decision-Making
	2.5 Algorithm Aversion and Appreciation
	2.6 Algorithmic Awareness and User Heuristics
	2.7 You Can See as Much as You Know
	2.8 User Awareness by Design
	2.9 Algorithmic Divide
	2.10 Conclusion
Chapter 3 Algorithmic Nudge
	3.1 Does Algorithmic Nudging Make Better Choices?
	3.2 Nudges and Algorithmic Affordance: From Blackbox AI to Transparent Affordances
	3.3 Algorithmic Social Managing: Algorithmic Behavior Modification
	3.4 Concerns Over Algorithm-Driven Nudges
		3.4.1 Algorithmic Un-Nudge: Algorithmic Aversion and Resistance to Algorithms
	3.5 Algorithmic Nudges with Meaningful Control and Algorithmic Audit
Chapter 4 Algorithmic Credibility
	4.1 Why does Credibility Matter in Algorithms?
	4.2 Algorithmic Credibility
	4.3 Trustworthy AI
	4.4 AI-Based Chatbot Interaction: How do Users Interact with Chatbot?
	4.5 Algorithmic Information Processing: Cognitive Perspective
	4.6 How do Humans Process Algorithmic Information?
	4.7 Humanizing Algorithmic Intelligence
Chapter 5 Algorithmic Bias
	5.1 Why is AI Vulnerable to Bias?
	5.2 Types of Algorithmic Bias
	5.3 A Negative Feedback Loop and Bias
	5.4 Fake News, Misinformation, and AI
	5.5 Responsible AI
	5.6 Fairness and Transparency in Algorithms
	5.7 Theorizing the Effects of Fairness and Transparency on Sensemaking Processes
	5.8 Fairness and Transparency Grounded in Users’ Perspectives: Transparent Fairness
Chapter 6 Explainable Algorithms
	6.1 Why Explain? Explaining Explainability
	6.2 Cognitive Response to Explainability in AI
		6.2.1 Mediating Effect of Explainability
		6.2.2 The Dual-Step Flow Model of AI Interaction
	6.3 Standards for Explainable AI
	6.4 A Right to Explanation
	6.5 Application in Explainable AI Use Cases
		6.5.1 Explanatory Journalism
		6.5.2 News Recommendation Systems
		6.5.3 Analytic Platforms
	6.6 Bridging the Gap Between Explainability and Human Cognition
	6.7 Beyond Explainable AI
Chapter 7 Algorithmic Journalism: Current Trends and Future Developments
	7.1 Introduction
		7.1.1 Algorithmic Filtering and Gatekeeping:
		7.1.2 News Algorithms: Algorithmed Public Spheres
		7.1.3 Growing Need for Algorithmic Fairness and Transparency
	7.2 Case Study of Naver’s Algorithmic News
		7.2.1 Algorithmic Journalism in South Korea
		7.2.2 Naver News Algorithms: AI-Driven News Recommendations
		7.2.3 How AiRS Works
		7.2.4 Concerns Regarding News Algorithms
		7.2.5 Algorithmic Transparency and Fairness
		7.2.6 Wider Impacts
		7.2.7 Fairness, Accountability, and Transparency (FAccT) in Algorithmic Journalism
		7.2.8 User Role in the Formation of Algorithms: The Changing Concept of Users
	7.3 Conclusions: Show Me the Algorithm
		7.3.1 Suggestions for News Algorithms
Chapter 8 Human-Centered AI
	8.1 Human-Centered AI and The Importance of Meaningful Human Control
	8.2 Building Human-Centered AI
	8.3 Examples and Frameworks of Human-Centered AI
		8.3.1 Removing Bias in AI-aided Hiring Process
		8.3.2 AI-Enabled Conversational Advertising System
		8.3.3 Human-Centered AI in Healthcare and Education
		8.3.4 Human-Centered Recommender Systems
	8.4 Conclusion: Are AI Systems Interpretable, Explainable, and Explicable?
Epilogue
Index