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

217 Pages·2023·8.48 MB·English

by  Don Donghee Shin

Algorithms, Humans,
and Interactions
Amidst the rampant use of algorithmization enabled by AI, the common
theme of AI systems is the human factor. Humans play an essential role
in designing, developing, and operationalizing AI systems. We have
a remit to ensure those systems run transparently, perform equitably,
value our privacy, and efectively fulfll human needs. Tis book takes an
interdisciplinary approach to contribute to the ongoing development of
human–AI interaction with a particular focus on the “human” dimension
and provides insights to improve the design of AI that could be genuinely
benefcial and efectively used in society. Te readers of this book will
beneft by gaining insights into various perspectives about how AI has
impacted people and society and how it will do so in the future, and
understanding how we can design algorithm systems that are benefcial,
legitimate, usable by humans, and designed considering and respecting
human values. Tis book provides a horizontal set of guidelines and insight
into how humans can be empowered by making choices about AI designs
that allow them meaningful control over AI. Designing meaningful AI
experiences has garnered great attention to address responsibility gaps
and mitigate them by establishing conditions that enable the proper
attribution of responsibility to humans. Tis book helps us understand
the possibilities of what AI systems can do and how they can and should
be integrated into our society.

Algorithms, Humans,
and Interactions
How Do Algorithms Interact
with People? Designing Meaningful
AI Experiences
Don Donghee Shin
Cover Image Credit: Anton Grabolle/Better Images of AI/Human-AI collaboration/CC-BY 4.0
First edition published 2023
by Routledge
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© 2023 Don Donghee Shin
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ISBN: 978-1-032-33358-8 (hbk)
ISBN: 978-1-032-33297-0 (pbk)
ISBN: 978-1-003-31931-3 (ebk)
DOI: 10.1201/b23083
Typeset in Minion
by Apex CoVantage, LLC
Contents
Preface, ix
Acknowledgments, xi
Author, xiii
Endorsements, xv
INTRODUCTION 1
SOCIOTECHNICAL PERSPECTIVE FOR ALGORITHMS 1
CHAPTER 1 ◾ Algorithmic Experience 11
1.1 INTERACTING WITH ALGORITHMS: HOW
PEOPLE PERCEIVE, COGNIZE, AND ENGAGE
WITH ALGORITHMS 11
1.2 THE FUNCTIONS AND ACCEPTANCE OF
ALGORITHMS 14
1.3 HEURISTIC–SYSTEMATIC PROCESS 17
1.4 THE ALGORITHM ACCEPTANCE MODEL:
HOW PEOPLE ACCEPT ALGORITHMS 20
1.4.1 FAccT (Fairness, Accountability, and
Transparency) 21
1.4.2 Trust and Utility 22
1.4.3 Personalization and Accuracy 23
1.5 DYNAMICS OF ALGORITHMIC CULTURE 25
1.6 IMPLICATIONS: WHAT YOU SOW SO SHALL
YOU REAP 27
1.7 CONCLUDING REMARKS 28
v
vi ◾ Contents
CHAPTER 2 ◾ A lgorithmic Awareness 35
2.1 WHY IS USER AWARENESS CRITICAL
IN ALGORITHMS? 35
2.2 KNOWING ALGORITHMS 39
2.3 ALGORITHMIC SENSEMAKING 43
2.4 ALGORITHMIC DECISION-MAKING 44
2.5 ALGORITHM AVERSION AND APPRECIATION 45
2.6 ALGORITHMIC AWARENESS AND USER HEURISTICS 49
2.7 YOU CAN SEE AS MUCH AS YOU KNOW 50
2.8 USER AWARENESS BY DESIGN 52
2.9 ALGORITHMIC DIVIDE 53
2.10 CONCLUSION 54
CHAPTER 3 ◾ A lgorithmic Nudge 59
3.1 DOES ALGORITHMIC NUDGING MAKE BETTER
CHOICES? 59
3.2 NUDGES AND ALGORITHMIC AFFORDANCE:
FROM BLACKBOX AI TO TRANSPARENT
AFFORDANCES 62
3.3 ALGORITHMIC SOCIAL MANAGING:
ALGORITHMIC  BEHAVIOR MODIFICATION 64
3.4 CONCERNS OVER ALGORITHM-DRIVEN NUDGES 69
3.4.1 Algorithmic Un-Nudge: Algorithmic Aversion
and Resistance to Algorithms 72
3.5 ALGORITHMIC NUDGES WITH MEANINGFUL
CONTROL AND ALGORITHMIC AUDIT 74
CHAPTER 4 ◾ A lgorithmic Credibility 81
4.1 WHY DOES CREDIBILITY MATTER IN
ALGORITHMS? 81
4.2 ALGORITHMIC CREDIBILITY 84
4.3 TRUSTWORTHY AI 87
4.4 AI-BASED CHATBOT INTERACTION: HOW DO
USERS INTERACT WITH CHATBOT? 90
4.5 ALGORITHMIC INFORMATION PROCESSING:
COGNITIVE PERSPECTIVE 91
Contents    ◾    vii
4.6 HOW DO HUMANS PROCESS ALGORITHMIC
INFORMATION? 96
4.7 HUMANIZING ALGORITHMIC INTELLIGENCE 97
CHAPTER 5 ◾ A lgorithmic Bias 103
5.1 WHY IS AI VULNERABLE TO BIAS? 103
5.2 TYPES OF ALGORITHMIC BIAS 108
5.3 A NEGATIVE FEEDBACK LOOP AND BIAS 110
5.4 FAKE NEWS, MISINFORMATION, AND AI 111
5.5 RESPONSIBLE AI 113
5.6 FAIRNESS AND TRANSPARENCY IN ALGORITHMS 117
5.7 THEORIZING THE EFFECTS OF FAIRNESS AND
TRANSPARENCY ON SENSEMAKING PROCESSES 119
5.8 FAIRNESS AND TRANSPARENCY GROUNDED IN
USERS’ PERSPECTIVES: TRANSPARENT FAIRNESS 120
CHAPTER 6 ◾ Explainable Algorithms 127
6.1 WHY EXPLAIN? EXPLAINING EXPLAINABILITY 127
6.2 COGNITIVE RESPONSE TO EXPLAINABILITY IN AI 131
6.2.1 Mediating Efect of Explainability 133
6.2.2 Te Dual-Step Flow Model of AI Interaction 134
6.3 STANDARDS FOR EXPLAINABLE AI 136
6.4 A RIGHT TO EXPLANATION 137
6.5 APPLICATION IN EXPLAINABLE AI USE CASES 140
6.5.1 Explanatory Journalism 140
6.5.2 News Recommendation Systems 141
6.5.3 Analytic Platforms 142
6.6 BRIDGING THE GAP BETWEEN EXPLAINABILITY
AND HUMAN COGNITION 143
6.7 BEYOND EXPLAINABLE AI 145
CHAPTER 7 ◾ A lgorithmic Journalism: Current Trends
and Future Developments 151
7.1 INTRODUCTION 152
7.1.1 Algorithmic Filtering and Gatekeeping: 153
viii ◾ Contents
7.1.2 News Algorithms: Algorithmed Public Spheres 155
7.1.3 Growing Need for Algorithmic Fairness and
Transparency 157
7.2 CASE STUDY OF NAVER’S ALGORITHMIC NEWS 158
7.2.1 Algorithmic Journalism in South Korea 158
7.2.2 Naver News Algorithms: AI-Driven News
Recommendations 160
7.2.3 How AiRS Works 161
7.2.4 Concerns Regarding News Algorithms 161
7.2.5 Algorithmic Transparency and Fairness 164
7.2.6 Wider Impacts 165
7.2.7 F airness, Accountability, and Transparency
(FAccT) in Algorithmic Journalism 165
7.2.8 U ser Role in the Formation of Algorithms: Te
Changing Concept of Users 169
7.3 CONCLUSIONS: SHOW ME THE ALGORITHM 170
7.3.1 Suggestions for News Algorithms 171
CHAPTER 8 ◾ Human-Centered AI 177
8.1 HUMAN-CENTERED AI AND THE IMPORTANCE OF
MEANINGFUL HUMAN CONTROL  178
8.2 BUILDING HUMAN-CENTERED AI 182
8.3 EXAMPLES AND FRAMEWORKS OF HUMAN-
CENTERED AI 186
8.3.1 Removing Bias in AI-aided Hiring Process 186
8.3.2 AI-Enabled Conversational Advertising System 186
8.3.3 Human-Centered AI in Healthcare and Education 186
8.3.4 Human-Centered Recommender Systems 187
8.4 CONCLUSION: ARE AI SYSTEMS INTERPRETABLE,
EXPLAINABLE, AND EXPLICABLE? 191
EPILOGUE 195
INDEX, 197
Preface
Algorithms are pervasive in our society, and we live and work with
them and AI – knowingly and unknowingly. A huge amount of data
regarding our daily routines is monitored and analyzed by AI to make rec-
ommendations that manage, control, and frame our behaviors in everyday
life. Algorithms have become the key organizers through which power is
enacted and institutionalized in society. Whether or not algorithms pro-
mote the transformation of the economy in the direction of sustainability
is defned by the way these algorithms are designed, implemented, and
adopted. How algorithms are coded and trained and how we can under-
stand their decisions are important issues in this AI era.
Tis book presents current theories, techniques, methods, and vari-
ous sociotechnical issues related to human–AI interaction. Tis book
is a guide to understanding the dynamics of AI in human contexts by
addressing some important questions: How do we ensure AI is used for
our common good? How do humans and AI interact? How is AI shaping
our understanding of ourselves and our societies? How do we bridge the
gap between ethical considerations and practical realities to create respon-
sible, reliable systems? Trough these questions, this book proposes a way
to empower humans by enabling them to make choices about AI design,
which allows them control over AI. Designing meaningful AI experiences
has garnered great attention to address responsibility gaps and mitigate
them by establishing conditions that enable the proper attribution of
responsibility to humans (e.g., users, designers, developers, providers, and
lawmakers). We should not risk losing our grip on AI, which will result
in amputating human intelligence and detaching unique human value.
Hence, the important task is not about how to replace humans with algo-
rithms or machine learning but rather identifying the best way to uti-
lize AI to enrich the human experience by empowering people. With the
human-centered approach, human input is situated at the center of the
ix