INDEX

Page numbers in italic indicate a figure and page numbers in bold indicatea table on the corresponding page.

• A
• Abstract features, categorization based on, 82–83
• Admissions. See College admissions scenarios
• Agency. See Moral agency
• Agrawal, Ajay, 109
• AI. See Artificial intelligence (AI)
• Airline reservation algorithm, 64
• Alexa, privacy concerns with, 88
• Algorithmic aversion, 5–6, 8, 37
• Algorithmic bias, 8
• college admissions scenarios for, 70–71, 73, 75, 79, 83–84
• demographic correlates with, 69, 73
• equity versus predictive accuracy in, 83–85
• examples of, 64–65
• explicit versus abstract features in, 82–83
• harm-wrongness ratings of, 131
• human resource screenings scenarios for, 70, 74, 78
• implicit association tests of, 19–20
• Likert-type scale for, 68–69
• origins of, 64–65
• policing scenarios for, 72, 77, 81, 84–85
• salary increase scenarios for, 71–72, 76, 80
• Amazon Mechanical Turk, 5n, 28, 166
• Ambush scenario
• moral dimensions of, 186, 186
• moral space representation of, 131–132
• moral space representations of, 128, 129, 131–132
• American Airlines reservation algorithm, 64
• Amusement park scenario
• moral dimensions of, 193, 193
• moral space representation of, 128, 129
• Anthems scenarios. See National symbol scenarios
• Artificial intelligence (AI)
• advances in, 14
• interactions with, 2–3
• liability for, 156–159
• moral agency of, 7, 16–18, 37
• moral rules versus social networks in, 150–156
• moral status of, 7, 16–18
• participant attitudes toward, 169–170, 171
• regulation of, 152
• responsibility for actions of, 156–159
• strong, 15–16
• weak, 15–16
• Asimov, Isaac, “three laws of robotics,” 150–153
• Authority
• definition of, 21
• measurement of, 28–29, 28
• perceived levels of (see Moral scenarios)
• Automatic associations
• cultural differences in, 22
• implicit association tests of, 19–20
• Automatic teller machines (ATMs), introduction of, 107–108
• Autonomous vehicle scenarios
• moral dimensions of, 23–24, 50–55, 53–54
• moral space representation of, 127, 128, 129, 130, 131
• Autonomous weapon systems, 14

• B
• Bank robbery scenario
• moral dimensions of, 185, 185
• moral space representation of, 128, 129
• Bias. See also Algorithmic bias
• intergroup, 124, 125n
• selection, 167
• Bimodal judgment, 9, 146, 157
• Blame, attribution of. See also Responsibility
• in peanut butter allergy scenario, 23
• in trolley problem, 174–176, 175
• Blasphemous comedian scenario
• moral dimensions of, 46–47, 48
• moral space representation of, 128, 129
• Botnik, 40–41
• Brand names scenario
• moral dimensions of, 192, 192
• moral space representation of, 128, 129
• Brynjolfsson, E., 119
• Bureaucracies, judgment of, 159–162

• C
• Camera bots, 88
• Chatbots, 3
• Citizen scoring scenario, 100–103, 101
• Civil engineer scenarios
• moral dimensions of, 201–202, 201, 202
• moral space representation of, 128, 129
• Civil Rights Act (1964), 84
• Cleaning robots, 17
• Clopening, 106
• Codelco, 50
• College admissions scenarios
• efficiency versus equity in, 83–84
• moral dimensions of, 70–71, 73, 75, 79
• moral space representation of, 128, 129, 131
• Comedian sketch scenario. See Blasphemous comedy scenario
• Competence, person perception and, 82
• Computer vision systems
• moral dimensions of, 91–96, 94–95
• privacy concerns with, 88
• Condorcet, Nicolas de, 121
• Congruent trials, 19–20
• Control conditions, 4
• Conversational robots. See Chatbots
• Counseling scenario
• moral dimensions of, 190, 190
• moral space representation of, 128, 129
• Creative artificial intelligence (AI)
• capabilities of, 40–41
• creative industry scenarios, 46–49, 48–49
• limitations of, 41n
• marketing scenarios, 42–44, 44–45
• moral space representation of, 128, 129
• Culture, impact on moral judgment, 22

• D
• Data privacy. See Privacy and surveillance concerns
• Deep fake videos, 40
• Demographic parity, 65–66
• Demographics, 9
• algorithmic bias and, 69, 73
• effects on moral judgment, 141–146, 142–143, 145
• of study participants, 167–169, 168
• Desecration of national symbols. See National symbol scenarios
• Detroit (game), 153–154
• Differential privacy, 90–91
• Digital records, privacy concerns with, 88
• Discriminatory situations. See also Algorithmic bias
• college admissions, 70–71, 73, 75, 79, 83–84
• demographic correlates with, 69, 73
• fairness in, 64–68
• human resource screenings, 70, 74, 78
• Likert-type scale for, 68–69
• moral space representation of, 128, 129
• policing, 72, 77, 81
• salary increases, 71–72, 76, 80
• Displacement. See Labor displacement
• Domingos, Pedro, 17
• Drones, military use of, 14
• Durkheim, Emile, 160

• E
• Education levels
• effects on moral judgment, 9, 141–146, 142–143, 145
• of study participants, 167–169, 168
• Emotions, in moral reasoning, 19–22
• Enlightenment, harm basis of morality in, 18–19
• Equality in false acceptances, 66
• Equality of false rejections, 65–66
• Ethical Algorithm, The (Kearns and Roth), 90
• Ethical concerns, 10, 16–18. See also Algorithmic bias; Labor displacement; Privacy and surveillance concerns
• autonomous vehicles, 23–24, 50–55, 53–54
• lewd or disrespectful behavior, 40–49, 41n, 44–45, 48–49
• moral agency, 7, 16–18, 37
• moral status, 7
• national symbol desecration, 56–61, 58–59
• need for research into, 3–6
• normative versus positive approaches to, 5–6
• risk-taking behavior, 35–38, 38–39, 178–179, 178–179, 180–181, 180–181
• strong versus weak AI in, 15–16
• Ethnicity
• correlation with moral judgments, 141–146, 142–143, 145
• of study participants, 167–169, 168
• European Commission, 152
• Explicit features, categorization based on, 82–83
• Explosive Ordinance Disposal (EOD) robots, 17

• F
• Facial recognition systems
• algorithmic bias in, 67
• moral dimensions of, 91–96, 94–95
• Facial recognition technology, 14
• Fairness, 21. See also Algorithmic bias
• equality of false rejections, 65–66
• measurement of, 28–29, 28
• perceived levels of (see Moral scenarios)
• statistical parity, 65–66
• Fake news, 40
• False acceptances, equality in, 66
• False rejections, equality of, 65–66
• Federated Learning, 91
• First Amendment rights, 56
• “Flag-burning” amendment, 56
• Flag desecration scenarios
• moral dimensions of, 56–57, 58–59
• moral space representation of, 128, 129
• Foreign contractors, displacement attributed to, 113–118, 117
• Foreign subsidiaries, displacement attributed to, 113–118, 117
• Foreign temporary workers, displacement attributed to, 109–118, 112–113, 117
• Forest fire scenario
• moral dimensions of, 178–179, 178–179
• moral space representation of, 128, 129, 130
• Frankenstein (Shelley), 2, 162
• Functions, moral. See Moral functions

• G
• Gaby mine, 50
• GANs. See Generative Adversarial Networks (GANs)
• Gas tax scenario
• moral dimensions of, 197, 197
• moral space representation of, 128, 129
• Gender
• correlation with moral judgments, 9, 141–146, 142–143, 145
• of study participants, 167–169, 168
• Generative Adversarial Networks (GANs), 40
• Generative AIs, 40–41
• Gödel, Kurt Friedrich, 153
• Good prediction scenario. See Procurement management scenarios
• Google, 152
• Graetz, G., 108
• Graveyard excavation scenario
• moral dimensions of, 26–31, 29
• moral space representation of, 127, 128, 129
• Griggs v. Duke Power Company (1971), 84

• H
• Haidt, Jonathan, 125
• Harm, 21. See also Intentionality
• measurement of, 28–29, 28
• moral functions of, 133–141, 137, 138, 139, 140
• moral space representation of, 125–132, 126, 128, 129
• patterns across scenarios, 127–132
• perceived levels of (see Moral scenarios)
• Harm basis of morality, 18–19
• Harm-intention plane, 127, 128, 129, 130
• Harm-wrongness plane, 129, 131–132
• Harris, Seth, 119
• Heroic assumptions, 133
• Heuristics, biases as, 82
• Hikers scenario
• moral dimensions of, 191, 191
• moral space representation of, 128, 129
• Hiring decisions. See Human resource screenings scenarios
• HIS Group hotel chain, hacking of, 88
• Human behavior, judgment of. See Moral judgments
• Human learning, limitations of, 161–162
• Human resource screenings scenarios
• moral dimensions of, 70, 74, 78
• moral space representation of, 128, 129,131
• Hume, David, 19n
• Hurricane scenario
• moral dimensions of, 180–181, 180–181
• moral space representation of, 128, 129, 130

• I
• I, Robot (Asimov), 150–152
• Implicit association tests, 19–20
• Implicit.harvard.edu website, 20
• Incongruent trials, 19–20
• Independent worker classification, 119
• Industrial Revolution, 107
• In-group favoritism. See Intergroup bias
• Intentionality, 10, 159–161
• demographic correlates with, 141–146, 142–143, 145
• human behavior judged by, 10, 23–26, 25, 124, 139, 146–147, 157, 159–162
• moral functions of, 133–141, 137, 138, 139, 140
• moral space representation of, 125–132, 126, 128, 129
• patterns across scenarios, 127–132
• perceived levels of (see Moral scenarios)
• wrongness and, 23–26, 25
• Interest rate scenario
• moral dimensions of, 198, 198
• moral space representation of, 128, 129, 131
• Intergroup bias, 124, 125n
• International Federation of Robotics (IFR), 108
• Intuition
• cultural differences in, 22
• implicit association tests of, 19–20

• J
• Jewelry robbery scenario
• moral dimensions of, 184, 184
• moral space representation of, 128, 129
• Judgments. See Moral judgments

• K
• K-anonymity, 89–90
• Kearns, Michael, 90
• Kleinberg, Jon, 83
• Knowledge diffusion, labor mobility as channel of, 119
• Komatsu, 50
• Kronos, 106
• Krueger, Alan, 119

• L
• Labor displacement
• factors affecting, 109
• fear of, 107–108
• inequality and, 14
• interaction between technology and labor in, 107–109
• mitigation of consequences of, 118–121
• moral dimensions of, 9, 109–118, 112–113, 117, 156
• precarization of work and, 106–107
• Labor mobility, barriers to, 119
• Learning, human versus machine, 161–162
• Legal implications, 10, 156–159
• Lewd behavior
• creative marketing scenarios, 42–44, 44–45
• moral space representation of, 128, 129
• playwright scenario, 47, 49
• Liability for machine actions, 156–159
• Licensing, state, 119
• Life-or-death decisions, attitudes toward AI in
• forest fire scenario, 178–179, 178–179
• hurricane scenario, 180–181, 180–181
• moral space representation of, 128, 129
• tsunami scenario, 35–38, 38–39
• Likability
• demographic correlates with, 141–146, 142–143, 145
• perceived levels of (see Moral scenarios)
• Likert-type scales
• for algorithmic bias scenarios, 68–69
• demographic correlates with, 141–146, 142–143, 145
• for graveyard excavator scenario, 26–27
• for labor displacement scenarios, 111
• for privacy scenarios, 94
• Literai, 40
• Looms, Luddite opposition to, 4, 107
• Loyalty
• definition of, 21
• measurement of, 28–29, 28
• perceived levels of (see Moral scenarios)
• Luddites, opposition to steam-powered looms, 4, 107
• Ludwig, Jens, 83

• M
• Machine actions, human judgment of. See Moral judgments
• Machine learning, 161–162
• Malle, B. F., trolley problem of, 4–5, 4n
• blame attributions for, 174–176, 175
• replication of, 172–173
• wrongness attributions for, 173–174, 174
• Marketing scenarios
• moral dimensions of, 42–44, 44–45
• moral space representation of, 128, 129
• Marx, Karl, 160
• Mathematics, incompleteness of, 153
• McAfee, A., 119
• Mechanical Turk (MTurk), 5n, 28, 166
• Methodology. See Study methodology
• Michaels, G., 108
• Microsoft Tay bot, 3
• Military draft, moral dimensions of, 21–22
• MIT Committee on the Use of Humans as Experimental Subjects, 166
• Mobile phone traces, 89
• Moral agency, 7, 16–18, 37
• Moral dimensions
• components of, 20–22
• impact on moral judgment, 22–26, 25
• intentionality and, 23–26
• measurement of, 28–29
• perceived levels of (see Moral scenarios)
• Moral foundation theory, 125
• Moral functions. See also Moral space
• definition of, 124–125
• modeling of, 133–141, 134, 135, 137, 138, 139, 140
• Morality. See also Moral judgments; Moral scenarios
• cultural differences in, 22
• definition of, 18
• dimensions of, 20–29, 25
• emotions and automatic associations in, 19–22
• Enlightenment theory of, 18–19
• harm basis of, 18–19
• implicit association tests of, 19–20
• moral foundation theory of, 125
• Moral judgments, 18–19. See also Moral scenarios
• algorithmic aversion and, 5–6, 8, 37
• bimodal, 9, 146, 157
• demographic correlates of, 9, 141–146, 142–143, 145
• ethical and legal implications of, 10, 156–159
• intentionality in, 25
• intentions versus outcomes in, 10, 23–26, 25, 124, 139, 146–147, 157, 159–162 (see also Intentionality)
• mathematical representation of, 24–26, 25
• moral functions of, 124–125, 133–141, 134, 135, 137, 138, 139, 140
• moral space representation of, 125–132, 126, 128, 129
• normative approach to, 5
• positive approach to, 5–6
• rejection/acceptance of technology and, 6
• responsibility for machine actions in, 156–159
• statistical models of, 9–10
• trolley problem example, 4–5, 4n, 172–173
• unimodal, 9, 146, 157
• Moral rules, contradictions with social relationships, 150–156
• Moral scenarios
• ambush, 128, 129, 131–132, 186, 186
• amusement park, 128, 129, 193, 193
• autonomous vehicles, 23–24, 50–55, 53–54, 127, 128, 129, 130–131
• bank robbery, 128, 129, 185, 185
• blasphemous comedian, 46–47, 48, 128, 129
• brand names, 128, 129, 192, 192
• citizen scoring, 100–103, 101
• civil engineer, 128, 129, 201–202, 201, 202
• college admissions, 70–71, 73, 75, 79, 83–84, 128, 129, 131
• computer vision systems, 91–96, 94–95
• counseling, 128, 129, 190, 190
• creative marketing, 42–44, 44–45, 128, 129
• definition of, 26
• dilemmas versus, 26
• forest fire, 128, 129, 130, 178–179, 178–179
• gas tax, 128, 129, 197, 197
• graveyard excavation, 26–31, 28, 29, 30, 127, 128, 129
• hikers, 128, 129, 191, 191
• human resource screenings, 70, 74, 78, 128, 129, 131
• hurricane, 128, 129, 130, 180–181, 180–181
• interest rate, 128, 129, 131, 198, 198
• jewelry robbery, 128, 129, 184, 184
• labor displacement, 9, 109–118, 112–113, 117, 156
• lewd playwright, 47, 49, 128, 129
• mathematical representation of, 24–26, 25
• moral agency in, 7, 16–18, 37
• national symbol desecration, 56–61, 58–59, 127, 128, 129
• nursing assistant, 128, 129, 194, 194
• patterns across, 127–132
• peanut butter allergy, 23–26, 25
• personal assistant, 128, 129, 200, 200
• pharmacy robbery, 128, 129, 183, 183
• physical therapist, 128, 129, 195, 195
• plagiarizing songwriter, 46–47, 48, 128, 129
• policing, 72, 77, 81, 84–85, 128, 129
• procurement management, 128, 129, 187–189, 187, 188, 189
• recommender systems, 96–100, 98–99
• salary increases, 71–72, 76, 80, 128, 129
• shoplifting security, 128, 129, 203, 203
• supermarket robbery, 128, 129, 182, 182
• suspected terrorist, 128, 129, 131–132, 199, 199
• tsunami, 35–38, 38–39, 128, 129, 130–131
• Twitter, 128, 129, 196, 196
• Moral space
• definition of, 125–126
• example of, 126–127, 126
• harm-intention plane in, 127, 128, 129, 130
• harm-wrongness plane in, 129, 131–132
• wrongness-intention plane in, 129, 130–131
• Moral status, 7, 16–18
• Moral surface, 133–141
• Moral wrongness. See Wrongness, perceived levels of
• More, Thomas, 121
• Mullainathan, Sendhil, 83

• N
• National symbol scenarios
• moral dimensions of, 56–61, 58–59
• moral space representation of, 127, 128, 129
• Navarro, Jannette, 106
• Negligence, 158
• Normative approaches, 5–6
• Nursing assistant scenario, 194, 194

• O
• OECD. See Organisation for Economic Co-operation and Development (OECD)
• Offshoring, displacement attributed to, 113–118, 117
• Online dating systems, 96–100, 99
• Organisation for Economic Co-operation and Development (OECD), 152
• Outcomes, moral judgment based on, 10, 23–26, 25, 124, 139, 146–147, 157, 159–162
• Outsourcing, displacement attributed to, 113–118, 117

• P
• Parity, statistical, 65–66
• Participants
• attitudes toward AI, 169–170, 171
• demographic characteristics of, 167–169, 168
• recruitment of, 28, 166–167
• PATE, 91
• Peanut butter allergy scenario, 23–26, 25
• Personal assistant scenario
• moral dimensions of, 200, 200
• moral space representation of, 128, 129
• Pharmacy robbery scenario
• moral dimensions of, 183, 183
• moral space representation of, 128, 129
• Physical therapist scenario
• moral dimensions of, 195, 195
• moral space representation of, 128, 129
• Plagiarizing songwriter scenario
• moral dimensions of, 46–47, 48
• moral space representation of, 128, 129
• Policing scenarios
• moral dimensions of, 72, 77, 81
• moral space representation of, 128, 129
• outcome-based approach to, 84–85
• Political orientation of study participants, 168–169
• Positive approaches, 5–6
• Precarization of work, 106–107
• Prediction costs, 109
• Prediction Machines (Agrawal), 109
• Predictive purchasing systems, 96–100, 98
• Prejudice, 82
• Pretrial risk assessment tools, algorithmic bias in, 67–68
• Principal components, 82–83
• Printing, introduction of, 4, 107
• Privacy and surveillance concerns, 14, 156
• citizen scoring scenario, 100–103, 101
• computer vision systems scenarios, 91–96, 94–95
• Likert-type scale for, 94
• privacy-preserving algorithms, 91
• proposed solutions to, 89–91
• recommender system scenarios, 96–100, 98–99
• risks of AI to, 64–65
• Procurement management scenarios
• moral dimensions of, 187–189, 187, 188, 189
• moral space representation of, 128, 129
• Product liability, 158–159
• Promotion decisions. See Salary increase scenarios
• Purity
• definition of, 21
• measurement of, 28–29, 28
• perceived levels of (see Moral scenarios)
• P-values, 30–31
• ambush scenario, 186
• amusement park scenario, 193
• autonomous vehicle scenarios, 53–54
• bank robbery scenario, 185
• blasphemous comedian scenario, 48
• brand names scenario, 192
• citizen scoring scenario, 101
• civil engineer scenarios, 201, 202
• college admissions scenarios, 75, 79
• computer vision systems scenarios, 94–95
• counseling scenario, 190
• creative marketing, 44–45
• forest fire scenario, 178–179
• gas tax scenario, 197
• graveyard excavation scenario, 30
• hikers scenario, 191
• human resource screenings scenarios, 74, 78
• hurricane scenario, 180–181
• interest rate scenario, 198
• jewelry robbery scenario, 184
• labor displacement scenarios, 112–113, 117
• lewd playwright scenario, 49
• national symbol desecration, 58–59
• nursing assistant scenario, 194
• personal assistant scenario, 200
• pharmacy robbery scenario, 183
• physical therapist scenario, 195
• plagiarizing songwriter scenario, 48
• policing scenarios, 77, 81
• procurement management scenarios, 187, 188, 189
• recommender systems scenarios, 98–99
• salary increase scenarios, 76, 80
• shoplifting security scenario, 203
• supermarket robbery scenario, 182
• suspected terrorist scenario, 199
• tsunami scenario, 38–39
• Twitter scenario, 196

• Q
• QR codes, 108

• R
• Rambachan, Ashesh, 83
• Randomized response, 90–91
• Rappor, 91
• Rationality, morality and, 18–19
• Raw material shortage/excess. See Procurement management scenarios
• Real-world studies, 7
• Recidivism, 67
• Recklessness, 158
• Recommender systems scenarios, 96–100, 98–99
• Regulation, labor, 118–119
• Reidentification risks, 89
• Religious orientation
• effects on moral judgment, 141–146, 142–143, 145
• of study participants, 167–169, 168
• Replace different/same assessments
• demographic correlates with, 141–146, 142–143, 145
• of scenarios (see Moral scenarios)
• Research methodology. See Study methodology
• Responsibility
• liability and, 156–159
• perceived levels of (see Moral scenarios)
• trolley problem example, 174–176, 175
• Rio Tinto, 50
• Risk-taking, attitudes toward AI in
• forest fire scenario, 178–179, 178–179
• hurricane scenario, 180–181, 180–181
• moral space representation of, 130–131
• tsunami scenario, 35–38, 38–39
• Robbery scenarios
• moral dimensions of, 182–185, 182, 183, 184
• moral space representation of, 128, 129
• Robots
• camera bots, 88
• chatbots, 3
• Explosive Ordinance Disposal, 17
• health-care, 14
• moral agency of, 7, 16–18, 37
• moral status of, 7, 16–18
• tax on use of, 119
• Roth, Aaron, 90

• S
• Salary increase scenarios
• moral dimensions of, 71–72, 76, 80
• moral space representation of, 128, 129
• Scenarios. See Moral scenarios
• Selection bias, 167
• Self-driving cars. See Autonomous vehicle scenarios
• Sex robots, 17
• Shelley, Mary, 2, 162
• Shelley AI, 40–41
• Shoplifting security scenario
• moral dimensions of, 203, 203
• moral space representation of, 128, 129
• Similar situation scale
• demographic correlates with, 141–146, 142–143, 145
• perceived levels of (see Moral scenarios)
• Simulated studies, 7
• Siri, privacy concerns with, 88
• Social networks, contradictions arising from, 150–156
• Split Learning, 91
• Starbucks, 106
• State licensing, 119
• Statistical models, 9–10
• Statistical parity, 65–66
• Status, moral, 7, 16–18
• Stereotyping, 82
• Strong artificial intelligence, 15–16
• Study methodology, 3–4, 8, 26–31. See also Trolley problem
• Likert-type scales, 26–27, 68–69, 94, 111
• participant attitudes, 169–170, 171
• participant demographics, 167–169, 168
• participant recruitment, 28, 166–167
• scenario structure, 26
• study design, 166
• word association exercise, 28–29, 28
• Supermarket robbery scenario
• moral dimensions of, 182, 182
• moral space representation of, 128, 129
• Surveillance. See Privacy and surveillance concerns
• Suspected terrorist scenario
• moral dimensions of, 199, 199
• moral space representation of, 128, 129, 131–132
• Sweeney, Latanya, 89

• T
• Tax, robot, 119
• Tay chatbot (Microsoft), 3
• Technological displacement. See Labor displacement
• Temporary foreign workers, displacement attributed to, 109–118, 112–113, 117
• Texas v. Johnson (1989), 56
• “Three laws of robotics” (Asimov), 152
• Travel company scenario, 96–100, 99
• Trolley problem
• blame attributions for, 4–5, 174–176, 175
• description of, 4n, 172–173
• moral status in, 17
• wrongness attributions for, 173–174, 174
• Tsunami scenario
• moral dimensions of, 35–38, 38–39
• moral space representation of, 128, 129, 130–131
• Tweeting bots, 40
• Twitter scenario
• moral dimensions of, 196, 196
• moral space representation of, 128, 129

• U
• Unfair treatment. See Discriminatory situations
• Unimodal distribution, 9, 146, 157
• United Airlines reservation algorithm, 64
• Universal basic income (UBI), 121
• Unlearning, 161–162

• V
• Vicarious liability, 158–159

• W
• Warmth, person perception and, 82
• Weak artificial intelligence (AI), 15–16
• Weber, Max, 159, 160
• Weld, William, 89
• Word association exercise, 28–29, 28
• Word embedding, 67
• Wrongness, perceived levels of. See also Intentionality; Moral scenarios
• blame attribution, 23, 174–176, 175
• demographic correlates with, 141–146, 142–143, 145
• moral functions of, 133–141, 137, 138, 139, 140
• moral space representation of, 125–132, 126, 128, 129
• patterns across scenarios, 127–132
• in trolley problem, 173–174, 174
• Wrongness-intention plane, 129, 130–131

• Y
• Yang, Andrew, 121
• Younger workers, displacement attributed to, 113–118, 117