- Advertising, targeted/discriminatory, 100–101, 115–116
- Allegheny County Family Screening Tool (AFST), 141–144
- AlphaGo, xix
- AlphaZero, 72
- Amazon, 3, 47, 115, 153–154
- Artificial general intelligence (AGI), xviii–xix
- Automation bias, 79–91, 138
- Autonomous vehicles, 73–76, 86, 168, 171–173
- no-fault compensation schemes and, 75–76
- strict liability and, 74
- Autonomy, 64, 103, 107–126
- human vs. machine, 71–72
- intrinsic vs. instrumental value of, 112–113
- liberty, actual vs. potential, 111–112
- privacy and, 113, 116, 118
- Babbage, Charles, xi
- Analytical Engine, xi
- Bias, 43–60. See also Privacy
- in algorithms, 45–46, 47–48, 52–54, 55–56, 57–60
- in humans, 34–36, 43–45
- sampling/selection, xv
- Cambridge Analytica, 103
- Classification tasks, xiv–xv
- human performance in, xiv–xv
- classifiers, 11–12
- Clinical judgment, xiii–xiv
- COMPAS, 57–60, 82–83
- Computer science, relation to AI, 1
- Computers vs. humans. See Humans vs. computers
- Confusion matrices, 15
- Copernicus, Nicolaus, xviii
- COVID-19, xvi, xxii, 175–176
- Echo chambers, 115
- Employment, 149–157
- COVID-19 and, 176
- dignity of, 154–157
- gig economy, 153–154
- privacy and, 103, 153–154
- women and, 151–152
- Engels, Friedrich, xii
- Communist Manifesto, xii
- Evaluation methods, 15–16, 140–141
- Ex Machina (film), xix
- Explainability, 24
- double standards, 39–41
- Explainable AI (XAI), 37–39
- human reasons and, 30–33
- neural networks and, 28–29
- practical reason and, 30–33
- reasons, duty to provide, 26–28
- reasons for decisions and, 26
- right to explanation, 26–28, 106
- Facebook, 51, 97n, 100–101, 102n, 104, 116, 124
- Filtering algorithms, 115–116
- Freud, Sigmund, xviii
- General Data Protection Regulation (GDPR), 30, 33, 106, 125
- Gig economy, 153–154
- Gompertz, Benjamin, 7
- Gompertz curve, 7
- Google, 3, 39, 51, 54, 57, 115, 161–162
- Hobbes, Thomas, 111
- House of Lords Select Committee on AI (UK), 33–34, 163
- Human dignity, xvii–xix, 141
- Human factors, 79–91, 138
- Humans vs. computers, xviii–xix, 71–72, 87–88, 89
- rationality of machine learning, xx–xi, 140
- Keynes, John Maynard, 150
- Machiavelli, Niccolò, 111
- Marx, Karl, xii
- Communist Manifesto, xii
- Microsoft, 51, 55
- Pettit, Philip, 111
- PredPol, 49–50
- Privacy, 93–106, 113, 116, 118
- advertising, targeted/discriminatory, 100–101, 115–116
- access to data, 99, 106
- anonymization, 99
- by design, 105–106
- collected vs. inferred data, 98
- consent to collection of data, elusiveness of, 97–98
- correction of data, 99, 106
- data brokerage, 97
- data protection, as distinct from, 95
- definitional challenges, 93–95
- deletion of data, 100, 106
- derived data, 98, 106
- employment and, 103, 153–154
- inferred data, 98, 106
- informational, 95, 96–100
- observed vs. inferred data, 98
- purpose limitation, 96
- retention of data, limits to, 100
- varieties of, 93–96, 103
- Professional judgment, xiii–xiv
- Raz, Joseph, 109–110
- Regression, 8–10
- Regulation, 159–174
- Reinforcement learning, 19
- Responsibility, 23, 25, 61–78
- AI and, 70–73, 77
- causation and, 64, 68–69
- conditions of, 64
- foresight and, 64, 69–70
- moral vs. legal, 62–63, 65, 66–67
- technology’s effects on, 67–70
- Responsibility gap, 71
- vs. moral crumple zone, 73
- Russell, Bertrand, 155, 157
- Smith, Adam, 127
- On the Wealth of Nations, 127
- Statistics, 1–19
- bias, sampling/selection xv
- correlations, counterintuitive, xx–xi
- history of, 3, 4–6
- vs. human judgment, xiii–xiv, 140
- machine learning, relation to, 3, 4–6
- Supervised learning, 13–14
- error backpropagation, 14
- Surveillance capitalism, 118
- Unsupervised learning, 18
- Verne, Jules, xii
- Twenty Thousand Leagues Under the Sea, xii
- von Neumann, John, xi, 1
- Wells, H. G., xi
- Wisdom of crowds, 48–49