The page references in this index correspond to the printed edition from which this ebook was created. To find a specific word or phrase from the index, please use the search feature of your ebook reader.
Endnotes in the index are denoted by an ‘n’ after the page number e.g. ‘ambiguous images 211n13’
23andMe 108–9
profit 109
promises of anonymity 109
sale of data 109
volume of customers 110
52Metro 177
abnormalities 84, 87, 95
acute kidney injuries 104
Acxiom 31
Adele 193
advertising 33
online adverts 33–5
exploitative potential 35
inferences 35
personality traits and 40–1
political 39–43
targeted 41
AF447 (flight) 131–3, 137
Afigbo, Chukwuemeka 2
AI (artificial intelligence) 16–19
algorithms 58, 86
omnipotence 13
threat of 12 see also DeepMind
AI Music 192
Air France 131–3
Airbnb, random forests 59
Airbus A330 132–3
algebra 8
algorithmic art 194
algorithmic regulating body 70
algorithms aversion 23
Alhambra 156
Alton Towers 20–1
ALVINN (Autonomous Land Vehicle In a Neural Network) 118–19
Alzheimer’s disease 90–1, 92
Amazon 178
recommendation engine 9
ambiguous images 211n13
American Civil Liberties Union (ACLU) 17
Ancestry.com 110
anchoring effect 73
Anthropometric Laboratory 107–8
antibiotics 111
AOL accounts 2
Apple 47
Face ID system 165–6
arithmetic 8
art 175–95
algorithms 184, 188–9
similarity 187
books 178
films 180–4
popularity 183–4
judging the aesthetic value of 184
machines and 194
meaning of 194
measuring beauty 184–5
music 176–80
piano experiment 188–90
popularity 177, 178, 179
quality 179, 180
quantifying 184–8
social proof 177–8, 179
artifacts, power of 1–2
artificial intelligence (AI) see AI (artificial intelligence)
association algorithms 9
asthma 101–2
identifying warning signs 102
preventable deaths 102
Audi
slow-moving traffic 136
traffic jam pilot 136
authority of algorithms 16, 198, 199, 201
misuse of 200
automation
aircraft 131–3
hidden dangers 133–4
ironies of 133–7
reduction in human ability 134, 137 see also driverless cars
Autonomous Emergency Braking system 139
autonomy 129, 130
full 127, 130, 134, 138
autopilot systems
A330 132
driverless cars 134
pilot training 134
sloppy 137
Tesla 134, 135, 138
bail
comparing algorithms to human judges 59–61
contrasting predictions 60
success of algorithms 60–1
high-risk scores 70
Bainbridge, Lisanne 133–4, 135, 138
balance 112
Banksy 147, 185
Baril, David 171–2
Barstow 113
Bartlett, Jamie 44
Barwell, Clive 145–7
Bayes’ theorem 121–4, 225n30
driverless cars 124
red ball experiment 123–4
simultaneous hypotheses 122–3
Bayes, Thomas 123–4
Bayesian inference 99
beauty 184–5
Beck, Andy 82, 95
Bell, Joshua 185–6
Berk, Richard 61–2, 64
bias
of judges 70–1, 75
in machines 65–71
societal and cultural 71
biometric measurements 108
blind faith 14–16, 18
Bonin, Pierre-Cédric ‘company baby‘131–3
books 178
boost effect 151, 152
Bratton, Bill 148–50, 152
breast cancer
aggressive screening 94
detecting abnormalities 84, 87, 95
diagnoses 82–4
mammogram screenings 94, 96
over-diagnosis and over-treatment 94–5
research on corpses 92–3
‘in situ’ cancer cells 93
screening algorithms for 87
tumours, unwittingly carrying 93
bridges (route to Jones Beach)
racist 1
unusual features 1
Brixton
fighting 49
looting and violence 49–50
Brooks, Christopher Drew 64, 77
Brown, Joshua 135
browser history see internet browsing history
buffer zone 144
Burgess, Ernest W. 55–6
burglary 150–1
the boost 151, 152
connections with earthquakes 152
the flag 150–1, 152
Caixin Media 45
calculations 8
calculus 8
Caldicott, Dame Fiona 223n48
Cambridge Analytica 39
advertising 42
fake news 42
personality profiles 41–2
techniques 41–2
whistleblowers 42
CAMELYON16 competition 88, 89
cameras 119–20
cancer
benign 94
detection 88–9
and the immune system 93
malignant 94
‘in situ’ 93, 94
uncertainty of tumours 93–4 see also breast cancer
cancer diagnoses study 79–80
Car and Driver magazine 130–1
Carnegie 117
Carnegie Mellon University 115
cars 113–40
driverless see driverless cars see also DARPA (US Defence Advanced Research Projects Agency)
categories of algorithms
association 9
classification 9
filtering 9–10
prioritization 8
Centaur Chess 202
Charts of the Future 148–50
chauffeur mode 139
chess 5–7
Chicago Police Department 158
China 168
citizen scoring system 45–6
breaking trust 46
punishments 46
Sesame Credit 45–6, 168
smallpox inoculation 81
citizen scoring system 45–6
Citroen DS19 116, 116–17
Citymapper 23
classification algorithms 9
Clinical vs Statistical Prediction (Meehl) 21–2
Clinton Foundation 42
Clubcard (Tesco) 26
Cohen’s Kappa 215n12
cold cases 172
Cold War 18
Colgan, Steyve 155
Commodore 64 ix
COMPAS algorithm 63, 64
ProPublica analysis
accuracy of scores 65
false positives 66
mistakes 65–8
racial groups 65–6
secrecy of 69
CompStat 149
computational statistics 12
computer code 8
computer intelligence 13 see also AI (artificial intelligence)
computer science 8
computing power 5
considered thought 72
cookies 34
Cope, David 189, 190–1, 193
cops on the dots 155–6
Corelogic 31
counter-intuition 122
creativity, human 192–3
Creemers, Rogier 46
creepy line 28, 30, 39
crime 141–73
algorithmic regulation 173
boost effect 151, 152
burglary 150–1
cops on the dots 155–6
geographical patterns 142–3
gun 158
hotspots 148, 149, 150–1, 155
HunchLab algorithm 157–8
New York City subway 147–50
predictability of 144
PredPol algorithm 152–7, 158
proximity of offenders’ homes 144
recognizable patterns 143–4
retail 170
Strategic Subject List 158
target hardening 154–5 see also facial recognition
crime data 143–4
Crimewatch programme 142
criminals
buffer zone 144
distance decay 144
knowledge of local geographic area 144
serial offenders 144, 145
customers
data profiles 32
inferred data 32–4
insurance data 30–1
shopping habits 28, 29, 31
supermarket data 26–8
superstore data 28–31
cyclists 129
Daimler 115, 130
DARPA (US Defence Advanced Research Projects Agency)
driverless cars 113–16
investment in 113
Grand Challenge (2004) 113–14, 117
course 114
diversity of vehicles 114
GPS coordinates 114
problems 114–15
top-scoring vehicle 115
vehicles’ failure to finish 115
Grand Challenge (2005) 115
targeting of military vehicles 113–14
data 25–47
exchange of 25, 26, 44–5
dangers of 45
healthcare 105
insurance 30–1
internet browsing history 36–7, 36–8
internet giants 36
manipulation and 39–44
medical records 102–7
benefits of algorithms 106
DeepMind 104–5
disconnected 102–3
misuse of data 106
privacy 105–7
patterns in 79–81, 108
personal 108
regulation of
America 46–7
Europe 46–7
global trend 47
sale of 36–7
Sesame Credit 45–6, 168
shopping habits 28, 29, 31
supermarkets and 26–8
superstores and 28–31
data brokers 31–9
benefits provided by 32
Cambridge Analytica 39–42
data profiles 32
inferred data 32–4, 35
murky practices of 47
online adverts 33–5
rich and detailed datasets 103
Sesame Credit 45–6
unregulated 36
in America 36
dating algorithms 9
Davies, Toby 156, 157
decision trees 56–8
Deep Blue 5–7, 8
deep learning 86
DeepMind
access to full medical histories 104–5
consent ignored 105
outrage 104
contract with Royal Free NHS Trust 104
dementia 90–2
Dewes, Andreas 36–7
Dhami, Mandeep 75, 76
diabetic retinopathy 96
Diaconis, Pesri 124
diagnostic machines 98–101, 110–11
differential diagnosis 99
discrimination 71
disease
Alzheimer’s disease 90–1, 92
diabetic retinopathy 96
diagnosing 59, 99, 100
hereditary causes 108
Hippocrates’s understanding of 80
Huntington’s disease 110
motor neurone disease 100
pre-modern medicine 80 see also breast cancer
distance decay 144
DNA (deoxyribonucleic acid) 106, 109
testing 164–5
doctors 81
unique skills of 81–2
Dodds, Peter 176–7
doppelgängers 161–3, 164, 169
Douglas, Neil 162–3
driver-assistance technology 131
driverless cars 113–40
advantages 137
algorithms and 117
Bayes’ red ball analogy 123–4
ALVINN (Autonomous Land Vehicle In a Neural Network) 118–19
autonomy 129, 130
full 127, 130, 134, 138
Bayes’ theorem 121–4
breaking the rules of the road 128
bullying by people 129
cameras and 117–18
conditions for 129
cyclists and 129
dealing with people 128–9
difficulties of building 117–18, 127–8
early technology 116–17
framing of technology 138
inevitability of errors 140
measurement 119, 120
neural networks 117–18
potential issues 116
pre-decided go-zones 130
sci-fi era 116
simulations 136–7
speed and direction 117
support for drivers 139
trolley problem 125–6
Uber 135
Waymo 129–30
driverless technology 131
Dubois, Captain 133, 137
Duggan, Mark 49
Dunn, Edwina 26
early warning systems 18
earthquakes 151–2
eBureau 31
Eckert, Svea 36–7
empathy 81–2
ensembles 58
Eppink, Richard 17, 18
Epstein, Robert 14–15
equations 8
Equivant (formerly Northpointe) 69, 217n38
errors in algorithms 18–19, 61–2, 76, 159–60, 197–9, 201
false negatives 62, 87, 88
false positives 62, 66, 87, 88
Eureka Prometheus Project 117
expectant mothers 28–9
expectations 7
Experiments in Musical Intelligence (EMI) 189–91, 193
Face ID (Apple) 165–6
Facebook 2, 9, 36, 40
filtering 10
Likes 39–40
news feeds experiment 42–3
personality scores 39
privacy issues 25
severing ties with data brokers 47
FaceFirst 170, 171
FaceNet (Google) 167, 169
facial recognition
accuracy 171
falling 168
increasing 169
algorithms 160–3, 165, 201–2
2D images 166–7
3D model of face 165–6
Face ID (Apple) 165–6
FaceFirst 170
FaceNet (Google) 167, 169
measurements 163
MegaFace 168–9
statistical approach 166–7
Tencent YouTu Lab 169
in China 168
cold cases 172
David Baril incident 171–2
differences from DNA testing 164–5
doppelgängers 161–3, 164, 169
gambling addicts 169–70
identical looks 162–3, 164, 165
misidentification 168
neural networks 166–7
NYPD statistics 172
passport officers 161, 164
police databases of facial images 168
resemblance 164, 165
shoplifters 170
pros and cons of technology 170–1
software 160
trade-off 171–3
Youssef Zaghba incident 172
fairness 66–8, 201
tweaking 70
fake news 42
false negatives 62, 87, 88
false positives 62, 66, 87, 88
FBI (Federal Bureau of Investigation) 168
Federal Communications Commission (FCC) 36
Federal Trade Commission 47
feedback loops 156–7
films 180–4
algorithms for 183
edits 182–3
IMDb website 181–2
investment in 180
John Carter (film) 180
novelty and 182
popularity 183–4
predicting success 180–1
Rotten Tomatoes website 181
study 181–2
keywords 181–2
filtering algorithms 9–10
Financial Times 116
fingerprinting 145, 171
Firebird II 116
Firefox 47
Foothill 156
Ford 115, 130
forecasts, decision trees 57–8
free technology 44
Fuchs, Thomas 101
Galton, Francis 107–8
gambling addicts 169–70
GDPR (General Data Protection Regulation) 46
General Motors 116
genetic algorithms 191–2
genetic testing 108, 110
genome, human 108, 110
geographical patterns 142–3
geoprofiling 147
algorithm 144
Germany
facial recognition algorithms 161
linking of healthcare records 103
Goldman, William 181, 184
Google 14–15, 36
creepy line 28, 30, 39
data security record 105
FaceNet algorithm 167, 169
high-paying executive jobs 35 see also DeepMind
Google Brain 96
Google Chrome plugins 36–7
Google Images 69
Google Maps 120
Google Search 8
Google Translate 38
GPS 3, 13–14, 114
potential errors 120
guardian mode 139
Guerry, André-Michel 143–4
gun crime 158
Hamm, John 99
Hammond, Philip 115
Harkness, Timandra 105–6
Harvard researchers experiment (2013) 88–9
healthcare
common goal 111–12
exhibition (1884) 107
linking of medical records 102–3
sparse and disconnected dataset 103
healthcare data 105
Hinton, Geoffrey 86
Hippocrates 80
Hofstadter, Douglas 189–90, 194
home cooks 30–1
homosexuality 22
hotspots, crime 148, 149, 150–1, 155
Hugo, Christoph von 124–5
human characteristics, study of 107
human genome 108, 110
human intuition 71–4, 77, 122
humans
and algorithms
opposite skills to 139
prediction 22, 59–61, 62–5
struggle between 20–4
understanding the human mind 6
domination by machines 5–6
vs machines 59–61, 62–4
power of veto 19
PredPol (PREDictive POLicing) 153–4
strengths of 139
weaknesses of 139
Humby, Clive 26, 27, 28
Hume, David 184–5
HunchLab 157–8
Huntington’s disease 110
IBM 97–8 see also Deep Blue
Ibrahim, Rahinah 197–8
Idaho Department of Health and Welfare
budget tool 16
arbitrary numbers 16–17
bugs and errors 17
Excel spreadsheet 17
legally unconstitutional 17
naive trust 17–18
random results 17
cuts to Medicaid assistance 16–17
Medicaid team 17
secrecy of software 17
Illinois prisons 55, 56
image recognition 11, 84–7, 211n13
inferred data 32–4, 35
personality traits 40
Innocence Project 164
Instagram 36
insurance 30–1
genetic tests for Huntington’s disease 110
life insurance stipulations 109
unavailability for obese patients 106
intelligence tracking prevention 47
internet browsing history 36–8
anonymous 36, 37
de-anonymizing 37–8
personal identifiers 37–8
sale of 36–7
Internet Movie Database (IMDb) 181–2
intuition see human intuition
jay-walking 129
Jemaah Islam 198
Jemaah Islamiyah 198
Jennings, Ken 97–8
Jeopardy (TV show) 97–9
John Carter (film) 180
Johnson, Richard 50, 51
Jones Beach 1
Jones, Robert 13–14
judges
anchoring effect 73
bail, factors for consideration 73
decision-making
consistency in 51
contradictions in 52–3
differences in 52
discretion in 53
unbiased 77
discrimination and bias 70–1, 75
intuition and considered thought 72
lawyers’ preference over algorithms 76–7
vs machines 59–61
offenders’ preference over algorithms 76
perpetuation of bias 73
sentencing 53–4, 63
use of algorithms 63, 64
Weber’s Law 74–5
Jukebox 192
junk algorithms 200
Just Noticeable Difference 74
justice 49–78
algorithms and 54–6
justification for 77
appeals process 51
Brixton riots 49–51
by country
Australia 53
Canada 54
England 54
Ireland 54
Scotland 54
United States 53, 54
Wales 54
discretion of judges 53
discrimination 70–1
humans vs machines 59–61, 62–4
hypothetical cases (UK research) 52–3
defendants appearing twice 52–3
differences in judgement 52, 53
hypothetical cases (US research) 51–2
differences in judgements 52
differences in sentencing 52
inherent injustice 77
machine bias 65–71
maximum terms 54
purpose of 77–8
re-offending 54, 55
reasonable doubt 51
rehabilitation 55
risk-assessment algorithms 56
sentencing
consistency in 51
mitigating factors in 53
substantial grounds 51
Kadoodle 15–16
Kahneman, Daniel 72
Kanevsky, Dr Jonathan 93, 95
kangaroos 128
Kant, Immanuel 185
Kasparov, Gary 5–7, 202
Kelly, Frank 87
Kerner, Winifred 188–9
Kernighan, Brian x
Killingbeck 145, 146
Larson, Steve 188–9
lasers 119–20
Leibniz, Gottfried 184
Leroi, Armand 186, 192–3
level 0 (driverless technology) 131
level 1 (driverless technology) 131
level 2 (driverless technology) 131, 136
careful attention 134–5
level 3 (driverless technology) 131
technical challenge 136
level 4 (driverless technology) 131
level 5 (driverless technology) 131
Li Yingyun 45
Lickel, Charles 97–8
LiDAR (Light Detection and Ranging) 119–20
life insurance 109
‘Lockdown’ (52Metro) 177
logic 8
logical instructions 8
London Bridge 172
London School of Economics (LSE) 129
Loomis, Eric 217n38
Los Angeles Police Department 152, 155
Lucas, Teghan 161–2, 163
machine-learning algorithms 10–11
neural networks 85–6
random forests 58–9
machines
art and 194
bias in 65–71
diagnostic 98–101, 110–11
domination of humans 5–6
vs humans 59–61, 62–4
paradoxical relationship with 22–3
recognising images 84–7
superior judgement of 16
symbolic dominance over humans 5–6
Magic Test 200
magical illusions 18
mammogram screenings 94, 96
manipulation 39–44
micro-manipulation 42–4
Maple, Jack 147–50
Marx, Gary 173
mastectomies 83, 84, 92, 94
maternity wards, deaths on 81
mathematical certainty 68
mathematical objects 8
McGrayne, Sharon Bertsch 122
mechanized weaving machines 2
Medicaid assistance 16–17
medical conditions, algorithms for 96–7
medical records 102–7
benefits of algorithms 106
DeepMind 104–5
disconnected 102–3
misuse of data 106
privacy 105–7
medicine 79–112
in ancient times 80
cancer diagnoses study 79–80
complexity of 103–4
diabetic retinopathy 96
diagnostic machines 98–101, 110–11
choosing between individuals and the population 111
in fifteenth-century China 81
Hippocrates and 80
magic and 80
medical records 102–6
neural networks 85–6, 95, 96, 219–20n11
in nineteenth-century Europe 81
pathology 79, 82–3
patterns in data 79–81
predicting dementia 90–2
scientific base 80 see also Watson (IBM computer)
Meehl, Paul 21–2
MegaFace challenge 168–9
Mercedes 125–6
microprocessors x
Millgarth 145, 146
Mills, Tamara 101–2, 103
MIT Technology Review 101
modern inventions 2
Moses, Robert 1
movies see films
music 176–80
choosing 176–8
diversity of charts 186
emotion and 189
genetic algorithms 191–2
hip hop 186
piano experiment 188–90
algorithm 188, 189–91
popularity 177, 178
quality 179, 180
terrible, success of 178–9
Music Lab 176–7, 179, 180
Musk, Elon 138
MyHeritage 110
National Geographic Genographic project 110
National Highway Traffic Safety Administration 135
Navlab 117
Netflix 8, 188
random forests 59
neural networks 85–6, 95, 119, 202, 219–20n11
driverless cars 117–18
in facial recognition 166–7
predicting performances of films 183
New England Journal of Medicine 94
New York City subway crime 147–50
anti-social behaviour 149
fare evasion 149
hotspots 148, 149
New York Police Department (NYPD) 172
New York Times 116
Newman, Paul 127–8, 130
NHS (National Health Service)
computer virus in hospitals 105
data security record 105
fax machines 103
linking of healthcare records 102–3
paper records 103
prioritization of non-smokers for operations 106
nuclear war 18–19
Nun Study 90–2
obesity 106
OK Cupid 9
Ontario 169–70
openworm project 13
Operation Lynx 145–7
fingerprints 145
overruling algorithms
correctly 19–20
incorrectly 20–1
Oxbotica 127
Palantir Technologies 31
Paris Auto Show (2016) 124–5
parole 54–5
Burgess’s forecasting power 55–6
violation of 55–6
passport officers 161, 164
PathAI 82
pathologists 82
vs algorithms 88
breast cancer research on corpses 92–3
correct diagnoses 83
differences of opinion 83–4
diagnosing cancerous tumours 90
sensitivity and 88
specificity and 88
pathology 79, 82
and biology 82–3
patterns in data 79–81, 103, 108
payday lenders 35
personality traits 39
advertising and 40–1
inferred by algorithm 40
research on 39–40
Petrov, Stanislav 18–19
piano experiment 188–90
pigeons 79–80
Pomerleau, Dean 118–19
popularity 177, 178, 179, 183–4
power 5–24
blind faith in algorithms 13–16
overruling algorithms 19–21
struggle between humans and algorithms 20–4
trusting algorithms 16–19
power of veto 19
Pratt, Gill 137
precision in justice 53
prediction
accuracy of 66, 67, 68
algorithms vs humans 22, 59–61, 62–5
Burgess 55–6
of crime
burglary 150–1
HunchLab algorithm 157–8
PredPol algorithm 152–7, 158
risk factor 152
Strategic Subject List algorithm 158
decision trees 56–8
dementia 90–2
development of abnormalities 87, 95
homicide 62
of personality 39–42
of popularity 177, 178, 179, 180, 183–4
powers of 92–6
of pregnancy 29–30
re-offending criminals 55–6
recidivism 62, 63–4, 65
of successful films 180–1, 182–3, 183
superiority of algorithms 22 see also Clinical vs Statistical Prediction (Meehl); neural networks
predictive text 190–1
PredPol (PREDictive POLicing) 152–7, 158, 228–9n27
assessing locations at risk 153–4
cops on the dots 155–6
fall in crime 156
feedback loop 156–7
vs humans, test 153–4
target hardening 154–5
pregnancy prediction 29–30
prescriptive sentencing systems 53, 54
prioritization algorithms 8
prisons
cost of incarceration 61
Illinois 55, 56
reduction in population 61
privacy 170, 172
false sense of 47
issues 25
medical records 105–7
overriding of 107
sale of data 36–9
probabilistic inference 124, 127
probability 8
ProPublica 65–8, 70
quality 179, 180
‘good’
changing nature of 184
defining 184
quantifying 184–8
difficulty of 184
Washington Post experiment 185–6
racial groups
COMPAS algorithm 65–6
rates of arrest 68
radar 119–20
RAND Corporation 158
random forests technique 56–9
rape 141, 142
re-offending 54
prediction of 55–6
social types of inmates 55, 56
recidivism 56, 62, 201
rates 61
risk scores 63–4, 65
regulation of algorithms 173
rehabilitation 55
relationships 9
Republican voters 41
Rhode Island 61
Rio de Janeiro–Galeão International Airport 132
risk scores 63–4, 65
Robinson, Nicholas 49, 50, 50–1, 77
imprisonment 51
Rossmo, Kim 142–3
algorithm 145–7
assessment of 146
bomb factories 147
buffer zone 144
distance decay 144
flexibility of 146
stagnant water pools 146–7
Operation Lynx 145–7
Rotten Tomatoes website 181
Royal Free NHS Trust 222–3n48
contract with DeepMind 104–5
access to full medical histories 104–5
outrage at 104
Rubin’s vase 211n13
rule-based algorithms 10, 11, 85
Rutherford, Adam 110
Safari browser 47
Sainsbury’s 27
Salganik, Matthew 176–7, 178
Schmidt, Eric 28
School Sisters of Notre Dame 90, 91
Science magazine 15
Scunthorpe 2
search engines 14–15
experiment 14–15
Kadoodle 15–16
Semmelweis, Ignaz 81
sensitivity, principle of 87, 87–8
sensors 120
sentencing
algorithms for 62–4
COMPAS 63, 64
considerations for 62–3
consistency in 51
length of 62–3
influencing 73
Weber’s Law 74–5
mitigating factors in 53
prescriptive systems 53, 54
serial offenders 144, 145
serial rapists 141–2
Sesame Credit 45–6, 168
sexual attacks 141–2
shoplifters 170
shopping habits 28, 29, 31
similarity 187
Slash X (bar) 113, 114, 115
smallpox inoculation 81
Snowden, David 90–2
social proof 177–8, 179
Sorensen, Alan 178
Soviet Union
detection of enemy missiles 18
protecting air space 18
retaliatory action 19
specificity, principle of 87, 87–8
speech recognition algorithms 9
Spotify 176, 188
Spotify Discover 188
Sreenivasan, Sameet 181–2
Stammer, Neil 172
Standford University 39–40
STAT website 100
statistics 143
computational 12
modern 107
NYPD 172
Stilgoe, Jack 128–9, 130
Strategic Subject List 158
subway crime see New York City subway crime
supermarkets 26–8
superstores 28–31
Supreme Court of Wisconsin 64, 217n38
swine flu 101–2
Talley, Steve 159, 162, 163–4, 171, 230n47
Target 28–31
analysing unusual data patterns 28–9
expectant mothers 28–9
algorithm 29, 30
coupons 29
justification of policy 30
teenage pregnancy incident 29–30
target hardening 154–5
teenage pregnancy 29–30
Tencent YouTu Lab algorithm 169
Tesco 26–8
Clubcard 26, 27
customers
buying behaviour 26–7
knowledge about 27
loyalty of 26
vouchers 27
online shopping 27–8
‘My Favourites’ feature 27–8
removal of revealing items 28
Tesla 134, 135
autopilot system 138
full autonomy 138
full self-driving hardware 138
Thiel, Peter 31
thinking, ways of 72
Timberlake, Justin 175–6
Timberlake, Justin (artist) 175–6
Tolstoy, Leo 194
TomTom sat-nav 13–14
Toyota 137, 210n13
chauffeur mode 139
guardian mode 139
trolley problem 125–6
true positives 67
Trump election campaign 41, 44
trust 17–18
tumours 90, 93–4
Twain, Mark 193
Twitter 36, 37, 40
filtering 10
Uber
driverless cars 135
human intervention 135
uberPOOL 10
United Kingdom (UK)
database of facial images 168
facial recognition algorithms 161
genetic tests for Huntington’s disease 110
United States of America (USA)
database of facial images 168
facial recognition algorithms 161
life insurance stipulations 109
linking of healthcare records 103
University of California 152
University of Cambridge
research on personality traits 39–40
and advertising 40–1
algorithm 40
personality predictions 40
and Twitter 40
University of Oregon 188–90
University of Texas M. D. Anderson Cancer Center 99–100
University of Washington 168
unmanned vehicles see driverless cars
URLs 37, 38
US National Academy of Sciences 171
Valenti, Jack 181
Vanilla (band) 178–9
The Verge 138
Volvo 128
Autonomous Emergency Braking system 139
Volvo XC90 139–40
voting 39–43
Walmart 171
Walt Disney 180
Warhol, Andy 185
Washington Post 185–6
Waterhouse, Heidi 35
Watson (IBM computer) 101, 106, 201
Bayes’ theorem 122
contesting Jeopardy 98–9
medical genius 99
diagnosis of leukaemia 100
eradication of cancer 99
grand promises 99
motor neurone disease 100
termination of contract 99–100
patterns in data 103
Watts, Duncan 176–7
Waymo 129–30
Waze 23
Weber’s Law 74–5
whistleblowers 42
Williams, Pharrell 192–3
Windows XP 105
Wired 134
World Fair (1939) 116
Xing.com 37
Zaghba, Youssef 172
Zilly, Paul 63–4, 65
Zuckerberg, Mark 2, 25
ZX Spectrum ix