Lecture II Ethics, Professionalism and Data Hugh Lawson-Tancred Department of Philosophy

Societal, Ethical and Professional Issues Lecture II Ethics, Professionalism and Data Hugh Lawson-Tancred Department of Philosophy 1

Summary of previous lecture Leviathan and the responses Collection Use Architecture Targeting 2

Collection Data combination and contamination Unlimited storage, synthesis and sharing 3

Hacking All systems can be hacked 4

Data market • Data can be wholesaled as a commodity • Data brokers • Acxiom - files on 10% of the world's population; 1500 data points per subject; in existence since 1969; 2005 Big Brother Awards; one of the biggest companies you've never heard of • Data as a service 5

IoT • 24 billion devices by 2020 • Fewer than 10,000 households can generate 150 million discrete data points every day • Eavesdropping • S2aaS • Machines cannot give consent 6

Recombination leading to deanonymisation • Ohm's law: Data can be either useful or perfectly anonymous but never both • Problem of aggregated microdata • Pseudonymisationand ICO code 7

Use • Netflix seeking to improve on Cinematch • Released 100 million ratings by 20 million users of 27,000 films • Auxiliary information in the form in particular of IMDb public ratings made it possible to determine the identity of the 20 million users with high probability and thus discover their entire Netflix use • Example: First, we can immediately find his political orientation based on his strong opinions about “Power and Terror: Noam Chomsky in Our Times” and “Fahrenheit 9/11.” Strong guesses about his religious views can be made based on his ratings on “Jesus of Nazareth” and “The Gospel of John”. He did not like “Super Size Me” at all; perhaps this implies something about his physical size? Both items that we found with predominantly gay themes, “Bent” and “Queer as folk” were rated one star out of five. He is a cultish follower of “Mystery Science Theater 3000”. This is far from all we found about this one person, but having made our point, we will spare the reader further lurid details. • Netflix pulled the proposed second offering of the prize. 8

State misuse of data: Snowden • Covert collection of data by the state • Constitutional rights • Freedom of information 9

Private misuse of data: NHS/DeepMind • DM was given 1.6 million identifiable personal medical records by the Royal Free NHS trust • They were used to test a smartphone app called Streams • It could help with early detection of kidney injury and the saving of lives • But explicit consent was not given to this use of the data and data was used even for patients with no kidney condition • DM have now set up a separate ethics and society unit 10

Right to be forgotten • Obligation of search engines to censor content: Mosley case • Mario Costeja González sued in May 2014 for the removal of an article published in 1998 about the foreclosure of his house (he had subsequently paid the debt) 11

Architecture • Decisions progressively automated: • Old style: algorithm facilitates, e.g., train ticket purchases • New style: algorithm takes decision on, e.g., mode of transport 12

Ashley Madison • Historical data stored, creating vulnerability to leaks • Need to pay to have wrong data removed • 1,200 Saudi accounts – adultery punishable by death 13

Uber • Uber triggering surge pricing in response to NY Taxi Workers Alliance ban on JFK after Trump muslim ban • Ratings records – bad drivers assigned to complaining riders? 14

Routing • Routes via the advertising billboards of sponsors in GPS systems 15

Facebook voter encouragement • Incentives to vote, but only for likely Democrat voters Yes No 16

Recruitment • Up to 72% of CVs weeded out before human assessment • "Algorithms are, in part, our opinions embedded in code." Harvard Business Review • Algorithms trained on recruitment history - algorithms tend to be conservative • Are Emily and Greg more employable than Lakisha and Jamal? • NBER experiment in July 2003 17

Summarisation • Abstractive summarisation likely to be used for news consumption • But who chooses the summarisation criteria? 18

Profiling • Sexual orientation detection 19

Insurance • Risk pooling • Premium discrimination 20

Outliers • People with nontypical lifestyles are data outliers • Neutral algorithm - could lead to benign or malign intervention 21

Gambling • Casino stops players leaving the table from leaving the building 22

Targeting Cambridge Analytica Brittany Kaiser AleksandrKogan Alexander Nix presenting psychographic advertisements for gun "rights" Carole Cadwalladr Christopher Wylie 23

Benign use of psychographics • Nudge • Disease prediction - Google flu prediction • Use of data to target areas of neglect Richard Thaler Cass Sunstein 24

Factmata • Advertising and hate speech scandal • Helping advertisers to avoid association with hate speech sites 25

Sources of ethical advice Centre for Data Ethics and Innovation Launch announced on 20 November 2018 To be chaired by Roger Taylor from the Open Data Institute 26

Other sources • Royal Statistical Society •Oxford Internet Institute •British Computer Society •DeepMind Ethics & Society •National Data Guardian •Alan Turing Institute •Cabinet Office Data Science Ethical Framework •UK Data Service •Information Commissioner's Office •National Audit Office •UK Statistics Authority 27

Ethical premium 28

Coursework You will be set an assignment requiring you to imagine an online personality analysis website which collects detailed personal data from its users. The assignment will ask ethical questions from the perspective of both the operators of the site and the users. Many thanks for your attention. 29

Lecture II Ethics, Professionalism and Data Hugh Lawson-Tancred Department of Philosophy