Research Blog Survey 1-1

Transcription

1 Research Blog Survey 1-1 Imagine your child requires a life-saving opera;on. You enter the hospital and are confronted with a stark choice. a. Do you take the tradi;onal path with human medical staff, including doctors and nurses, where longterm trials have shown a 90% chance that they will save your child s life? b. Or do you choose the robo;c track, in the factorylike wing of the hospital, tended to by technical specialists and an array of robots, but where similar longterm trials have shown that your child has a 95% chance of survival? 1

2 Research Blog Survey 1-2 How much do you value the in;macy between doctor and pa;ent? a. Very important b. Important c. Neutral d. Not so important e. Meh 2

3 Research Blog Survey 1-3 Let s say your medical treatment goes tragically wrong but there was no human interference. Who would you blame? a. The person who programmed the robot b. The hospital c. The robot d. The other doctors present during the opera;on e. Nobody 3

4 Research Blog Survey 1-4 Let s say that you are having heart problems. Would you trust a diagnosis from a doctor that has 40 years of experience, or would you trust ar;ficial intelligence that uses pazern recogni;on to compare your heart scans, blood test results, etc. to thousands of other pa;ents across the country? a. Doctor b. AI 4

5 Research Blog Survey 1-5 If you were to support robot driven surgeries, what would be your main reason? a. Efficiency b. Robots wouldn t be affected by extraneous factors and wouldn t get ;red if it was a long surgery c. Sanita;on d. Quicker e. Robots could poten;ally catch more than a human eye 5

6 Research Blog Survey 1-6 Discussion ques;on (Answer on piazza) What do you think are the pros and cons about having ar;ficial intelligence in healthcare? 6

7 Ethics of Big Data Peter Danielson Univ. of British Columbia COGS Mar

8 Scien;fic accuracy (vs. hype): Google Flu Trends Manipula;on & Consent in social science (Facebook) in commerce & poli;cs De-iden;fica;on & Big Data Research 8

9 Google Flu Trends Success of GFT lead story in Mayer- Schonberger, V. & Cukier, K. (2013) Big data: A revolution that will transform how we live, work, and think. NY:Houghton Mifflin Harcourt. Cheap and very quick! 9

10 Flu ~ Flu oundantation truct of compare meanimum, nstable ecause oogle s ics are mprove nsumnges in behave most ror is a ( 1, 15)., it canng high rs. The d other ing the d flu ) % ILI Error (% baseline) Google Flu Google Flu + CDC Lagged CDC CDC 0 07/01/09 07/01/10 07/01/ Google Flu Google Flu + CDC Lagged CDC Google estimates more than double CDC estimates Google starts estimating high 100 out of 108 weeks 07/01/12 07/01/13 07/01/09 07/01/10 07/01/11 07/01/12 07/01/13 Data GFT overestimation. GFT overestimated the prevalence of flu in the season and overshot the 10

11 Q1 Which of the following was not a conclusion of the emo;onal contagion experiment conducted via Facebook? A) Nonverbal behavior is not necessary for emo;onal contagion to occur B) Direct interac;on is not necessary for emo;onal contagion to occur C) Exposure to the happiness of others may produce an alone together social comparison effect thereby actually depressing the individuals who view it D) Emo;onal contagion is propor;onal to emo;onal expression rather than the content of a post E) All of the above are true Kevin 11

12 Rate Quiz Ques;on 1 A. Excellent B. Very Good C. Good D. Acceptable E. Poor 12

13 Q2 Could par;cipants opt out of the emo;onal contagion experiment? A. Yes, this is required by the Cornell Univ. s Human Research Protec;on Program. B. Yes, because it involved emo;ons. C. No, because it was conducted by Facebook for internal purposes. D. A & B E. None of the above 13

14 Q2 Could par;cipants opt out of the emo;onal contagion experiment? A. Yes, this is required by the Cornell Univ. s Human Research Protec;on Program. B. Yes, because it involved emo;ons. C. No, because it was conducted by Facebook for internal purposes. D. A & B E. None of the above 14

15 Facebook Experiment Size: big sample and small effect ~ 155,000 per condi;on; 1/10 of 1 % Informed consent And Facebook terms of service (1-800 in US) Opportunity to opt out Editorial Expression of Concern Was experiment legal? Ethical? 15

16 Rules (Canadian Tri Council) If the survey is normally administered as an opera;onal requirement for quality assurance, quality improvement, or for program evalua;on purposes, then it would not require REB review Ar;cle 2.5), because the survey would not be considered research as defined in this policy. 16

17 Consequences And at the end of the day, the actual impact on people in the experiment was the minimal amount to sta;s;cally detect it, Having wrizen and designed this experiment myself, I can tell you that our goal was never to upset anyone. [ ] In hindsight, the research benefits of the paper may not have jus;fied all of this anxiety. (Adam Kramer, lead author) 17

18 Double Standard II: Internal Experiments We no;ced recently that people didn t like it when Facebook experimented with their news feed. Even the FTC is geung involved. But guess what, everybody: if you use the Internet, you re the subject of hundreds of experiments at any given ;me, on every site. That s how websites work. Chris;an Rudder hzp://blog.okcupid.com/index.php/page/2/ 18

19 Two Hello Cupid Experiments Remove Photos Disaster for usage: But Normal Tuesday Responses to 1 st messages up 44% Conversa;ons went deeper Contact details exchanged quicker 19

20 A Casual Experiment/Blog Meta-data on photos AZrac;veness vs. Age 20

21 Q3 According to poli;cal opera;ves, why would a campaign official ask ci;zens, before an elec;on, whether they would walk or drive to poll sta;ons? a) to know whether having a man handing out flyers near the poll sta;on would benefit the campaign. b) to know how many ci;zens own a car, so they can change their automobile policies. c) to get them to think about vo;ng, thereby increasing the chances of actually going to vote. d) to understand the transporta;on behaviours of voters, so that loca;ons of poll sta;ons could be changed for future elec;ons. e) None of the above 21

22 Elec;on Campaigns You don t want your analy;cal efforts to be obvious because voters get creeped out. (Duhigg, Campaigns.. ) What explains what creeps us out? Did Facebook experiment CYO? Target? How elated to moral permissibility? 22

23 Q4 What's the right order of steps in the process that creates habits? 1. Rou;ne 2. Reward 3. Cue A) B) C) D) Irem 23

24 Rate Quiz Ques;on 4 A. Excellent B. Very Good C. Good D. Acceptable E. Poor 24

25 Q5 According to the New York Times Ar;cle on habits, when is a woman most likely to develop new shopping habits? a) Shortly axer the birth of her child b) Shortly axer marriage c) During pregnancy d) Upon post-secondary gradua;on Pavit 25

26 Rate Quiz Ques;on 5 A. Excellent B. Very Good C. Good D. Acceptable E. Poor 26

27 Discussion Would the data driven techniques used by Target be effec;ve against ra;onal agents? Do the use of these techniques undercut ra;onality? 27

28 Ethics Methods Foiled by Big Data 1. No;ce and consent Dilemma of New unforeseen uses for data Flu from search Not feasible to re-consult Or blanket permission for all uses 2. Op;ng out: German Street View blur Target for egging! 28

29 Big Data is Private Data Research Methodology Lack of transparency at Google Flue Insider access at Yahoo, Facebook, OKCupid How check or replicate private data? To make datasets public need to de-iden;fy 29

30 Ethics Methods Foiled by Big Data 3. Anonymiza;on vs. Reiden;fica;on The MassachuseZs Group Insurance Commission had a bright idea back in the mid-1990s it decided to release "anonymized" data on state employees that showed every single hospital visit. The goal was to help researchers, and the state spent ;me removing all obvious iden;fiers such as name, address, and Social Security number. But a graduate student in computer science saw a chance to make a point about the limits of anonymiza;on. 30

31 At the ;me GIC released the data, William Weld, then Governor of MassachuseZs, assured the public that GIC had protected pa;ent privacy by dele;ng iden;fiers. In response, then-graduate student Sweeney started hun;ng for the Governor s hospital records in the GIC data. She knew that Governor Weld resided in Cambridge, MassachuseZs, a city of 54,000 residents and seven ZIP codes. For twenty dollars, she purchased the complete voter rolls from the city of Cambridge, a database containing, among other things, the name, address, ZIP code, birth date, and sex of every voter. By combining this data with the GIC records, Sweeney found Governor Weld with ease. Only six people in Cambridge shared his birth date, only three of them men, and of them, only he lived in his ZIP code. In a theatrical flourish, Dr. Sweeney sent the Governor s health records (which included diagnoses and prescrip;ons) to his office. 31

32 Ethics Methods Foiled by Big Data 3. Anonymiza;on vs. Reiden;fica;on AOL content of searches alone 60 single men, landscapers in Lilburn Ga Ne~lix contest (and other data) In 2000, [Sweeney] showed that 87 percent of all Americans could be uniquely iden;fied using only three bits of informa;on: ZIP code, birthdate, and sex. Cell Phone data even easier. This new subspecialty of computer science, reiden;fica;on science unearths a tension that shakes a founda;onal belief about data privacy: Data can be either useful or perfectly anonymous but never both. (Ohm, 2004, p.1703f) 32

33 Vs. Yakowitz: Tragedy of the Data Commons Benefit of Public Data Sets Risks: Theore;cal? Harm: "The risk of privacy harm from reiden;fica;on is actually significantly lower than many of the everyday risks we take for granted, such as those azendant on throwing out our trash. 33

34 Big Data Research Dilemma We re living through a golden age of behavioral research. It s amazing how much we can figure out about how people think now. (Eric Siegal quoted in Duhigg, How Companies ) WaZs to Rudder But also undercuung ethical basis of behavioral research? 34

35 References Ohm, Paul, Broken Promises of Privacy: Responding to the Surprising Failure of Anonymiza;on (August 13, 2009). UCLA Law Review, Vol. 57, p. 1701, 2010; U of Colorado Law Legal Studies Research Paper No Available at SSRN: hzp://ssrn.com/abstract= Yakowitz, J. (2011). Tragedy of the data commons. Harv. JL & Tech., 25, 1. Mayer-Schonberger, V. & Cukier, K. (2013) Big data: A revolution that will transform how we live, work, and think. NY:Houghton Mifflin Harcourt. Watts, D.J. (2011) Everything Is Obvious: *Once You Know the Answer. Crown Business Rudder, C. (2014) Dataclysm: Who We Are (when we think no one s looking). Crown, 35