Question 1-

Once, only hairdressers and bartenders knew people's secrets. Now, smart phones know everything - where people go, what they search for, what they buy, what they do for fun and when they go to bed. Drawbridge is one of several start-ups that have figured out how to follow people without cookies, and to determine that a cell phone, work computer, home computer and tablet belong to the same person, even if the devices are in no way connected. Drawbridge is one of several start-ups that have figured out how to follow people without cookies, and to determine that a cell phone, work computer, home computer and tablet belong to the same person, even if the devices are in no way connected. If you research a Hawaiian vacation on your work desktop, you could see a Hawaii ad that night on your personal cell phone. Drawbridge watches the notifications for behavioral patterns and uses statistical modeling to determine the probability that several devices have the same owner and to assign that person an anonymous identifier.

So if someone regularly checks a news app on a phone in bed each morning, browses the same news site from a laptop in the kitchen, visits from that laptop at an office an hour later and returns that night on a tablet in the same home, Drawbridge concludes that those devices belong to the same person. And if that person shopped for airplane tickets from a work computer, Drawbridge could show that person an airline ad on the tablet that evening. The company's pinpointing was so accurate that it could show spouses different, personalized ads on a tablet they share. Numerous companies that most people have never heard of - like Drawbridge, Flurry, Velti and SessionM - are now monitoring our behaviors and helping marketers and advertisers.

Debate the ethics of this form of customer monitoring and targeted advertising, which is part of Customer Relationship Management - describe in detail whether you think it is ethical for companies to use such practices. Justify your position.

Question 2-

Redlining is an old practice that involves denial of service, or the offering of different services, at institutions like banks or healthcare companies based on a profile the customer might fit, such as if they are black or a woman or live in a low-income neighborhood. Redlining specifically prohibits companies from differing their offers to potential customers based on what they know about them. For instance, they can't offer a loan at six percent interest to someone who lives in Tonyville, CA and then turn around and offer to same loan at 12 percent interest to someone who lives in Boondoggle, TX.

Kate Crawford, a principal at Microsoft Research, cites examples that fall into the barely-regulated end of the spectrum: a WebMD search about breast cancer plus a book you buy from Amazon about cancer survival, yields you unable to get health insurance or approved for a loan. Why? Data indicates you might possibly be dead soon. It doesn't matter so much that the breast cancer search and book were out of curiosity and generosity, respectively, for your dying great aunt who has never touched the Internet. As Crawford points out, personal data collected and resold is not subject to HIPAA, or very much privacy protection at all. Crawford cites the infamous example of a woman whom Target determined was pregnant before her family did, saying it "will simply look quaint compared to what's coming down the pipeline."

Consider Google. Google has been collecting huge swaths of data from users - even if you've never registered an account. Search Google and your records are kept indeterminately. Send an email and Google will process the contents for advertising and marketing purposes. Google isn't alone. A recent study at Cambridge University looking at almost 60,000 people's Facebook "likes" was able to predict with high degrees of accuracy their gender, race, sexual orientation and even a tendency to drink excessively. The mathematical model could tell a gay man from a straight man correctly 88% of the time and predict race with 95% accuracy, for example.

Government agencies, employers or landlords obtain such data and analyze them to make decisions about you, Crawford warns. A lender, for example, who didn't want borrowers of a certain race could show online offers only to people whose social network activity fits certain parameters. Banks must report detailed statistics about their actual lending activity to regulators, but web advertising parameters are seemingly free of discrimination. By never putting offers in front of unwanted groups, and thus never formally rejecting them, those who engage in online discrimination could sidestep fair lending and redlining laws that apply in the physical world. "It's not that big data is effectively discriminating -- it is, we know that it is," says Crawford. "It's that you will never actually know what those discriminations are." She says, "If I predict something about you and I'm right, that can be just as dangerous as if I predict something about you and I am wrong."

Databases are now combining a vast array of different sources - everything from the output of mobile apps and Web searches to radio tags on items bought at a store and phone-location trackers. Even data scrubbed to remove personal references can be reconnected to individuals. Cell phone carriers are selling collections of data about phone movements, for instance, with all personal details removed. But a group of researchers from MIT, the Universite Catholique de Louvain in Belgium and other institutions looked at one such collection and were able to pinpoint 95% of the unique users by analyzing lust four GPS time, and location stamps per person. Several years ago, researchers at Carnegie Mellon University were able to create a system to uncover Social Security numbers from birthday and hometown information listed on social networking sites like Facebook. Companies are very likely to use such methods to pinpoint and target individuals in their business operations.

Question:

Such methods of data mining are part of Customer Relationship Management. Describe in detail whether you think it is ethical for companies to use such practices.