Directions: In the question given below, a short paragraph is given. Select the answer choice that provides the correct sentence that completes the passage and is contextually and grammatically correct:
On Friday, Facebook banned Cambridge Analytica (CA). We have been talking about the role CA's uniquely-targeted advertising approach played in the 2016 US presidential election since just after the election. This much-more-recent ban occurred because of a breach of data management protocol (which broadly covers how data are obtained, transferred, and stored) and not because of the way those data were used. An academic researcher ,Aleksandr Kogan obtained the data by asking users to opt-in to an app designed to estimate users' personalities from their pattern of behaviour on Facebook. _______________(A)_______________.
Facebook found out about the break in data management protocol and requested that CA delete the data. CA agreed, but then Facebook found out from a whistle blower that they had lied, and so now CA is banned. _______________(B)_______________. Such prediction and targeting happens every day, anytime you engage in a behaviour that can be linked to your identity, either online, through social media profiles that track individuals across websites by comparing email addresses or site cookies, or in the 'real world', with purchases made at different stores using different bank and credit cards being matched up by credit reporting agencies.
Most of this prediction happens in the background, with consumers rarely thinking about it, and consent for the collection and use of data exists in the fine print of user agreements that most of us click through without thinking. _______________(C)_______________. If a researcher were to infer political orientation by politicians a person supports, we would call that face valid data. That is, the measure (politicians supported) is clearly related to the thing we're trying to predict (political orientation).
What's less intuitive is that most - if not all - of your personal attributes can be guessed (even if imperfectly) by any information that is known about you. Measures do not need to be face valid to provide accurate estimates. If we can establish that one thing is consistently related to another, it doesn't matter if that link is obvious or causal. _______________(D)_______________. This is commonly referred to as an empirical, or bottom-up, or data-driven approach to measurement.
_______________(E)_______________.This is an example of the principle of aggregation: more data is always better, even if some or all of that data is of poor quality. Of course, you need less high-quality data to get the same accuracy of prediction; but if high-quality data might be suspect (for example, concerns about lying in direct, face-valid measures) or just flat out aren't available (for example, in-depth measures of millions of internet users), lots of low-quality data will do just fine.