In the prior chapter, we explored the benefits and risks associated with Online Behavioral Targeting (OBT) and the balance of “living publicly” from the user’s experience of SM and the Internet.
In this chapter, we turn the tables and look at OBT and other advanced technologies from the ethical perspective of the business owners who use them.
This week’s discussion will put you into the position of a business owner whose success depends on remaining competitive. You will have the power to use an algorithm to predict characteristics and behaviors of your customers to gain a competitive advantage. How far would you go given the opportunity to calculate some very intimate details about them?
Open-source intelligence (OSINT): (Excerpted from the Wikipedia entry). Huge collections of data about individuals can be obtained through open-source resources provided the information does not require any type of clandestine collection techniques to obtain it and that it must be obtained through means that entirely meet the copyright and commercial requirements of the vendors were applicable. These open sources include:
- Media: print newspapers, magazines, radio, and television from across and between countries.
- Internet, online publications, blogs, discussion groups, citizen media (i.e. – cell phone videos, and user created content), YouTube, and other social media websites (i.e. – Face-book, Twitter, Instagram, etc.). This source also outpaces a variety of other sources due to its timeliness and ease of access.
- Public Government Data, public government reports, budgets, hearings, telephone directories, press conferences, websites, and speeches. Although this source comes from an official source they are publicly accessible and may be used openly and freely.
- Professional and Academic Publications, information acquired from journals, conferences, symposia, academic papers, dissertations, and theses.
Commercial Data, commercial imagery, financial and industrial assessments, and databases.
Grey literature, technical reports, preprints, patents, working papers, business documents, unpublished works, and newsletters.
An example of an app that draws from OSINT data is the Predictim app whose service evaluates whether the babysitter you intended to hire poses any risks. (See the optional article resource below from CBS News).
What should you be focusing on?
Your objective in this module is:
- Develop a strategic position on the use of OBT from a business person’s perspective which takes into account its ethical ramifications.
Readings & Media
Thematic narrative in this chapter
In the following readings and media, the authors will present the following themes:
- Metadata and tracking systems are constantly improving.
- Even though metadata systems do not collect or report PII, there are ethical issues to consider when businesses use recommender systems that are surprisingly accurate.
Required Database: The privacy cost of free apps
AppCensus is a non-profit research organization that evaluates Android apps to determine what data it collects, how it collects it, and with whom it is shared.
- Type in a search term in the search box at the top (like “sleep” or browse by category) and then explore any random app’s test results. You can see at a glance which apps share sensitive data by the badges next to each item.
- Click on “View Privacy Analysis” to see the results.
- Here is an example for “OK Cupid Dating“. Hover over the boxes to see which sets of data are shared.
The value of this exploration is to compare the variations in privacy settings. These settings are business decisions put in place to achieve a strategic business plan.
Required Article: The ethical codes of using OBT
Review “The ethics of Online Behavioral Targeting (OBT)”. You only need to review the parts about the ethical codes related to OBT. You are free to skim the other parts related to all of the methods of data collection if it would help you with your project. The main focus of this piece begins on page 135 (pg 11 of 22 within the PDF) under the heading “Ethical Analysis of Online Behavioral Targeting and the FTC Principles and Guidelines”
Required Article: The case for banning OBT
Review The New Republic – “Ban Targeted Advertising“. David Dayan makes the case for banning targeted advertising altogether. He suggests that not only does the “surveillance economy” fail to deliver anything meaningful to advertisers but building massive databases of metadata creates an attractive target for hackers. The risks outweigh the perceived benefits.
Dayan argues from both a business perspective and from an ethical perspective. As you read, consider whether Dayan’s commentary is a strong argument for banning OBT, or whether his argument would be weaker if the OBT system had a few tweaks?
Required Interaction: What information is being tracked while you interact online?
Spend a few minutes clicking around ClickClickClick.click to see just how much metadata can be collected about your Web browser behavior as you navigate online. This will give you a sense of how sophisticated Web browser tracking technology has become.
Required Data Sheet: What is in a person’s actual profile data?
Would you like to see what the Internet actually “knows” about you from your online behavior and other data collection? Here is how you find out: Go to https://aboutthedata.com/ and scroll down to the Registration button. Sign up – it’s free!
Optional: Supplemental resources related to OBT and publicly obtained data
The Federal Deposit Insurance Corporation (FDIC) Center for Financial Research describes how metadata from one’s digital footprint is being used as a basis of determining credit worthiness. “On the Rise of the FinTechs—Credit Scoring Using Digital Footprints” includes the following statements:
The growth of the internet leaves a trace of simple, easily accessible information about almost every individual worldwide – a trace that we label “digital footprint”. Even without writing text about oneself, uploading financial information, or providing friendship or social network data, the simple act of accessing or registering on a webpage leaves valuable information. As a simple example, every website can effortlessly track whether a customer is using an iOS or an Android device; or track whether a customer comes to the website via a search engine or a click on a paid ad. In this project, we seek to understand whether the digital footprint helps augment information traditionally considered to be important for default prediction and whether it can be used for the prediction of consumer payment behavior and defaults.
…Using more than 250,000 observations, we show that even simple, easily accessible variables from the digital footprint equal or exceed the information content of credit bureau scores. Furthermore, the discriminatory power for unscorable customers is very similar to that of scorable customers. Our results have potentially wide implications for financial intermediaries’ business models, for access to credit for the unbanked, and for the behavior of consumers, firms, and regulators in the digital sphere.
BBC News: “Bereaved mother criticises Face-book over baby ads“. This article describes how a woman who suffered the loss of a stillborn birth continued to receive targeted ads for baby products and related articles no matter how often she objected to the ad placements through Face-book’s feedback mechanisms. This article will give you a sense of how, despite the incredible capabilities of OBT, it is not good enough to know when circumstances have changed.
CBS News: “AI babysitting service Predictim vows to stay online after being blocked by Face-book and Twitter“. Find out why Predictim, an app designed to evaluate the risk of the babysitter you intended to hire (which sounds like a good idea), turned into such a huge problem that the service was suspended. This is relevant because it is predicated on the ability of the system to “scrape” social media content about an applicant.