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 from the ethical perspective of the business owners who use them.
This week’s discussion will place you into the position of a business owner whose success depends on remaining competitive. You will have the decision-making power to use an algorithm to predict the characteristics and behaviors of your customers so that you can 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. – Facebook, 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Grey literature, technical reports, preprints, patents, working papers, business documents, unpublished works, and newsletters.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 Video: “TikTok is tracks you even when you are not using it!” by Liron Segev via YouTube (13:53).
This video publisher produces investigative content related to computer systems, hacking, privacy, and many other popular issues related to common user experiences. The style of his presentation is intended for entertainment, so it is not presented in a scholarly manner. However, his live demonstrations are revealing and informative. In this video, consider that Tik Tok is not the only app developer that employs the tactics described in this video.
Required Database: “FTC Sues Kochava for Selling Data that Tracks People at Reproductive Health Clinics, Places of Worship, and Other Sensitive Locations” Published August 29, 2022 by the Federal Trade Commission. (3 pages)
This article details the case against Kochava Inc., a data broker service, for selling metadata that can “…reveal people’s visits to reproductive health clinics, places of worship, homeless and domestic violence shelters, and addiction recovery facilities.” This statement is a bit misleading. As you will see when you read the entire article, the data referred to here comes from mobile devices – not from actual people. However, it is possible to co-locate the presence of the mobile device at a specific location (during sleeping hours, for example) that is a known residence of a person or group of people. While imprecise, it refines the advertiser’s ability to focus on the vicinity of a specific consumer or group of consumers.
Thus, the data can reveal the presence of a device (and its user, presumably) at sensitive locations. In one sense, this can be interpreted as an invasion of privacy. In another, it becomes another method for providing consumers with a personalized experience, among other benefits. An interactive illustration of this phenomenon is shown in The New York Times article, “Your Apps Know Where You Were Last Night, and They’re Not Keeping It Secret.”
Required Article: The ethical codes of using OBT (17 pages)
Review “The Ethics of Legal and Ethical Challenges of Online Behavioral Targeting in Advertising“. 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 18 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 (5 pages)
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 he believes 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? (15:00)
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. In addition, consider that the manner of your interaction can be tracked to identify patterns of your engagement that are as unique as your fingerprint or manner of walking.
Required Interaction: What is the fingerprint of your browser? (15:00)
Go to the Electronic Frontier Foundation’s dedicated webpage for checking your browser’s fingerprint. After checking your browser, skim through their page, “How Do trackers Work?”
Optional: Supplemental resources related to OBT and publicly obtained data
Marketoonist – The personalization privacy paradox: This brief article describes and illustrates “Zero party data,” a category of user data that is intended to be different than the third-party metadata described above in OBT. If you are furthering your studies in Marketing, this might be a trend to follow.
Privacy Badger is a browser extension that blocks trackers. Review what it claims to remediate.
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 Facebook 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 content no matter how often she objected to the ad placements through Facebook’s feedback mechanisms. This article will give you a sense of how, despite the incredible capabilities of OBT, it is not “intelligent” enough to know when circumstances in a person’s life have changed.
CBS News: “AI babysitting service Predictim vows to stay online after being blocked by Facebook 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 a problem so unmanageable that the service was suspended. This is relevant in this chapter because it is predicated on the ability of the system to “scrape” social media content about an applicant.
The World Privacy Forum is a research organization on the forefront of examining privacy issues in the digital era.