Module 4: Discussion: Ethical and Social Issues and Data Mining

Module 4: Discussion

Ethical and Social Issues and Data Mining

Ethical issues with data mining abound. For example, when businesses collect personal or private customer data, questions may arise such as: What can be done with the data/information? Who owns it? How can it be used? When should it be disposed of? Where can it be securely stored? Should data collection consent be required? Can the accuracy of the data be determined?

Data can be thought of as a business currency that is collected for its ever-increasing resale value. And yet, businesses must carefully balance capitalizing on the usage of this dubious currency while navigating a multitude of ethical and/or legally explosive landmines. Understanding the various ethical issues surrounding the use of data mining techniques and its applications is challenging at best.

For your initial post in the discussion forum, review the module lesson, and address the following:

First, share an example from the news media (journals, newspapers, magazine articles, or current news stories, etc.), of an ethical issue posed by data mining techniques and its applications within a specific organization or industry. Be sure to link to the example, and briefly describe the specific ethical concerns and how they are being addressed.

Then, reflect on how you personally define ethics, and share what you think the ethical approach is for the example you shared.

Next, assess what some of the potential ethical problems may be with predictive analytics. Provide specific examples, and share links to examples that support your arguments.

1. Respond to at least two of your peers by reviewing their posts, and explain whether you think the example of ethical considerations they presented should be enforced and if so, explain how (and if not, explain your rationale). Then share your opinion on how far governing bodies should go in restricting the data mining activities of businesses in order to protect private citizens (for example, governance, legislation, oversight, opt-in/opt-out laws, etc.).
2. To successfully complete this assignment, view the Discussion Rubric document.
3. To Comment On:

Response 1:A. According to PMC PubMed Central, data mining poses many ethical issues in the medical field. Digital phenotyping is a process that takes information from devices like smartphones, computer searches, and tablets and uses that information to map out behaviors. They use computer learning along with physiological and biometric data gathered from our devices and apply that information to behavior tests to help with many different aspects of the medical field. The ethical issues come from no one controls the type of data they can collect, when they collect it, and to what depths they are allowed to stoop too in order to gather information. Some of the ways that this is trying to be countered is through HIPPA and other programs for privacy but as shown by Facebook these programs are easy to get around and show no concern for the information gathered.

B. I think it’s fine to pull someone’s shopping habits or viewing habits from the internet, though I think people need to be more careful about what they view on the internet. I don’t like that all of these apps and programs and devices listen to everything we say. They should not be allowed to pull information from phone calls or text messages. Those should be considered private and not for public information. They should never be allowed to record conversations in real life having nothing to do with devices of any sort.

C. Some of the problems with predictive analytics are things like incomplete information. Computers cannot always pick up on moods or sarcasm or emotional statements. They also can’t understand a human’s background and reasons behind some statements or thoughts. Another problem is the range in which some information is collected can be narrow and exclude many more complicated reasons for a person’s behavior. I sadly do not see a realistic way to govern this type of information leaks. It would not matter what rules you applied, there would be no way to enforce it with any great strength and no way to stop it from happening again and again. Our lives now that the technology is here cannot go backwards into safety.

References:

Martinez-Martin, N., Insel, T. R., Dagum, P., Greely, H. T., & Cho, M. K. (2018). Data mining for health: staking out the ethical territory of digital phenotyping. Npj Digital Medicine, 1(1).

https://doi.org/10.1038/s41746-018-0075-8Links to an external site.

Loshin, D. (2021, December 7). 6 challenges of building predictive analytics models. Business Analytics.

https://www.techtarget.com/searchbusinessanalytics…

Response 2: The Cambridge Analytica Scandal

The Cambridge Analytica scandal revealed serious ethical breaches involving the unauthorized harvesting of Facebook user data. The data was used to create detailed psychological profiles to influence voter behavior during significant political events, including the 2016 U.S. presidential election and the Brexit referendum (Confessore, 2018).

The ethical concerning factors were consent, ownership, purpose and use, security and disposal.

Consent: Facebook users’ data was collected without their explicit consent, which raises questions about the transparency and fairness of the data collection methods (Confessore, 2018).

Ownership: Users’ data was exploited by Cambridge Analytica without their informed knowledge or approval, highlighting issues around data ownership and control (UNKNOWN, 2019).

Purpose and Use: The data was used for targeted political manipulation, which many view as an abuse of personal information for purposes beyond what users anticipated (McCallum, 2022).

Security and Disposal: The data’s handling by a third party with questionable ethical practices led to concerns about data security and the adequacy of measures to prevent misuse (Confessore, 2018).

In response to the scandal, Facebook undertook steps to tighten data access policies and improve transparency. The company also faced legal repercussions and public criticism, which led to a broader dialogue on data privacy and the need for stronger regulations (McCallum, 2022). However, ongoing criticisms question the effectiveness and enforcement of these measures.

Ethics involves adhering to principles of right and wrong that guide behavior, focusing on fairness, transparency, respect for individual rights, and accountability. An ethical approach would have involved obtaining explicit, informed consent from users before collecting and utilizing their data. Ensuring transparency about data use and securing the data against misuse would have aligned with respecting user autonomy and privacy.

Ethical Problems with Predictive Analytics could include privacy invasion, discrimination and bias, and also the misuse of data.

Privacy Invasion: Predictive analytics often involves the collection and analysis of extensive personal data, which can lead to significant privacy concerns if not handled with proper consent and safeguards.

Example: Target’s Predictive Analytics Controversy Target’s use of predictive analytics to identify pregnant women based on purchasing behavior led to privacy issues when the company sent targeted advertisements to a teenage girl, revealing her pregnancy before she had disclosed it to her family (HILL, 2022).

Discrimination and Bias: Predictive models can perpetuate existing biases if the training data is skewed, leading to discriminatory outcomes such as biased hiring or loan approval processes.

Example: ProPublica’s Investigation into Predictive Policing ProPublica’s report uncovered that predictive policing algorithms often exhibited bias against minority communities, resulting in disproportionately higher levels of surveillance and policing in these areas (Julia Angwin, 2016).

Data Misuse: Predictive analytics can lead to the misuse of data for unintended purposes, such as manipulating consumer behavior or profiling individuals without their consent.

Example: Facebook and Cambridge Analytica the misuse of data by Cambridge Analytica for political manipulation underscores the risks of predictive analytics being applied unethically. The lack of adequate oversight and control over predictive models can result in significant ethical challenges (Confessore, 2018).

Ethical issues in data mining and predictive analytics revolve around consent, privacy, fairness, and accountability. Addressing these concerns requires robust data governance practices, transparency, and adherence to ethical standards that safeguard individuals’ rights and dignity. Technology advancement is essential for the growth we look for although it is essential to safeguard our privacy and use the data ethically.

Discussion Rubric
Your active participation in the discussion forums is essential to your overall success. Discussion questions are designed to help you make
meaningful connections between the course content and the larger concepts and goals of the course. These discussions offer you the opportunity to
express your own thoughts, ask questions for clarification, and gain insight from your classmates’ responses and instructor’s guidance.
Requirements for Discussion Board Assignments
Students are required to post one (1) initial post due by ​day three (3)​ (unless otherwise noted by the instructor) and to follow up with at
least two (2) response posts by ​day seven (7)​ for each discussion board assignment. Please be sure to post on at least two separate
days.
For your initial post (1), you must do the following:
● Compose a post of one to two paragraphs.
● Take into consideration material such as course content and other discussion boards from the current module and previous modules, when
appropriate. ​(Make sure you are using proper citation methods when referencing scholarly or popular resources.)
For your response posts (2), you must do the following:
● Reply to at least two different classmates outside of your own initial post thread.
● Demonstrate more depth and thought in your responses.
Instructor Feedback:​ This activity uses an integrated rubric in Blackboard.
Critical Elements
Comprehension
Timeliness
Engagement
Writing
(Mechanics)
Satisfactory​(100%)
Develops an initial post with
an organized, clear point of
view or idea using rich and
significant detail.
Submits initial post by day
three (3).
Proficient (85%)
Develops an initial post with
a point of view or idea using
adequate organization and
detail.
Submits initial post one day
late (day four).
Not Evident (0%)
Does not develop an initial
post with an organized point
of view or idea.
Provides relevant response
posts with some explanation
and detail.
Needs Improvement (55%)
Develops an initial post with
a point of view or idea but
with some gaps in
organization and detail.
Submits initial post two or
more days late (day five or
later).
Provides somewhat relevant
response posts with some
explanation and detail.
Provides relevant and
meaningful response posts
with clarifying explanation
and detail.
Writes posts that are easily
understood, clear, and
concise using proper citation
methods where applicable
with no errors in citations.
Writes posts that are easily
understood using proper
citation methods where
applicable with few errors in
citations.
Writes posts that are
understandable using proper
citation methods where
applicable with a number of
errors in citations.
Value
40
Does not submit a post.
10
Provides response posts that
are generic with little
explanation or detail.
30
Writes posts that others are
not able to understand and
does not use proper citation
methods where applicable.
20
Total
100%