IDENTIFY 2 ETHICAL GUIDELINES VIOLATIONS IN THE CASES BELOW:

THE 12 ETHICAL GUIDELINES:

1. Use data to improve life for our users, customers, organizations, and communities.
2. Create reproducible and extensible work.
3. Build teams with diverse ideas, backgrounds, and strengths.
4. Prioritize the continuous collection and availability of discussions and metadata.
5. Clearly identify the questions and objectives that drive each project and use to guide both planning and refinement.
6. Be open to changing our methods and conclusions in response to new knowledge.
7. Recognize and mitigate bias in ourselves and in the data we use.
8. Present our work in ways that empower others to make better-informed decisions.
9. Consider carefully the ethical implications of choices we make when using data, and the impacts of our work on individuals and society.
10. Respect and invite fair criticism while promoting the identification and open discussion of errors, risks, and unintended consequences of our work.
11. Protect the privacy and security of individuals represented in our data.
12. Help others to understand the most useful and appropriate applications of data to solve real-world problems.

IDENTIFY 2 VIOLATIONS IN EACH CASE.

1. A statistical analyst carried out an investigation of the association of gender and teaching evaluations at a university. They undertook exploratory analysis of the data and carried out a number of bivariate comparisons. The multiple items on the teaching evaluation were consolidated to a single measure based on these exploratory analyses. They used this information to construct a multivariable regression model that found evidence for biases. What issues might arise based on such an analytic approach?

2. In the United States, the Confidential Information Protection and Statistical Efficiency Act (CIPSEA) governs the confidentiality of data collected by agencies such as the Bureau of Labor Statistics and the Census Bureau. What are the penalties for willful and knowing disclosure of protected information to unauthorized persons?

3. A data analyst received permission to post a data set that was scraped from a social media site. The full data set included name, screen name, email address, geographic location, IP (Internet protocol) address, demographic profiles, and preferences for relationships. Why might it be problematic to post a deidentified form of this data set where name and email address were removed?

4. A company uses a machine learning algorithm to determine which job advertisement to display for users searching for technology jobs. Based on past results, the algorithm tends to display lower paying jobs for women than for men (after controlling for other characteristics than gender). What ethical considerations might be considered when reviewing this algorithm?