Self-Reflection: Business cases

Overview

Now that you have explored how businesses use data in the real world, you can pause for a moment and think about what you are learning. In this self-reflection, you will consider fairness and data use in three example business cases and respond to brief questions with your thoughts.

This self-reflection will help you develop insights into your own learning and prepare you to apply your knowledge of fairness practices to scenarios that represent real-life business case studies. As you answer questions—and come up with questions of your own—you will consider concepts, practices, and principles to help refine your understanding and reinforce your learning. You’ve done the hard work, so make sure to get the most out of it: This reflection will help your knowledge stick!

Case study 1

To improve the effectiveness of its teaching staff, the administration of a high school offered the opportunity for all teachers to participate in a workshop. They were not required to attend; instead, the administration encouraged teachers to sign up. Of the 43 teachers on staff, 19 chose to take the workshop.

At the end of the academic year, the administration collected data on teacher performance for all teachers on staff. The data was collected via student survey. In the survey, students were asked to rank each teacher's effectiveness on a scale of 1 (very poor) to 6 (very good).

The administration compared data on teachers who attended the workshop to data on teachers who did not. The comparison revealed that teachers who attended the workshop had an average score of 4.95, while teachers who did not attend had an average score of 4.22. The administration concluded that the workshop was a success.

Reflection

Consider this scenario:

• What are the examples of fair or unfair practices?
• How could a data analyst correct the unfair practices?

Now, write 2-3 sentences (40-60 words) in response to each of these questions. Type your response in the text box below.

Explain:

Great work reinforcing your learning with a thoughtful self-reflection! This is an example of unfair practice. It is tempting to conclude—as the administration did—that the workshop was a success. However, since the workshop was voluntary and not random, it is not appropriate to infer a causal relationship between attending the workshop and the higher rating.

The workshop might have been effective, but other explanations for the differences in the ratings cannot be ruled out. For example, another explanation could be that the staff volunteering for the workshop were the better, more motivated teachers. This group of teachers would be rated higher whether or not the workshop was effective.

It’s also notable that there is no direct connection between student survey responses and workshop attendance. The data analyst could correct this by asking for the teachers to be selected randomly to participate in the workshop. They could also collect data that measures something more directly related to workshop attendance, such as the success of a technique the teachers learned in that workshop.

Case opinion

Recently, you were presented with cases about data analytics in the real world. One case involved an unfair conclusion about the performance of women who worked at a business. It demonstrated that data can sometimes be true, yet unfair. In addition, it highlighted the importance of asking, "Why?" when reviewing the results of data analysis.

Another example involved data analysts prioritizing fairness and going out of their way to ensure their data was as fair as possible. Because they were working with sensitive and potentially biased health data, they chose to collaborate with social scientists in order to better understand the social context behind that data.

If you need to, return to the video to refresh your understanding of the examples before you continue. Then, discuss the first case and how the analysts at that company could improve their process:

• What could they have done differently to be fairer in their analysis?
• What could have made their conclusion less biased?

Submit two more more paragraphs (100-200 words total). Then, visit the discussion forum to read what other learners have written, and respond to at least two of them with your own thoughts.

Test your knowledge on making fair business decisions

Question 1

What steps do data analysts take to ensure fairness when collecting data? Select all that apply

• Understand the social context

Explain: Considering inclusive sample populations, social context, and self-reported data enable fairness in data collection.

• Clean the data provided

• Use an inclusive sample population

Explain: Considering inclusive sample populations, social context, and self-reported data enable fairness in data collection.

• Include data self-reported by individuals

Explain: Considering inclusive sample populations, social context, and self-reported data enable fairness in data collection.

Question 2

Avens Engineering needs more engineers, so they purchase ads on a job search website. The website’s data reveals that 86% of engineers are men. Based on that number, an analyst decides that men are more likely to be successful applicants, so they target the ads to male job seekers. What should the analyst have done instead?

A. Let Avens Engineering decide which type of applicants to target ads to.

B. Decline to accept ads from Avens Engineering because of fairness concerns.

C. Only show ads for the engineering jobs to women.

D. Make sure their recommendation doesn’t create or reinforce bias

The correct answer is D. Make sure their recommendation doesn’t create or reinforce bias. Explain: They should make sure their recommendation doesn't create or reinforce bias. As a data analyst, it’s important to help create systems that are fair and inclusive to everyone.

Question 3

On a railway line, peak ridership occurs between 7:00 AM and 5:00 PM. The fairness of a passenger survey could be improved by over-sampling data from which group?

A. Female passengers

B. Male passengers

C. Nighttime riders

D. Daytime riders

The correct answer is C. Nighttime riders. Explain: Over-sampling the data from nighttime riders, an under-represented group of passengers, could improve the fairness of the survey. Review this video on understanding data and fairness.

Question 4

A real estate company needs to hire a human resources assistant. The owner asks a data analyst to help them decide where to advertise the job opening. The analyst learns that the majority of human resources professionals are women, validates this finding with research, and targets ads to a women's community college. This is fair because the analyst conducted research to make sure the information about gender breakdown of human resources professionals was accurate. True or False?

A. True

B. False

It is false. This is not fair. Fairness means ensuring that analysis doesn't create or reinforce bias. As a data analyst, it’s important to help create systems that are fair and inclusive to everyone.