Reference no: EM132589532

BUS5PB - Principles of Business Analytics Assignment - La Trobe University, Australia

Assessment - Data Ethics

The assignment focuses on the important topic of data ethics in business analytics.

Task 01 - Conduct independent research and compile a review report on the use of word embeddings in business and its possible ethical issues. Your report should include the following requirements in order:

a) Describe two possible applications of word embedding in business.

Hint: For each application, mention what are the motivations/benefits, how it works, what datasets are involved and its results (if known), etc.

b) Discuss two popular implicit biases that usually occur in word embedding applications and their possible ethical issues.

Hint: Describe each bias, give examples and explain why and how biases occur and may lead to ethical issues.

c) Suggest two most important measures/best practices that you think can be used to alleviate the ethically significant harms of these bias problems. Provide justification of your choices and challenges of implementing these measures.

Hint: Your suggestions should align with the harms that you have discussed in the previous section (question 1b). You may review the lecture slides and select the relevant knowledge points. You may also need to perform research on literature to explain and support your points.

Task 02 - There is a case study provided and you are required to analyse and provide answers to the questions outlined below. You can use lecture material and literature to support your responses. Fred and Tamara, a married couple in their 30's, are applying for a business loan to help them realize their long-held dream of owning and operating their own restaurant. Fred is a highly promising graduate of a prestigious culinary school, and Tamara is an accomplished accountant. They share a strong entrepreneurial desire to be 'their own bosses' and to bring something new and wonderful to their local culinary scene; outside consultants have reviewed their business plan and assured them that they have a very promising and creative restaurant concept and the skills needed to implement it successfully. The consultants tell them they should have no problem getting a loan to get the business off the ground. For evaluating loan applications, Fred and Tamara's local bank loan officer relies on an off-the-shelf software package that synthesizes a wide range of data profiles purchased from hundreds of private data brokers. As a result, it has access to information about Fred and Tamara's lives that goes well beyond what they were asked to disclose on their loan application.

Some of this information is clearly relevant to the application, such as their on-time bill payment history. But a lot of the data used by the system's algorithms is of the sort that no human loan officer would normally think to look at, or have access to-including inferences from their drugstore purchases about their likely medical histories, information from online genetic registries about health risk factors in their extended families, data about the books they read and the movies they watch, and inferences about their racial background. Much of the information is accurate, but some of it is not. A few days after they apply, Fred and Tamara get a call from the loan officer saying their loan was not approved. When they ask why, they are told simply that the loan system rated them as 'moderate-to-high risk.' When they ask for more information, the loan officer says he doesn't have any, and that the software company that built their loan system will not reveal any specifics about the proprietary algorithm or the data sources it draws from, or whether that data was even validated. In fact, they are told, not even the system's designers know how what data led it to reach any particular result; all they can say is that statistically speaking, the system is 'generally' reliable.

Fred and Tamara ask if they can appeal the decision, but they are told that there is no means of appeal, since the system will simply process their application again using the same algorithm and data, and will reach the same result. Provide answers to the questions below based on what we have learnt in the lecture. You may also need to perform research on literature to explain and support your points.

a) What sort of ethically significant benefits could come from banks using a big-data driven system to evaluate loan applications?

b) What ethically significant harms might Fred and Tamara have suffered as a result of their loan denial? Discuss at least three possible ethically significant harms that you think are most important to their significant life interests.

c) Beyond the impacts on Fred and Tamara's lives, what broader harms to society could result from the widespread use of this loan evaluation process?

d) Describe three measures/best practices that you think are most important and/or effective to lessen or prevent those harms. Provide justification of your choices and challenges of implementing these measures.

Hint: your suggestion should align with the harms that you have discussed in the previous sections (questions 2-b and 2-c). You may review the lecture slides and select the relevant knowledge points. You may also need to perform research on literature to explain and support your points.