MACQUARIE BUSINESS SCHOOL Managament of Data, Analytics & Change (BUSA8030) CASE STUDY Unit Coordinator: Dr Ali ESHRAGHI TOTAL MARK A total of 30% DUE DATE 24/04/2022, 5:00pm 

ABOUT THE ASSIGNMENT This is an open book assignment that examines students knowledge about the learning material as well as their critical thinking and analysis skills in the context of managing data, analytics and change. Students will need to recognise different perspectives to assess evidence-based and data driven decision-making practices and analyse fundamental digital technological needs for the effective implementation of analytics projects. The assignment will have two parts: a case study followed by short-answer questions (10% mark), and a research report (20% mark). Part one will assess students critical and analytical thinking of a given case study as well as their knowledge about the learning materials. In part 2, students will need to present their understanding of a real- world problem in analytics, find and examine a real-world study/case study, and conduct a research for potential solutions to the problem. It is anticipated that the total length of the answers to part 1 and 2 would be around 2,500 words. INCLUSION Part 1 Answer the questions within the space recommended in the answer-sheet. Make sure your answer for each question is properly contextualized and is strongly related to the case study. Make a proper use of a clear language and professional presentation in your answers (e.g., bullet points, tables/diagrams, bold/italic etc.) Part 2 Use the answer-sheet to do the report. The outcome of this task is a research report. Students need to demonstrate their ability to individually conduct a research on ONE of the following topics: 1. Algorithmic bias in analytics-driven decision making, 2. Transparency and explainability in big data analytics models, 3. Ethics in big data analytics decision making. Follow the template provided under Part 2. Report (page four of this document). Students are recommended to use academic articles for this task, however professional articles from other sources are accepted. Your artcile needs to use 7 to 10 references. Each reference should be carefully used and discussed in detail to support your arguments Use of academic databases is encouraged for finding appropriate journal/conference papers. As a guide, use Google Scholar. Students are encouraged to use Macquaries library resources available to assist with conducting a research online and using electronic databases for search for articles (https://www.mq.edu.au/about/campus-services- and-facilities/library/teaching-support/library- support-for-your-students). SUBMISSION Submit answer-sheet online and via Turnitin. LATE SUBMISSION Special considerations must have been received prior to the submission deadline. RUBRIC AND MARKING Criteria Mark (%) Part One: Case Study Each question will have five marks. Each question will be assessed against correctness of the answer in relation to the case study as well as the presentation and clarity of the answers. Answers must be directly addressing the corresponding question. Make use of a clear language and professional presentation in your answers. Required and recommended text for this unit would cover all questions. No need to refer to external sources/articles. 10 Part Two: Research Report Report is within the word limit 1 Abstract: A succinct summary of the 2 report, objectives, and key findings Case Study: A well explained real- 3 world case study that is relevant to the selected topic Referencing style: proper use of 2 references in the text and in the reference list Use of relevant papers/references 1 Research Background: a structured review of the literature, with a sufficient representation of key references related to the questions of interest 4 Alternative Solutions: A critical, relevant, and out of the box thinking and argumentation for the proposed roadmap. 4 Conclusion: A clear summary of the report, and a thorough personal reflection on the trends ahead for the selected topic 3 Total 30 MACQUARIE BUSINESS SCHOOL PART 1: CASE STUDY Jason Lee is the founder and CEO of technology company eJudge, a company based in Singapore that aims to bring the analytics and artificial intelligence into the legal industry. Jason and his team have developed a flagship software program, AutoFair, an artificially intelligent software tool for South East Asian court systems that can predict a defendant's guilt or innocence likelihood. If the person is found guilty, the system can also predict the likelihood that the person reoffends and also recommend the penalty rate. The system is in its early stages, and thus has been designed to only be used for non- criminal offences such as domestic arguments and lost property. Jason originally created AutoFair 1.0 in order to automate data entry and reporting, to standardize decision-making within the criminal justice system, and to bring down human error or bias impacting legal rulings. He found that many courts and legal companies use old information systems or in some cases manual systems to collect, store, and process documents and evidence, which often makes it a lengthy process to restore information about a defendants background and to make a decision about potential penalties for non-criminal offences. Furthermore, Jason found that appeal applications also take a lot of time to be processed, which can disadvantage innocent people as some may not want to go through the trouble of making an appeal application. Thus, initially he was motivated to design an information system that would automate data entry and enhance the speed and efficiency of data collection, storage, and processing and would also help with improving the process for data recovery, reporting and presentation. Years after eJudge was listed on the stock market and was widely used by various legal firms and the court systems, Jason released AutoFair 2.0 which was an expansion to the previous version and essentially was taking advantage of the rise of big data analytics in the legal industry. It incorporated big data from various sources to make prediction about an individuals likelihood of making an offence based on their demographic background as well as social media and other publicly available information. Recently, an investigative journalism report made speculations about benefits of AutoFair 2.0 for the legal systems versus the risks. The report claimed that system might discriminate minorities within the society. The accuracy of the systems prediction was also under question, and that why a judge should rely on a man-made intelligent system than using his or her brain to solve legal disputes or to impose penalties on individuals. Questions 1. Jason is in the process of preparing a response to the media to address the claims made by the journalist. He has asked you to help with this response, and to prepare two benefits of AutoFair 2.0 for the legal systems and two benefits for the individuals/defendants. Discuss each benefit in the context of eJudge (5 marks). 2. Apply the 5 Vs model for big data and discuss what Volume, Veracity, Velocity, and Variety means to the legal system. To answer this question, you need to present your understanding of how AutoFair 2.0 has been able to take advantage of big data analytics (5 marks). PART 2: RESEARCH REPORT Selected Topic: Abstract (max 150 words) Provide an overview of key objectives and findings as well as the sources for your search/number of articles reviewed. Research Background (max 500 words) Review and discuss findings from the literature and past studies. Discuss key challenges in your selected topics, the importance of these challenes for organizations and how such challenges can impact managerial decision making. Alternative solutions (max 500 words) Review and discuss potential solutions to the challenges you identified in the previous section, and how those solutions can help decision makers. Where do you see the industry and academia is going in the future to address these challenges, and what proposals are available for addressing the challenges in your selected topic. A Case Study (max 500 words) Find a case study/real story of an organization that have experienced the challenge in relation to your selected topic, present some details about the organization, discuss how the challenge has impacted them, and what solution(s) they may have considered in addressing the challenge. Conclusion (max 250 words) Provide a succinct and analytical summary of findings and add your personal reflection on the future trends in regard to your selected topic. References (7 to 10 academic/profesional references; not included in word count)