support@essaywritingexpert.orgEthical Decision Making (EDM) model. The main focus of the report should be on the application of EDM in a specific situation. This undergraduate level report should be provided in a PDF format.
DataBridge Consulting is a technology consulting firm that helps companies adopt new data analytics systems to improve business insights. They were recently hired by Food Haven, a national supermarket chain, to implement a customer data analytics platform. The project timeline was tight, as Food Haven wanted to leverage the new system for upcoming holiday promotions.
Food Haven provided DataBridge with three years of customer purchase data to be integrated into the new platform. The data included names, addresses, demographics, and full purchase histories across Food Haven’s 300 stores. During planning, DataBridge proposed using a third-party data enrichment service to append additional details to customer profiles, such as income levels, number of children, marital status, and other lifestyle indicators. Food Haven agreed this would provide richer insights.
As the project kicked off, deadlines slipped due to data compatibility issues. DataBridge brought on extra staff and implemented overtime hours to get back on track. One new team member was Jeff, a talented developer who previously worked for a social media company. Jeff bragged to coworkers that he knew techniques to scrape additional customer data from public social media profiles to further enrich the data set. The team was hesitant, but they felt pressure from management to deliver results under a tight timeline.
When the system went live, Food Haven’s marketing team was thrilled with the granularity of customer insights available. They could micro-target specific customer segments with tailored promotions and offers. However, IT security at Food Haven soon noticed anomalies in the data that didn’t match their sources. An audit revealed that many customer profiles contained personal social media data that was scraped without consent. There were also concerning inaccuracies – income levels and ages didn’t match control data.
It was uncovered that Jeff had implemented unauthorized web scraping against social media platforms and purchased additional customer lifestyle data from shady brokers. Much of this data was fabricated, but DataBridge analysts didn’t scrutinize it enough in their haste. The CEOs of both companies demanded to know how this data breach occurred and who was responsible.
While DataBridge leadership weighed options on handling the crisis, the developer responsible had left the company two weeks after project completion for a better paying job at an e-commerce company. Food Haven began receiving customer complaints about the inappropriate targeting and accuracy of direct mail promotions. Their legal team determined the incident violated data regulations, which carried steep fines.
DataBridge agreed to pay the fines to avoid legal action. Both companies now realized governance processes were lacking to ensure ethics in data practices. Food Haven hired a Chief Data Ethics Officer and mandated new training on data transparency, consent, and bias avoidance. DataBridge instituted stronger audits for incoming data sources and retrained its analytical staff on ethical practices. They also revised hiring practices to better screen developers for ethics markers rather than just technical skills.
In the months following the data ethics incident, both Food Haven and DataBridge updated policies and training to prevent similar events in the future. However, as data analytics expanded across more of Food Haven’s operations, new ethical dilemmas emerged.
Food Haven wanted to optimize staffing schedules in stores to match customer traffic patterns. DataBridge built a machine learning model to predict customer visits throughout the day and week based on historical data. Store managers were instructed to adjust employee shifts and staffing levels according to the model’s forecasts.
Initially the program succeeded in reducing staffing costs and excess cashier capacity during slow periods. But it soon became apparent that the model’s forecasts were inaccurate on certain holidays and events missing from the historical data. On Australia Day weekend there were extreme staff shortages across stores, creating disruptions.
DataBridge data scientists had not considered potential gaps in the training data. And the stores lacked ability to override the model’s prescribed staffing levels when its forecasts were off. Employees were left stranded during the busy weekend, which sparked morale issues and union grievances.
Next Food Haven asked DataBridge to analyse customer basket data to inform product pricing and promotions. Using an optimization algorithm, the model maximized predicted profits by raising prices on staple items just below competitive thresholds based on captive customer segments.
This profit-driven pricing disproportionately affected low-income shoppers, widening inequality. Long-term and elderly customers on fixed incomes started shopping elsewhere leading to declining loyalty. DataBridge analysts failed to build ethics checks into the optimization model to prevent unfair pricing outcomes.
Meanwhile, IT security had been receiving increased reports of external hacking attempts trying to access Food Haven’s database through employee credentials. Tyler, an intern developer at DataBridge, revealed at a party that he had picked up some tricks for retrieving passwords while an undergraduate by intercepting encrypted traffic on the university network. He bragged he could easily grab customer data, thinking it would impress his friends.
The next week there was a major data breach. Investigations revealed Tyler had set up a machine to intercept customer data packets being transmitted from Food Haven and crack encrypted passwords using a sniffer tool. When confronted, Tyler rationalized that he had done similar things in college without repercussions. The crisis further exposed Food Haven’s data vulnerabilities and caused a damaging public notification of the breach.
As consumer distrust grew, Food Haven’s Chief Ethics Officer engaged an external consultancy to audit its data practices. They implemented stricter governance frameworks for developing responsible AI models, including bias testing and ethical training for data teams. To rebuild public trust, Food Haven voluntarily submitted their AI systems to an ethics standards certification process as a commitment to equitable outcomes.
The incidents illuminated the need for proactive ethical foresight as part of the data analytics process. Food Haven learned ethics could not be an afterthought and had to be ingrained from the start. The lessons helped inspire an industry-wide movement for responsible AI principles and stronger legal protections around consumer data and privacy.
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2024 Assignment 2 – CASE STUDY
3410ICT Assignment 2 Template 2024