Ethics 1

Example #1:

One situation that clearly shows unethical behavior in a software system happened with Robinhood during the GameStop stock surge in 2021. Robinhood is a trading app that became popular by making investing feel simple and fun, especially for new users. The app used bright colors, confetti animations, and constant notifications to encourage frequent trading, turning serious financial decisions into something that felt like a game. When GameStop’s stock began skyrocketing due to retail investors, Robinhood suddenly stopped users from buying the stock. Many lost money, and it appeared the company was protecting large institutional investors instead of regular users. This was unethical because the design encouraged risky behavior and then removed user control at a critical moment. It showed that the team prioritized profit and engagement over transparency and fairness. The incident also damaged user trust and sparked public debate over whether the company truly served its community. It serves as a reminder that financial technology must treat users with honesty and caution, especially when their real money is on the line.

Example #2:

Another example comes from Uber with a tool they created called Greyball. This program, uncovered in 2017, was designed to block law enforcement officials from booking rides in cities where Uber was not authorized to operate. The software analyzed user behavior to identify regulators and showed them fake cars so they could not catch drivers or shut down the service. This was unethical because it was a deliberate attempt to break the law and deceive city governments. Engineers knowingly used their skills to hide illegal actions rather than solve a real problem. It reflects a conscious choice to evade accountability and prioritize rapid growth over compliance and trust. By using technology to outsmart legal oversight, Uber’s leadership and developers showed a willingness to bend the rules for business advantage. This case demonstrates how innovation without integrity can erode public trust and harm relationships with regulators and customers alike.

Example #3:

A third case can be seen with YouTube’s recommendation system. Over time, it became clear that the algorithm often pushed viewers toward extreme or misleading videos because those generated more watch time and ad revenue. Internal reports showed that some employees were aware of the issue, yet leadership continued to focus on engagement rather than user well-being. The result was the spread of misinformation and online radicalization. This was unethical because the system was designed to maximize attention without regard for harm. The problem lay in both the algorithm’s design and management’s decision to ignore its social consequences. By valuing profit over responsibility, YouTube allowed technology to influence beliefs in harmful ways. This situation reveals how algorithmic systems, if left unchecked, can shape public opinion in unintended and dangerous directions. It also highlights the need for stronger accountability and ethical review in large-scale content platforms.