Facebook / Cambridge Analytica Data Scandal
The Facebook-Cambridge Analytica Data Scandal shows a significant breach in user privacy and data misuse. Personal data from millions of Facebook users were collected without proper consent by a third-party app and then shared with Cambridge Analytica for political profiling and targeting, affecting democratic processes, including the 2016 US Presidential election. The main ethical concerns center around lack of users consent, data privacy, and transparency within tech platforms.
It questions the responsibilities of software engineers and corporate decision-makers in making sure user information is protected and demonstrates the need for strict data protection and ethical guidelines in today’s technology-run world.
Apple Slowing iPhones / Batterygate
The “Batterygate” scandal, where Apple was found to be slowing down older iPhone models through software updates, is problematic due to the lack of transparency as well as a possibility that this was used to limit the functionality of older models to encourage users to purchase their newer iPhone models. The main ethical concerns center around transparency, consumer trust, and the manipulation of product lifespan.
While software engineers implemented these changes, the core ethical dilemmas seem rooted in higher-level corporate decisions rather than individual actions. The software functions ultimately caused these older iPhone models to grow slower, however who exactly is at fault for these functions are debatable.
Source: https://www.cbsnews.com/news/apple-iphone-payment-500-million-settlement-what-to-know/
Amazon Recruitment Tool Discrimination
The Amazon recruitment tool bias is another example of unethical AI application in corporate settings. The AI system was designed to screen job applicants but was found to be biased against women, particularly for technical positions, because of its reliance on previous hiring data dominated by male candidates. This case shows ethical problems in algorithm design and implementation, lacking diversity and holding unconscious biases within training datasets.
While software engineers might not have intentionally designed the AI to discriminate, the possible oversight in not adequately addressing or testing for bias compromised both the functions performed by the software and the behavior of the engineers. This situation highlights the ethical necessity for more inclusive and equitable AI systems, emphasizing the importance of transparency, accountability, and corrective measures in AI development and deployment.
Source: https://www.businessinsider.com/amazon-built-ai-to-hire-people-discriminated-against-women-2018-10