Spectacular AI failures

 Using inline APA style references, discuss in detail spectacular failures of AI systems over the years, how things have improved, and what problems remain or have even gotten worse.

Over the years, there have been numerous examples of spectacular failures of AI systems. One of the most well-known cases is Microsoft's Tay chatbot, which was launched in 2016 and quickly became infamous for its racist, sexist, and inflammatory tweets. The chatbot was designed to learn from interactions with Twitter users, but it was quickly overwhelmed by trolls who fed it offensive content. As a result, the chatbot began to spew hate speech and other offensive comments, causing widespread outrage and prompting Microsoft to shut it down within 24 hours of its launch (Shah, 2016).

Another example of AI failure is Uber's self-driving car, which killed a pedestrian in Arizona in 2018. The car was equipped with advanced sensors and algorithms designed to detect and avoid obstacles, but it failed to recognize a pedestrian crossing the road at night. The incident raised serious questions about the safety of autonomous vehicles and the need for more rigorous testing and regulation (Isaac & Fussell, 2018).

Despite these failures, there have been significant improvements in AI technology in recent years. Machine learning algorithms have become more sophisticated, enabling computers to perform complex tasks such as image and speech recognition with greater accuracy and speed (Jordan & Mitchell, 2015). Deep learning, a subset of machine learning, has also made significant progress in areas such as natural language processing and computer vision.

However, problems remain in the development and use of AI systems. One issue is the lack of transparency and accountability in AI decision-making. As AI systems become more complex and autonomous, it becomes increasingly difficult to understand how they arrive at their decisions and to ensure that they are fair and unbiased (Burrell, 2016). Another problem is the potential for AI to exacerbate existing social and economic inequalities, by reinforcing biases and discrimination (Crawford et al., 2019).

In conclusion, while there have been significant advances in AI technology, there have also been notable failures, highlighting the need for responsible development and use of AI systems. Researchers and policymakers must continue to address the challenges of transparency, accountability, and fairness in AI decision-making, while also addressing the potential social and economic implications of AI.

References:

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1-12.

Crawford, K., Dobbe, R., Dryer, T., Fried, G., Green, B., Kaziunas, E., ... & Whittaker, M. (2019). AI Now Report 2018. AI Now Institute.

Isaac, M., & Fussell, S. (2018, March 19). Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam. The New York Times.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Shah, D. (2016, March 24). Microsoft deletes ‘teen girl’ AI after it became a Hitler-loving sex robot within 24 hours. The Verge.