Published By: BBC News, 12/5/2016
Uber has acquired an artificial intelligence (AI) startup, Geometric Intelligence, and put the team to work on (among other things) self-driving cars. The company already uses machine learning to predict when and where cars will be needed, but self-driving cars would be a significant boon for company profits — albeit at the expense of existing drivers working with Uber.
Extended Discussion Questions
- The article points out that AI-driven vehicles would put current Uber drivers out of work.
- The Uber CEO is quoted as saying that new jobs will open up to replace those lost jobs. Do you agree with his optimism? What kinds of jobs might those be?
- What are some things we could do as a society to prepare for future job losses due to automation?
- The leader of the new AI team is quoted as saying that their approach uses pre-programmed logic as well as statistical modeling because they want to achieve as close to 100% reliability as possible.
- Will there ever be a percentage of error that is acceptable for self-driving cars?
- At what point should we allow them on the roads — as soon as they make fewer errors on average than humans, or should we impose a higher standard? Why or why not?
- Assuming AI-driven cars could reach 100% reliability (or very near it), do you think AI-driven vehicles should entirely replace human-operated vehicles? Why or why not?
- If an AI-driven car does make an error that causes harm to a human, who should be held responsible? Should there be legal consequences?
Relating This Story to the CSP Curriculum Framework
Global Impact Learning Objectives:
- LO 7.3.1 Analyze the beneficial and harmful effects of computing.
- LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.
Global Impact Essential Knowledge:
- EK 7.3.1A Innovations enabled by computing raise legal and ethical concerns.
Other CSP Big Ideas:
- Idea 4 Algorithms
Banner Image: “Network Visualization – Violet – Offset Crop”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0