[student name withheld]
My process of AI prompting
- I based my prompt on the Role-Output-Level-Context-Standard template shown in class: [INSERT PROMPT HERE]
- The essay first generated by the AI did not include sources, so I added a request to include at least three sources.
- The AI originally generated a 5 paragraph essay, so I asked it to reduce the number to 3. I had to do this several times; apparently ChatGPT has trouble counting!
Comparison of human- and AI-generated essays
- While I think both essays have a fairly clear and effective thesis statement, I think the AI one is probably more complex and descriptive, and therefore more interesting.
- I’d say they both did pretty well at defending their claims. However, the AI one’s sources were all messed up (wrong authors, unknown articles, etc.), so it’s hard to know exactly what is backing up the claims.
- I think both essays are organized well and have distinct topic sentences and such. The AI one is quite long in the middle paragraph because I had it elaborate on a few topics that I felt it was missing.
- I think the prose and grammar in both is good. The AI one is probably slightly more articulate, since I did ask it to write as an articulate and wise college student.
- I think the tone is appropriate for both, although the AI one sounds like a student that was trying really hard to sound smart. But nothing wrong with that, I probably just wouldn’t try that hard on an assignment of this scale.
Essay text
Task 1 version
Autonomous vehicles are likely to have a huge impact on the taxi industry in upcoming years. This new technology could drastically change the way that these taxi services are presented to the public, financially and socially, and will also affect the taxi companies in likely a positive way, as autonomous vehicles require less labor costs.
It is quite possible that many people could benefit from self-driving taxis. Once the taxis are fully autonomous, taxi companies will no longer be paying drivers, which will cut down on their labor costs. This could lead to lower fares, which would be extremely helpful for people who otherwise wouldn’t be able to regularly afford taxi services (taxibutler.com). It is also possible that people would feel safer in a car without a driver. Real people are, quite obviously, more likely to be driving while intoxicated, or texting while driving, or else be resigned to the possibility of human error.
Autonomous vehicles, on the other hand, should face none of these dangers. A driverless car also has the potential to make certain groups of people, such as women, LGBTQ members, or minorities feel distinctly safer on their ride, as there is no threat to their safety due to being part of these groups (npr.org). This is not to say that autonomous vehicles are without fault, however. Anything that runs from a computer has the potential to go wrong, whether it be by faulty AI, system glitches, or hacking (trantololaw.com). There is also the fact that autonomous taxis will cause taxi drivers to lose their jobs. This not only affects the taxi drivers themselves, but also the whole social aspect of the industry. Many people enjoy riding in a taxi because they are able to talk with the drivers and learn more about the city they are in (zedcarz.co.uk).
In conclusion, autonomous taxis have the ability to both help and cause problems for greater society. While taxi companies would be paying less for workers and customers could be seeing cheaper fares, taxi drivers would be losing their jobs and customers would not benefit from the knowledge of those workers. Society will shift even farther into the issues we face with humans being entirely reliant on computers and being led away from the distinctly human interactions that built our society.
https://www.taxibutler.com/blog/the-future-of-taxis-a-look-at-the-impact-of-self-driving
https://www.npr.org/2023/08/12/1193612036/driverless-cars-can-now-operate-like-taxis-in-san-francisco-raising-safety-conce
https://www.trantololaw.com/law-firm-blog/car-accidents/5-dangers-driverless-cars
https://zedcarz.co.uk/2021/02/28/how-will-driverless-cars-impact-the-taxi-industry
Task 3 version
[link to notes for this assignment]
The Transformative Impact of Generative AI on the Taxi Industry
Generative AI, with its ability to learn and replicate tasks traditionally performed by humans, has the potential to significantly transform the taxi industry. Specifically, it could lead to the development and widespread adoption of autonomous taxis. These AI-powered vehicles could provide efficient, round-the-clock service, eliminating issues related to driver fatigue or unavailability. Additionally, autonomous taxis could potentially enhance customer safety by reducing the risk of accidents caused by human error, as well as providing a more equitable service experience free from human biases (Abrams, 2016). Utilizing real-time data on traffic conditions, AI can optimize routes to reduce journey times and fuel consumption, thereby enhancing customer satisfaction and environmental sustainability (Shetty, 2020). Moreover, the integration of AI with other technologies like blockchain could streamline the payment process, providing a seamless experience for passengers (Marr, 2018).
The benefits and detriments of this disruption would be unevenly distributed. For customers, the impacts are multifaceted. On the positive side, customers would enjoy improved service availability and potentially lower fares due to reduced operational costs. The convenience of AI taxis could lead to increased mobility, especially for individuals who are unable to drive due to age, disability, or other constraints (Abrams, 2016). However, the transition to AI taxis could also have negative impacts on customers. For instance, individuals who value the personable interactions with drivers may feel isolated or disengaged in an autonomous vehicle. This could particularly affect older adults and others who rely on such interactions for social connection (Marr, 2018). Furthermore, there may be issues of digital divide; those without access to the necessary technology or lacking digital literacy might find it difficult to use AI taxi services, potentially exacerbating social inequalities (Shetty, 2020). Indeed, the advent of autonomous taxis could have specific implications for minority customers. For those who have faced discrimination or bias in traditional taxi services, AI taxis could offer a safer, more equitable alternative. In conventional taxi systems, minority passengers have often reported instances of being denied service or being subjected to differential treatment based on their identity (Ge, Y. et al., 2016). Autonomous vehicles have the potential to eliminate such discriminatory practices, providing a sense of safety and fairness for minority customers.
If the traditional taxi system were to vanish entirely, societal impacts would be profound. The shift to AI taxis could lead to safer roads, as autonomous vehicles could potentially reduce human error-related accidents (Abrams, 2016). Additionally, more efficient mobility could boost productivity and economic growth. However, the loss of jobs in the driving sector could exacerbate income inequality and social unrest, particularly if support for reskilling and job transition is inadequate (Marr, 2018). Furthermore, the disappearance of human-driven taxis could erode the personal touch in transportation. The spontaneous conversations and local insights that taxi drivers often provide would be lost, arguably diminishing the richness of urban life (Shetty, 2020).
– Abrams, R. (2016). Self-Driving Cars May Get Here Before We’re Ready. The New York Times.
– Shetty, S. (2020). Uber’s self-driving cars are a key to its path to profitability. CNBC.
– Marr, B. (2018). How Blockchain Will Transform The Supply Chain And Logistics Industry. Forbes.
– Ge, Y., Knittel, C.R., MacKenzie, D., & Zoepf, S. (2016). Racial and Gender Discrimination in Transportation Network Companies. National Bureau of Economic Research.
– Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research.