Technology will lead to a huge change in human life as well as a future closer to automation. Artificial intelligence is not as bulky as the robots of the past, it is more focused on highly skilled work, and many studies have proven that AI can already assist humans in their work, recently the hot ChatGpt, AI can not only solve many issues, it can also help humans to assist in their tasks, for example they can write a lot of code, help to complete papers, and perform question-and-answer sessions with humans to achieve auxiliary functions. Artificial intelligence can be automated by humans feeding them algorithms and data. With artificial intelligence, you could argue that it has created a lot of impossibilities. On the one hand, machines increase human productivity and efficiency. Tesla’s self-driving enables artificial intelligence to help humans achieve their goals. The fusion of data algorithms and intelligence provides data for the city’s network system. The integration of data and intelligence has helped Tesla to track relevant data, but at the same time, the inflexibility and unpredictability of autonomous driving has made the automation prone to accidents. On the other hand, since automated cars are equipped with many miniature sensors, such as sensing systems on wheels, which may involve very private aspects, it provides information for safe traffic organization, accident reduction, and data for accident investigation, but there are some questions worth exploring, such as whether it will lead to centralized storage and processing of data, which will lead to misuse and leakage risks? Will it sell customers’ privacy to third-party companies and thus steal their privacy? It is worth discussing how the data should be used and the data governance of autonomous driving. The responsibility of who should be responsible for accidents caused by motorization and whether the government should be involved are among the issues that need to be discussed.
Division of responsibility for automation issues
Tesla Motors is a good example of a company that has automated driving that has generated a lot of interest from the audience. Through artificial intelligence, it has been able to drive instead of humans on the road. However, due to the technology set up and the data algorithm delivery that needs to be designed through humans, Tesla’s automated cars have also experienced a lot of failures and the frequent crashes of the Tesla company have caused a lot of concern in the society. Automation can assist humans well, but it can also have a lot of problems. On the other hand, since automation can already do a lot of human work, whether artificial intelligence will replace humans has become a hot topic of discussion in society, and at the same time, if artificial intelligence has a lot of product failure problems resulting in some human accidents, such as the car accident problems caused by Tesla’s autopilot, these problems of how should the responsibility be divided?
The article published by the Yousha Law Group points out that the NHTSA investigation may involve Tesla, which also faces some liability, in the allocation and division of responsibility. At the same time, the article mentions that according to the cars currently sold,Tesla has not yet opened its fully self-driving vehicles to the public, and that Tesla has also stated that the function of Autopilot, which is hands-free steering, braking and speeding, cannot be fully dependent on Autopilot and that it requires driver supervision. Although Tesla proved that the driver’s over-reliance is the driver’s responsibility, in the NHTSA statement, Tesla will also face liability and possible fines that will reach $115 million. In addition, the NHTSA will decide to establish federal safety regulations for drivers as well as manufacturers. Liability for the manufacturer includes problems with Autopilot steering, braking, and automatic acceleration that does not allow driver control. Liability for drivers includes excessive reliance on autopilot, drunk driving, and driving violations.
How to distinguish artificial intelligence
Although it is a complex and fragile operation, artificial intelligence can also replace humans to complete, such as the mining industry, an increasing number of companies can not be separated from the automated procedures, with the development of the Internet, machine learning that can be kept up with the large amount of data will be necessary for society (Gary, 2017).
With the rapid development of AI and the reliance of humans on AI, AI can replace human work in some aspects as well, such as automated responses to platforms, AI can already complete a complete conversation with customers, which may replace humans and thus achieve company layoffs, which will lead to human concerns about AI, believing that in some areas, AI has replaced humans. When I go to the drive website to search for some information, this website will automatically pop up a dialog box (see figure 1), for example, about the inventory of Tesla, and ask if he is human, we can build a valid conversation, although it replies that it is human, but when I re-enter this website, it is still a dialog box called Anna pop out, it is difficult to distinguish whether it’s a human or an AI auto-reply.
The combination of algorithms, datafication and artificial intelligence
Artificial intelligence does not completely replace humans, but in many ways it has reshaped the way things are done at the moment, and if it does not work with AI, the company will most likely be replaced. Artificial intelligence has been a hot trend in recent years, and if a company wants to grow in the long run, it must be combined with AI. According to CNBC News (2019), Tesla’s automation has attracted a lot of attention from the community, which has led them to bring in a lot of consumers, potential investors and opportunities. In this article, they think Tesla company is going to received a huge opportunities, and valued $2 trillion market opportunities in 2019. Algorithems and Datafication plays an important role in Artificial Intelligence, Big data analysis, algorithms, and image recognition technologies in cities help inform traffic networks, improve roads through predictive simulation techniques, cloud computing algorithms, and spatial data analysis, and detect traffic road conditions quickly and accurately through algorithms (Kovacova et al., 2022). Visualization, remote sensing technology, and autonomous driving play a large role in intelligent transportation the development of virtual technologies through sensors and algorithms. In Flew’s book, he argues that the data extraction can be precise to understand the positioning and can be searched by the location.
Artificial intelligence cannot achieve complete replacement of humans and accident analysis of artificial intelligence
Tesla has recalled 363,000 cars due to the high risk of the car crashes, model X, model 3, and model Y are the most effective cars due to they rely on the automatic system (Drive, 2023). According the intercept news organisation report, it was reported that due to Tesla’s autopilot, a Tesla model S car caused an eight-vehicle accident on Thanksgiving Day after a sudden lane change as well as braking, which included nine people injured as well as a two-year-old child. After July 2021, full autopilot has led to 273 accidents, with Tesla accounting for 70 percent of crashes involving autopilot.
(Photo form the website: https://theintercept.com/2023/01/10/tesla-crash-footage-autopilot/
via California Highway Patrol)
Through the use of fully automated systems, as well as the case study of Tesla, it is proven that artificial intelligence systems do not solve many of the problems of humans, technology is inflexible as well as can not predict the unexpected situation, in the case of driving like a car, humans can modify the driving plan and route through the number of vehicles, the situation in front, which can reduce many accidents. On the other hand, when it concerns whether technology will threaten human development, the answer is negative. Artificial intelligence can learn about our behaviors and imitate and shape human actions. It is extracting data to get a lot of information rather than thinking instinctively (Flew, 2021). At the same time, many jobs, such as some emotional workers like doctors, caregivers, teachers, AI does not have different solutions for different groups and categories as humans would. Therefore, at this stage, it is impossible for machines to completely replace humans. The development of the machine requires human input data along with algorithms, and without human help, it is difficult for it to evolve itself.
How should privacy issues related to data be governed?
As mentioned in this article automated machines are positioned to interpret people’s behavior. Autonomous driving is equipped with many miniature sensors, from LIDAR to cameras, which can involve very private aspects, such as sensing systems on the axle wheels that monitor whether the car deviates from the GPS system. There are also privacy issues involved in autonomous driving. Will this lead to centralized storage and processing of data, leading to misuse and leakage risks? Data governance for autonomous driving is a question that deserves to be explored.
In Taeihagh and Lim’s (2019) article mentioned that since customers may accept the terms and conditions without complete knowledge, this may lead to privacy issues for customers, and the data may be sold to insurance companies. They may use this information to tickle the customers. Location data is a very serious issue. The U.S. and South Korean governments have established relevant data privacy rules. Additionally, they had mentioned that the U.S. laws and regulations stipulate that all vehicles should protect owners’ information, prohibit manufacturers from abusing customers’ privacy, and prohibit relevant companies from using data that customers are not aware of and accessing it only when it is needed for safety incident investigations. At the same time, the Korean government also established a relevant law where they require anyone to obtain approval from the relevant authorities when using as well as collecting data. The policies of these countries are good for other countries to use as reference.
Humans need to keep learning, innovating and reflecting in order not to be replaced by artificial intelligence
Advancement in technology can enhance human efficiency, for example, artificial intelligence can automatically reply to human messages, it establishes a good connection with humans, as well as help humans answer some questions. In automobiles, automated systems could assist humans in temporarily relieving the fatigue of driving. In terms of car problems and accidents, AI allows humans to quickly scan and identify which systems have problems, thus avoiding many mistakes and helping humans to improve efficiency. In terms of urban network systems, it provides data for urban network systems through the fusion of algorithms and data, for example, it detects whether the city’s roads are congested and whether there are accidents ahead. But at the same time, intelligent detection, can also threaten human privacy, for example, through automated cars, which are equipped with a number of micro-sensors, and the central processing of data as well as storage may be led to the leakage of customer information. The protection of customer privacy requires policies and government governance of the Internet culture. While automation can replace humans in some ways, it is only a complementary role, and the data and algorithms it generates require human assistance. The frequent accidents of automated driving show that automation cannot be fully relied on. However, the reassuring point is that when an automated car is involved in an accident, his responsibility is shared by the driver as well as the company, but the question to consider is whether automated driving can be considered a meaningless operation since one of the reasons for accidents is the inability to accurately identify lane changes of the vehicle ahead, since the responsibility is shared. Automation in the current view is to assist human work, and can not be completely replaced, which will decrease the human many concerns, but on the other hand, humans should continue to innovate and continuously reflect in order not to be surpassed by artificial intelligence to achieve harmonious coexistence.
CNBC News. (2019). Tesla has a ‘great lead’ in this $2 trillion market opportunity: Analyst. https://www.cnbc.com/2019/06/19/tesla-has-a-great-lead-in-this-2-trillion-opportunity-analyst.html
Drive. (2023). Tesla Recalls 363,000 Cars in the US with Dodge Autonomous Tech. https://www.drive.com.au/news/tesla-recalls-us-cars-with-full-self-driving/
Flew, T. (2021).The Evolution of Digital Platforms. Regulating Platforms. Cambridge: Polity, (pp. 79-86).
Gary, E. (2017). Why AI is more important than big data for IoT development. Network world, https://www.proquest.com/docview/1928877316/fulltext/C5FA6C32D1FE4B71PQ/1?accountid=14757
Kovacova, M., Olah, J., Popp, J., & Nica, E. (2022). The Algorithmic Governance of Autonomous Driving Behaviors: Multi-Sensor Data Fusion, Spatial Computing Technologies, and Movement Tracking Tools. Contemporary readings in law and social justice, 14(2), 27-45. http://dx.doi.org.ezproxy.library.sydney.edu.au/10.22381/CRLSJ14220222
The Intercept. (2023). Exclusive: Surveillance footage of Tesla Crash On SF’S Bay Bridge Hours After Elon Musk Announce “Self-Driving” Feature. https://theintercept.com/2023/01/10/tesla-crash-footage-autopilot/
Taeihagh, A., & Lim, H. S. M. (2019). Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport Reviews, 39(1), 103-128. https://doi-org.ezproxy.library.sydney.edu.au/10.1080/01441647.2018.1494640
Who’s At Fault In A Self-Driving Tesla Accident. (2023). https://yoshalawfirm.com/blog/whos-at-fault-in-a-self-driving-tesla-accident/#:~:text=If%20a%20Tesla%20car%20malfunctions,may%20lead%20to%20driver%20responsibility