What should AI see? Using the public’s opinion to determine the perception of an AI

Abstract

Deep neural networks (DNN) have made impressive progress in the interpretation of image data so that it is conceivable and to some degree realistic to use them in safety critical applications like automated driving. From an ethical standpoint, the AI algorithm should take into account the vulnerability of objects or subjects on the street that ranges from “not at all”, e.g. the road itself, to “high vulnerability” of pedestrians. One way to take this into account is to define the cost of confusion of one semantic category with another and use cost-based decision rules for the interpretation of probabilities, which are the output of DNNs. However, it is an open problem how to define the cost structure, who should be in charge to do that, and thereby define what AI-algorithms will actually “see”. As one possible answer, we follow a participatory approach and set up an online survey to ask the public to define the cost structure. We present the survey design and the data acquired along with an evaluation that also distinguishes between perspective (car passenger vs. external traffic participant) and gender. Using simulation based F-tests, we find highly significant differences between the groups. These differences have consequences on the reliable detection of pedestrians in a safety critical distance to the self-driving car. We discuss the ethical problems that are related to this approach and also discuss the problems emerging from human–machine interaction through the survey from a psychological point of view. Finally, we include comments from industry leaders in the field of AI safety on the applicability of survey based elements in the design of AI functionalities in automated driving.

A comprehensive AI policy education framework for university teaching and learning

“…Thirdly, as the use of AI in education and assessment becomes more prevalent, it is essential that students understand the principles behind the technology in order to maintain academic integrity and prevent cheating as mentioned previously (Chan, 2023;Cotton et al, 2023). An AI education policy can teach students about the ethical considerations surrounding AI, such as bias and fairness, as well as the potential consequences of using AI in academic contexts.…”

Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method

“…Sentiment reviews are labeled as positive, neutral, or negative, based on visitor stars. 1 and 2 stars are categorized as negative sentiment, 3 stars as neutral sentiment, and 4 and 5 stars as positive [21] [22].…”

Japanese University EFL Student Insights on the Emerging ChatGPT Phenomenon

“…Most research reviewed for this paper agrees that students should not completely rely on text created by GenAI tools. Furthermore, if AI is to be embraced by academic institutions for student use, effort must be put into protecting academic integrity (Chan, 2023;Chan & Hu, 2023;Kanbar, 2023;Lepik, 2023;Shoufan, 2023;Tossell et al, 2023;Van Wyk, 2024). This provides an opportunity for students and educators to consider the ethics of AI in contemporary academia.…”

“…Since 2023, many scholarly articles (Bishop, 2023;Chan, 2023;Chan & Hu, 2023;Cotton et al, 2023;Fitria, 2023;Fyfe, 2023;Kanabar, 2023;Mohammadkarimi, 2023;Price & Sakellarios, 2023;Smolansky et al, 2023;Van Wyk, 2024) have been published concerning ethics and assessing the use of AI-generated writing in student assignments. According to Kanabar (2023), in the spring semester of 2023, it was becoming evident that strategies to prohibit GenAI would not be realistic.…”

“…This is especially practical in the context of EFL. Chan (2023) states that there are different perceptions of what constitutes "plagiarism" between cultures. This is not as simple as multiculturalism however, as illustrated by a study of ten female Australian undergraduate business school students that compounds this by revealing there was not "a single universal ethical perspective" on the use of GenAI amongst any of the participants of similar demographics (Murray & Williams, 2023).…”