1
|
Aharoni E, Fernandes S, Brady DJ, Alexander C, Criner M, Queen K, Rando J, Nahmias E, Crespo V. Attributions toward artificial agents in a modified Moral Turing Test. Sci Rep 2024; 14:8458. [PMID: 38688951 PMCID: PMC11061136 DOI: 10.1038/s41598-024-58087-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 03/25/2024] [Indexed: 05/02/2024] Open
Abstract
Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24-28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI's moral reasoning as superior in quality to humans' along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans' raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.
Collapse
Affiliation(s)
- Eyal Aharoni
- Department of Psychology, Georgia State University, Atlanta, GA, USA.
- Department of Philosophy, Georgia State University, Atlanta, GA, USA.
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA.
| | | | - Daniel J Brady
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Caelan Alexander
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Michael Criner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Kara Queen
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | | | - Eddy Nahmias
- Department of Philosophy, Georgia State University, Atlanta, GA, USA
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Victor Crespo
- Department of Philosophy, Duke University, Durham, NC, USA
| |
Collapse
|
2
|
Şahin MF, Ateş H, Keleş A, Özcan R, Doğan Ç, Akgül M, Yazıcı CM. Responses of Five Different Artificial Intelligence Chatbots to the Top Searched Queries About Erectile Dysfunction: A Comparative Analysis. J Med Syst 2024; 48:38. [PMID: 38568432 PMCID: PMC10990980 DOI: 10.1007/s10916-024-02056-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
The aim of the study is to evaluate and compare the quality and readability of responses generated by five different artificial intelligence (AI) chatbots-ChatGPT, Bard, Bing, Ernie, and Copilot-to the top searched queries of erectile dysfunction (ED). Google Trends was used to identify ED-related relevant phrases. Each AI chatbot received a specific sequence of 25 frequently searched terms as input. Responses were evaluated using DISCERN, Ensuring Quality Information for Patients (EQIP), and Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) metrics. The top three most frequently searched phrases were "erectile dysfunction cause", "how to erectile dysfunction," and "erectile dysfunction treatment." Zimbabwe, Zambia, and Ghana exhibited the highest level of interest in ED. None of the AI chatbots achieved the necessary degree of readability. However, Bard exhibited significantly higher FKRE and FKGL ratings (p = 0.001), and Copilot achieved better EQIP and DISCERN ratings than the other chatbots (p = 0.001). Bard exhibited the simplest linguistic framework and posed the least challenge in terms of readability and comprehension, and Copilot's text quality on ED was superior to the other chatbots. As new chatbots are introduced, their understandability and text quality increase, providing better guidance to patients.
Collapse
Affiliation(s)
- Mehmet Fatih Şahin
- Faculty of Medicine Department of Urology, Tekirdağ Namık Kemal University, Süleymanpaşa, Tekirdağ, 59020, Turkey.
| | - Hüseyin Ateş
- Faculty of Medicine Department of Urology, Tekirdağ Namık Kemal University, Süleymanpaşa, Tekirdağ, 59020, Turkey
| | - Anıl Keleş
- Faculty of Medicine Department of Urology, Tekirdağ Namık Kemal University, Süleymanpaşa, Tekirdağ, 59020, Turkey
| | - Rıdvan Özcan
- Department of Urology, Bursa State Hospital, Nilüfer, Bursa, 16110, Turkey
| | - Çağrı Doğan
- Faculty of Medicine Department of Urology, Tekirdağ Namık Kemal University, Süleymanpaşa, Tekirdağ, 59020, Turkey
| | - Murat Akgül
- Faculty of Medicine Department of Urology, Tekirdağ Namık Kemal University, Süleymanpaşa, Tekirdağ, 59020, Turkey
| | - Cenk Murat Yazıcı
- Faculty of Medicine Department of Urology, Tekirdağ Namık Kemal University, Süleymanpaşa, Tekirdağ, 59020, Turkey
| |
Collapse
|
3
|
Sony M, Antony J, McDermott O. The Impact of Healthcare 4.0 on the Healthcare Service Quality: A Systematic Literature Review. Hosp Top 2022; 101:288-304. [PMID: 35324390 DOI: 10.1080/00185868.2022.2048220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Healthcare 4.0 is inspired by Industry 4.0 and its application has resulted in a paradigmatic shift in the field of healthcare. However, the impact of this digital revolution in the healthcare system on healthcare service quality is not known. The purpose of this study is to examine the impact of healthcare 4.0 on healthcare service quality. This study used the systematic literature review methodology suggested by Transfield et al. to critically examine 67 articles. The impact of healthcare 4.0 is analyzed in-depth in terms of the interpersonal, technical, environmental, and administrative aspect of healthcare service quality. This study will be useful to hospitals and other stakeholders to understand the impact of healthcare 4.0 on the service quality of health systems. Besides, this study critically analyses the existing literature and identifies research areas in this field and hence will be beneficial to researchers. Though there are few literature reviews in healthcare 4.0, this is the first study to examine the impact of Healthcare 4.0 on healthcare service quality.
Collapse
Affiliation(s)
- Michael Sony
- WITS Business School, University of Witwatersrand, Johannesburg, South Africa
| | - Jiju Antony
- Industrial and Systems Engineering, Khalifa University, Abu Dhabi, UAE
| | - Olivia McDermott
- College of Engineering and Science, National University of Ireland, Gallway, Ireland
| |
Collapse
|
4
|
Boucher EM, Harake NR, Ward HE, Stoeckl SE, Vargas J, Minkel J, Parks AC, Zilca R. Artificially intelligent chatbots in digital mental health interventions: a review. Expert Rev Med Devices 2021; 18:37-49. [PMID: 34872429 DOI: 10.1080/17434440.2021.2013200] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Increasing demand for mental health services and the expanding capabilities of artificial intelligence (AI) in recent years has driven the development of digital mental health interventions (DMHIs). To date, AI-based chatbots have been integrated into DMHIs to support diagnostics and screening, symptom management and behavior change, and content delivery. AREAS COVERED We summarize the current landscape of DMHIs, with a focus on AI-based chatbots. Happify Health's AI chatbot, Anna, serves as a case study for discussion of potential challenges and how these might be addressed, and demonstrates the promise of chatbots as effective, usable, and adoptable within DMHIs. Finally, we discuss ways in which future research can advance the field, addressing topics including perceptions of AI, the impact of individual differences, and implications for privacy and ethics. EXPERT OPINION Our discussion concludes with a speculative viewpoint on the future of AI in DMHIs, including the use of chatbots, the evolution of AI, dynamic mental health systems, hyper-personalization, and human-like intervention delivery.
Collapse
|