Drivers behind the public perception of artificial intelligence: insights from major Australian cities

Artificial Intelligence Augmented Qualitative Analysis: The Way of the Future?

The artificial intelligence (AI) revolution is here and gathering momentum, thanks to new models of natural language processing (NLP) and rapidly increasing adoption by the public. NLP technology uses statistical analysis of language structures to analyse and generate human language, using text or speech as its source material. It can also be applied to visual mediums like images and videos. A few qualitative research early adopters are beginning to adopt this technology into their work, but our understanding of its potential remains in its infancy. This article will define and describe NLP-based AI and discuss its benefits and limitations for reflexive thematic analysis in health research. While there are many platforms available, ChatGPT is the most well-known and accessible. A worked example using ChatGPT to augment reflexive thematic analysis is provided to illustrate potential application in practice. This article is intended to inspire further conversation around the role of AI in qualitative research and offer practical guidance for researchers seeking to adopt this technology.

Existential anxiety about artificial intelligence (AI)- is it the end of humanity era or a new chapter in the human revolution: questionnaire-based observational study

Background

Existential anxiety can profoundly affect an individual, influencing their perceptions, behaviours, sense of well-being, academic performance, and decisions. Integrating artificial intelligence into society has elicited complex public reactions, marked by appreciation and concern, with its acceptance varying across demographics and influenced by factors such as age, gender, and prior AI experiences. This study aimed to investigate the existential anxiety about artificial intelligence (AI) in public in Saudi Arabia.

Methods

The present questionnaire-based observational, analytical cross-sectional study with a structured, self-administered survey was conducted via Google Forms, using a scale to assess the existential anxiety levels induced by the recent development of AI. The study encompassed a diverse population with a sample size of 300 participants.

Results

This study's findings revealed a high prevalence of existential anxieties related to the rapid advancements in AI. Key concerns included the fear of death (96% of participants), fate's unpredictability (86.3%), a sense of emptiness (79%), anxiety about meaninglessness (92.7%), guilt over potential AI-related catastrophes (87.7%), and fear of condemnation due to ethical dilemmas in AI (93%), highlighting widespread apprehensions about humanity's future in an AI-dominated era.

Conclusion

The public has concerns including unpredictability, a sense of emptiness, anxiety, guilt over potential AI-related catastrophes, and fear of condemnation due to ethical dilemmas in AI, highlighting widespread apprehensions about humanity's future in an AI-dominated era. The results indicate that there is a need for a multidisciplinary strategy to address the existential anxieties in the AI era. The strategic approach must blend technological advancements with psychological, philosophical, and ethical insights, underscoring the significance of human values in an increasingly technology-driven world.

Clearing the way for participatory data stewardship in artificial intelligence development: a mixed methods approach

Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers' willingness to provide their data to AI. This mixed-methods study applied the extended Technology Acceptance Model (TAM) with additional predictors of trust and subjective norms. Participants' data donation profile was also measured to assess the influence of individuals' social duty, understanding of the purpose and guilt. Participants (N = 322) completed an experimental survey. Individuals were willing to provide data to AI via PDS when they believed it was their social duty, understood the purpose and trusted AI. However, the TAM may not be a complete model for assessing user willingness. This study establishes that individuals value the importance of trusting and comprehending the broader societal impact of AI when providing their data to AI.Practitioner summary: To build responsible and representative AI, individuals are needed to participate in data stewardship. The factors driving willingness to participate in such methods were studied via an online survey. Trust, social duty and understanding the purpose significantly predicted willingness to provide data to AI via participatory data stewardship.