Algorithm awareness: Why user awareness is critical for personal privacy in the adoption of algorithmic platforms?

Integration of artificial intelligence of things (AIoT) in the public sector: drivers, barriers and future research agenda

“…AI-based applications make decisions based on intelligence by completing tasks using data-learning algorithms or a set of rules or by processing data from which they can learn to produce accurate results. AI can help governments make "smart" and "intelligent" decisions and policies (Wang et al, 2019); however, the algorithms should be designed in such a way that transparency and fairness is assured (Shin et al, 2022a(Shin et al, , 2022b(Shin et al, , 2022c. It can also be used to improve communication with citizens and residents and the speed and quality of public services.…”

“…Given that differential rates of development exist across the globe, it may be worthwhile to study the manner in which developed and developing countries perceive the integration of AIoT in the public sector. Further research may be conducted to assess the user awareness, experience and perceptions of the algorithms (Shin et al, 2022a) because they The present research focused on the public sector, and this provides the lead for conducting further research to understand how the private sector stands to gain by imbibing the AIoT model in its operations. Finally, it may be considered a viable research avenue to probe the extent to which AIoT integration in the public sector might lead to changes in the government-citizen interactions in terms of collaborative and participative governance.…”

Unpacking the effects of personality traits on algorithmic awareness: The mediating role of previous knowledge and moderating role of internet use

“…In the area of algorithmic awareness, building on previous conceptualization of algorithms as experience technologies ( DeVito et al, 2018 ; Cotter and Reisdorf, 2020 ), Swart (2021) defines algorithmic awareness as users’ understanding of the presence and operations of algorithms in the context of news selection on social media. Building on previous discussions on fairness, accountability, and transparency of algorithmic awareness, Shin (2021a) constructs awareness with four subscales: fairness, accountability, transparency, and explainability (FATE) Shin and Park (2019) , Shin (2021b) , Shin et al (2022a) and proposes that FATE can be extended as a basis of algorithmic literacy ( Shin et al, 2021c ; Shin, 2022b ). Specifically, in the area of algorithmic content recommendations, Zarouali et al (2021) constructs the framework of algorithmic media content awareness (AMCA) in four dimensions, including users’ awareness of content filtering, automated decision-making, human-algorithm interplay, and ethical considerations.…”

“…At the individual level, being aware of the operations and impact of algorithms is essential for users to protect their privacy ( Rader, 2014 ), increase their level of autonomy online ( Dogruel et al, 2020 ), diversify the sources of their information and increase their income, which are dependent on acquiring online attention ( Klawitter and Hargittai, 2018 ; Bishop, 2019 ). Furthermore, on the societal level, users’ increasing level of awareness could also contribute to a more fair and transparent design of algorithms ( Shin et al, 2022a ), and thereby offer potentials to ensure algorithms as a tool for social justice. Thereby, this study aims to examine the factors that influence users’ algorithmic awareness in China during the pandemic era.…”

Acceptance, initial trust formation, and human biases in artificial intelligence: Focus on clinicians

“…Trust in AI can be defined as the tendency of the user to rely on AI recommendation and willingly be vulnerable to the technology. Thus far, most of the studies addressing AI trust have assumed trust as a construct that varies overtime and have explored the role of fairness ( 9 ), interpretability, privacy ( 10 , 11 ), and algorithmic awareness ( 12 ) in shaping users' trust in AI. Other factors such as their interaction with the technology, AI accuracy, data quality, and biases, have been considered to significantly impact user trust in AI.…”