Cultural proximity bias in AI-acceptability: The importance of being human

Tubadji, Annie; Huang, Haoran; Webber, Don J.

doi:10.1016/j.techfore.2021.121100

Cited by 18 publications

(22 citation statements)

References 81 publications

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“…Human response to artificially created outputs is heavily influenced by the development process and mode of manufacture (Judge, Fernando, Paladino, & Kashima, 2020). Tubadji et al (2021), while analyzing human-made and AI-generated goods, argue that consumers naturally have a higher cultural proximity to human creators. The authors state that "humans will logically prefer 'natural' human interaction and products over 'artificial' alternatives when otherwise they could be viewed as being equivalent" (p. 4).…”

Section: Human Response To Artificially Created Outputsmentioning

confidence: 99%

“…Considering how humans respond to artificially created outputs and evaluate the value of products, awareness of how automation is used during the production process should have a significant effect on the evaluation of artificially created art. As argued by Tubadji et al (2021), the human factor is expected to play a positive role in the value perception of artistic products made solely by humans when compared with fully automated products. Also, as stated by Caves (2000), the use automation should impact the perceived novelty of processes consequently positively impacting how products are perceived.…”

Section: Value Perceptionmentioning

confidence: 99%

“…Artificial intelligence (AI) has established itself as a pivotal tool in the creative industries (Anantrasirichai & Bull, 2022; Deahl, 2018; Tubadji, Huang, & Webber, 2021). Production processes that require both rational and abstract styles of thinking have now been optimized by AI, enabling faster, cheaper, and scalable outputs.…”

mentioning

confidence: 99%

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Artificial Intelligence Creates Art? An Experimental Investigation of Value and Creativity Perceptions

Tigre Moura,

Castrucci,

Hindley

2023

Journal of Creative Behavior

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This paper presents a study analyzing the perception of artistic products created by or with the support of artificial intelligence (AI). The research builds on previous studies revealing that the output of artificial creativity processes can indeed rival human‐made products, satisfy consumer expectations, and derive enjoyment. However, in contrast to earlier works, the study focuses on the awareness of how AI is used during production processes (full automation, human‐AI co‐creation, and solely human) and product types (levels of involvement and product tangibility). A laboratory experiment was conducted to compare the value and creativity perceptions of 199 participants. Results revealed that, overall, the use of AI enhanced perceptions of process novelty. Additionally, the use of AI led to more positive evaluations of intangible and high involvement products such as songs compared to tangible and high involvement products such as paintings. This shows a decrease in perceived quality as more AI is involved in the production process. Furthermore, the level of product involvement level indicated no causal relationship to the degree of AI used in the production process.

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Section: Human Response To Artificially Created Outputsmentioning

confidence: 99%

Section: Value Perceptionmentioning

confidence: 99%

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Artificial Intelligence Creates Art? An Experimental Investigation of Value and Creativity Perceptions

Tigre Moura,

Castrucci,

Hindley

2023

Journal of Creative Behavior

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“…Surveying the literature on I4T and management systems, there have been numerous studies investigating the use of AI (Oke 2008, Zawacki-Richter et al 2019, Benbya et al 2021, Collins et al 2021. However, the debate in mainstream and regional economics has focused largely on firms, employment and consumer behaviour (Frey, Osborne 2017, Tubadji et al 2021, Hidalgo 2021, with little attention paid to the use of AI for welfare management (Oravec 2019, Vinuesa et al 2020). Additionally, there has been only partial recognition of the use of blockchain technology for management systems, primarily in the context of identity and digital rights management (Faber et al 2019, Chen et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Welfare Distribution Model for Digital Inclusivity

Tartan

2023

REGION

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An unprecedented rate of technological advancement, compounded by the COVID-19 pandemic, has expedited our transition to a fully digitalised society. Traditionally, digital inclusion focuses on an individual's ability to connect to and access information from the internet. The adoption of novel technologies such as artificial intelligence and blockchain is little distinguished in the literature from internet accessibility. While these digital solutions present novel opportunities, they may also perpetuate or exacerbate the existing hurdles faced by digitally excluded localities. However, these technologies could also be used to tackle the digital divide. Inspired by the design of Bitcoin, the current study offers a conceptual blockchain-based welfare model that adopts a two-pronged approach to enable the fair distribution of capital and resource allocation across the UK regions. The model offers transparency over institutional processes and improves their trustworthiness while preserving privacy. At the community level, the model assumes the application of economic incentives in order to promote digital inclusivity and stimulate cooperation and competition within local cultures. By mobilising both public institutions and communities, such a holistic model would assist the flow of information between the supply and demand side of the regional economy. This approach may not only help to dissolve the welfare losses arising from the digital divide, but also improve social well-being in all regions.

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“…As artificial intelligence (AI) becomes more widely adopted, the problem of model bias has become increasingly apparent. Bias has long been a critical area of research and concern in AI, and it reflects widespread societal biases about race, gender, biological gender, age, and culture ( Ntoutsi et al, 2020 ; Kapur, 2021 ; Kim et al, 2021 ; Tubadji et al, 2021 ). In this study, we investigated the bias from gender-dependent AI models and aimed to present new insights into the model bias issue.…”

Section: Introductionmentioning

confidence: 99%

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19

et al. 2021

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Artificial intelligence (AI) technologies have been applied in various medical domains to predict patient outcomes with high accuracy. As AI becomes more widely adopted, the problem of model bias is increasingly apparent. In this study, we investigate the model bias that can occur when training a model using datasets for only one particular gender and aim to present new insights into the bias issue. For the investigation, we considered an AI model that predicts severity at an early stage based on the medical records of coronavirus disease (COVID-19) patients. For 5,601 confirmed COVID-19 patients, we used 37 medical records, namely, basic patient information, physical index, initial examination findings, clinical findings, comorbidity diseases, and general blood test results at an early stage. To investigate the gender-based AI model bias, we trained and evaluated two separate models—one that was trained using only the male group, and the other using only the female group. When the model trained by the male-group data was applied to the female testing data, the overall accuracy decreased—sensitivity from 0.93 to 0.86, specificity from 0.92 to 0.86, accuracy from 0.92 to 0.86, balanced accuracy from 0.93 to 0.86, and area under the curve (AUC) from 0.97 to 0.94. Similarly, when the model trained by the female-group data was applied to the male testing data, once again, the overall accuracy decreased—sensitivity from 0.97 to 0.90, specificity from 0.96 to 0.91, accuracy from 0.96 to 0.91, balanced accuracy from 0.96 to 0.90, and AUC from 0.97 to 0.95. Furthermore, when we evaluated each gender-dependent model with the test data from the same gender used for training, the resultant accuracy was also lower than that from the unbiased model.

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Cultural proximity bias in AI-acceptability: The importance of being human

Cited by 18 publications

References 81 publications

Artificial Intelligence Creates Art? An Experimental Investigation of Value and Creativity Perceptions

Artificial Intelligence Creates Art? An Experimental Investigation of Value and Creativity Perceptions

Blockchain-Based Welfare Distribution Model for Digital Inclusivity

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19

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