Garbage in, toxic data out: a proposal for ethical artificial intelligence sustainability impact statements

Bogani, Ronny; Theodorou, Andreas; Arnaboldi, Luca; Wortham, Robert H.

doi:10.1007/s43681-022-00221-0

Cited by 6 publications

(6 citation statements)

References 12 publications

(9 reference statements)

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“…2 Graphs showing the significant interaction effect of level of digital confidence on the relationship between experience of AI technologies and attitudes to AI (general, global, personal) level of interest in and engagement with AI, particularly in the form of generative AI products such as OpenAI's Chat-GTP [52]. It is without question that the impact of AI on society will only increase, and concerns have been raised globally as to the ethical implications of this acceleration [53,54,62]. These concerns have focused on a range of issues, but inclusivity has often been a key focus [69].…”

Section: Discussionmentioning

confidence: 99%

“…The latest surge in AI developments and applications has been fuelled by significant levels of investment, which are predicted to reach close to USD $160 billion globally by 2025 [51]. The speed of this roll-out has been particularly marked since the launch of generative AI applications such as OpenAI's ChatGPT in late 2022 [52][53][54]. Generative AI, a form of AI that uses machine learning methods such as natural language processing or deep neural nets, is used to generate novel content, whether text, image or audio-based.…”

Section: Artificial Intelligence and The "Ai Divide"mentioning

confidence: 99%

“…The speed of recent AI developments has, however, outrun the ability for adequate governance or regulation [15,58], and has prompted concerns regarding the ethical nature of these technologies and their data sources [53][54][55]. Questions have been raised over a potential 'AI-divide' in which some groups will have greater access to the advantages of this technology than others.…”

Section: Artificial Intelligence and The "Ai Divide"mentioning

confidence: 99%

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The digital divide in action: how experiences of digital technology shape future relationships with artificial intelligence

Bentley,

Naughtin,

McGrath

et al. 2024

AI Ethics

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The digital divide remains an ongoing societal concern, with digital exclusion shown to have a significantly detrimental impact on people’s quality of life. Artificial intelligence (AI), the latest wave of digitalisation, is being integrated into the fabric of society at an accelerated rate, the speed of which has prompted ethical concerns. Without addressing the digital divide, the AI revolution risks exacerbating the existing consequences of digital exclusion and limiting the potential for all people to reap the benefits provided by AI. To understand the factors that might contribute to experiences of AI, and how these might be related to digital exclusion, we surveyed a diverse online community sample (N = 303). We created a novel measure of digital confidence capturing individual levels of awareness, familiarity, and sense of competence with digital technology. Results indicated that measures of digital confidence were predicted by structural, behavioural, and psychological differences, such that women, older people, those on lower salaries, people with less digital access, and those with lower digital well-being, reported significantly less digital confidence. Furthermore, digital confidence significantly moderated the relationship between people’s experiences with everyday AI technologies and their general attitudes towards AI. This understanding of the spill-over effects of digital exclusion onto experiences of AI is fundamental to the articulation and delivery of inclusive AI.

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Section: Discussionmentioning

confidence: 99%

Section: Artificial Intelligence and The "Ai Divide"mentioning

confidence: 99%

Section: Artificial Intelligence and The "Ai Divide"mentioning

confidence: 99%

See 1 more Smart Citation

The digital divide in action: how experiences of digital technology shape future relationships with artificial intelligence

Bentley,

Naughtin,

McGrath

et al. 2024

AI Ethics

View full text Add to dashboard Cite

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“…Leveraging advanced algorithms and analyzing comprehensive patient data such as clinical records, cardiovascular MRI, and demographic information, these models provide accurate and personalized assessments of mortality risk. This transformative approach empowers clinicians to make informed decisions, tailor surgical strategies, and ultimately enhance patient outcomes in ACHD surgery [167] .…”

Section: Congenital Heart Diseasementioning

confidence: 99%

Is artificial intelligence the new kid on the block? Sustainable applications in cardiology

Strangio,

Leo,

Sabatino

et al. 2024

Vessel Plus

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Artificial intelligence (AI) is changing our clinical practice. This is particularly true in cardiology where the clinician is often required to handle a large amount of clinical, biological, and imaging data during decision making. In this context, AI can address the need for fast and accurate tools while reducing the burden on clinicians and improving the efficiency of healthcare systems. With this inevitable shift towards more automated and efficient systems, patients may benefit from a more accurate diagnosis and more tailored treatment. A multitude of clinical applications have already been made available and implemented in several fields of cardiology. The aim of this narrative review is to provide an overall picture of the most recent evidence in the literature about AI implementations, highlighting their potential impact on clinical practice.

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“…The lack of an effective data cleaning process may result in a “garbage in and garbage out” scenario [ 8 ], adversely affecting the subsequent data analysis. In contrast, an effective data-cleaning process can transform dirty data into clean, reliable data that reflect real-world situations, providing researchers with more valuable information [ 9 ]. Therefore, data cleaning plays a decisive role in improving data quality.…”

Section: Impact Of Data Cleaning On Data Qualitymentioning

confidence: 99%

Normal Workflow and Key Strategies for Data Cleaning Toward Real-World Data: Viewpoint

Guo¹,

Wang²,

Yang³

et al. 2023

Interact J Med Res

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With the rapid development of science, technology, and engineering, large amounts of data have been generated in many fields in the past 20 years. In the process of medical research, data are constantly generated, and large amounts of real-world data form a “data disaster.” Effective data analysis and mining are based on data availability and high data quality. The premise of high data quality is the need to clean the data. Data cleaning is the process of detecting and correcting “dirty data,” which is the basis of data analysis and management. Moreover, data cleaning is a common technology for improving data quality. However, the current literature on real-world research provides little guidance on how to efficiently and ethically set up and perform data cleaning. To address this issue, we proposed a data cleaning framework for real-world research, focusing on the 3 most common types of dirty data (duplicate, missing, and outlier data), and a normal workflow for data cleaning to serve as a reference for the application of such technologies in future studies. We also provided relevant suggestions for common problems in data cleaning.

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Garbage in, toxic data out: a proposal for ethical artificial intelligence sustainability impact statements

Cited by 6 publications

References 12 publications

The digital divide in action: how experiences of digital technology shape future relationships with artificial intelligence

The digital divide in action: how experiences of digital technology shape future relationships with artificial intelligence

Is artificial intelligence the new kid on the block? Sustainable applications in cardiology

Normal Workflow and Key Strategies for Data Cleaning Toward Real-World Data: Viewpoint

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