Toward a More Equal World: The Human Rights Approach to Extending the Benefits of Artificial Intelligence

Gibbons, Elizabeth

doi:10.1109/mts.2021.3056295

Cited by 17 publications

(12 citation statements)

References 1 publication

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“…The digital divide will likely increase existing education, skill [ 34 , 88 , 89 , 90 , 91 ], OSH, and income inequities [ 45 , 73 , 82 , 92 ]. The digital divide and technological displacement may be most apparent in historically marginalized communities, for example, in rural and low-income communities.…”

Section: Resultsmentioning

confidence: 99%

Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review

Fisher

Flynn

Pratap

et al. 2023

IJERPH

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Artificial intelligence (AI) has the potential to either reduce or exacerbate occupational safety and health (OSH) inequities in the workplace, and its impact will be mediated by numerous factors. This paper anticipates challenges to ensuring that the OSH benefits of technological advances are equitably distributed among social groups, industries, job arrangements, and geographical regions. A scoping review was completed to summarize the recent literature on AI’s role in promoting OSH equity. The scoping review was designed around three concepts: artificial intelligence, OSH, and health equity. Scoping results revealed 113 articles relevant for inclusion. The ways in which AI presents barriers and facilitators to OSH equity are outlined along with priority focus areas and best practices in reducing OSH disparities and knowledge gaps. The scoping review uncovered priority focus areas. In conclusion, AI’s role in OSH equity is vastly understudied. An urgent need exists for multidisciplinary research that addresses where and how AI is being adopted and evaluated and how its use is affecting OSH across industries, wage categories, and sociodemographic groups. OSH professionals can play a significant role in identifying strategies that ensure the benefits of AI in promoting workforce health and wellbeing are equitably distributed.

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Section: Resultsmentioning

confidence: 99%

Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review

Fisher

Flynn

Pratap

et al. 2023

IJERPH

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“…Collaborative efforts, informed by human rights frameworks and practical recommendations, are essential. These actions not only preserve but also nurture trust, ensuring the inclusive and effective application of AI technology in healthcare 36 37…”

Section: Discussionmentioning

confidence: 99%

“…These actions not only preserve but also nurture trust, ensuring the inclusive and effective application of AI technology in healthcare. 36 37 …”

Section: Discussionmentioning

confidence: 99%

Women’s perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study

Viberg Johansson,

Dembrower,

Strand

et al. 2024

BMJ Open

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BackgroundUnderstanding women’s perspectives can help to create an effective and acceptable artificial intelligence (AI) implementation for triaging mammograms, ensuring a high proportion of screening-detected cancer. This study aimed to explore Swedish women’s perceptions and attitudes towards the use of AI in mammography.MethodSemistructured interviews were conducted with 16 women recruited in the spring of 2023 at Capio S:t Görans Hospital, Sweden, during an ongoing clinical trial of AI in screening (ScreenTrustCAD, NCT 04778670) with Philips equipment. The interview transcripts were analysed using inductive thematic content analysis.ResultsIn general, women viewed AI as an excellent complementary tool to help radiologists in their decision-making, rather than a complete replacement of their expertise. To trust the AI, the women requested a thorough evaluation, transparency about AI usage in healthcare, and the involvement of a radiologist in the assessment. They would rather be more worried because of being called in more often for scans than risk having overlooked a sign of cancer. They expressed substantial trust in the healthcare system if the implementation of AI was to become a standard practice.ConclusionThe findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist. Effective communication regarding the role and limitations of AI is crucial to ensure that patients understand the purpose and potential outcomes of AI-assisted healthcare.

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“…According to (15), contextual bias means the development of predictive AI models trained with data not reflecting the real context of the use of the algorithms, which can be considered a threat to the promise of AI to foster healthcare democratization of health services in LMICs because the models trained with the wrong data, cannot be used to build decision support tools for primary healthcare practitioners, that way overcoming the shortage of specialized health care professionals. Moreover, the fact that ML models are created in HICs can drive inequality, concentrating wealth, resources, and decisionmaking power in the hands of a few countries, companies, or citizens (21).…”

Section: Rationalementioning

confidence: 99%

“…Another critical aspect is change resistance, mainly due to the fear that AI will replace the work of healthcare professionals and staff. Training and education of clinicians about the benefits and limits of artificial intelligence and machine learning, and more recently, hackathons and datathons Solutions to Regulation and Legal Frameworks challenges (21). -Training and educa�on of clinicians abou�he benefits and limits of art ificial intelligence and machine learning.…”

Section: Cost-effectivenessmentioning

confidence: 99%

Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence

et al. 2022

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BackgroundRecent studies demonstrate the potential of Artificial Intelligence to support diagnosis, mortality assessment, and clinical decisions in low-and-middle-income countries (LMICs). However, explicit evidence of strategies to overcome the particular challenges for transformed health systems in these countries does not exist.ObjectiveThe present study undertakes a review of research on the current status of artificial intelligence (AI) to identify requirements, gaps, challenges, and possible strategies to strengthen the large, complex, and heterogeneous health systems in LMICs.DesignAfter introducing the general challenges developing countries face, the methodology of systematic reviews and the meta-analyses extension for scoping reviews (PRISMA-ScR) is introduced according to the preferred reporting items. Scopus and Web of Science databases were used to identify papers published between 2011–2022, from which we selected 151 eligible publications. Moreover, a narrative review was conducted to analyze the evidence in the literature about explicit evidence of strategies to overcome particular AI challenges in LMICs.ResultsThe analysis of results was divided into two groups: primary studies, which include experimental studies or case studies using or deploying a specific AI solution (n = 129), and secondary studies, including opinion papers, systematic reviews, and papers with strategies or guidelines (n = 22). For both study groups, a descriptive statistical analysis was performed describing their technological contribution, data used, health context, and type of health interventions. For the secondary studies group, an in-deep narrative review was performed, identifying a set of 40 challenges gathered in eight different categories: data quality, context awareness; regulation and legal frameworks; education and change resistance; financial resources; methodology; infrastructure and connectivity; and scalability. A total of 89 recommendations (at least one per challenge) were identified.ConclusionResearch on applying AI and ML to healthcare interventions in LMICs is growing; however, apart from very well-described ML methods and algorithms, there are several challenges to be addressed to scale and mainstream experimental and pilot studies. The main challenges include improving the quality of existing data sources, training and modeling AI solutions based on contextual data; and implementing privacy, security, informed consent, ethical, liability, confidentiality, trust, equity, and accountability policies. Also, robust eHealth environments with trained stakeholders, methodological standards for data creation, research reporting, product certification, sustained investment in data sharing, infrastructures, and connectivity are necessary.Systematic review registration[https://rb.gy/frn2rz].

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Toward a More Equal World: The Human Rights Approach to Extending the Benefits of Artificial Intelligence

Cited by 17 publications

References 1 publication

Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review

Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review

Women’s perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study

Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence

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