Truth from the machine: artificial intelligence and the materialization of identity

Keyes, Os; Hitzig, Zoë; Blell, Mwenza

doi:10.1080/03080188.2020.1840224

Cited by 34 publications

(26 citation statements)

References 76 publications

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“…(Goertzel, 2017). Finally, the robot can appear in an Employment Test, where it has to clear a vocational test, including writing and driving exams (Keyes et al, 2021).…”

Section: Types Of Artificial Intelligence: Asi Agi Animentioning

confidence: 99%

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On the Commoditization of Artificial Intelligence

2021

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As artificial intelligence's potential and pervasiveness continue to increase, its strategic importance, effects, and management must be closely examined. Societies, governments, and business organizations need to view artificial intelligence (AI) technologies and their usage from an entirely different perspective. AI is poised to have a tremendous impact on every aspect of our lives. Therefore, it must have a broader view that transcends AI's technical capabilities and perceived value, including areas of AI's impact and influence. Nicholas G. Carr's seminal paper “IT Does not Matter (Carr, 2003) explained how IT's potential and ubiquity have increased, but IT's strategic importance has declined with time. AI is poised to meet the same fate as IT. In fact, the commoditization of AI has already begun. This paper presents the arguments to demonstrate that AI is moving rapidly in this direction. It also proposes an artificial intelligence-based organizational framework to gain value-added elements for lowering the impact of AI commoditization.

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“…(Goertzel, 2017). Finally, the robot can appear in an Employment Test, where it has to clear a vocational test, including writing and driving exams (Keyes et al, 2021).…”

Section: Types Of Artificial Intelligence: Asi Agi Animentioning

confidence: 99%

“…Next, the College Robot Test requires the AGI robot to enroll in college and successfully pass all classes (Goertzel, 2017 ). Finally, the robot can appear in an Employment Test, where it has to clear a vocational test, including writing and driving exams (Keyes et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

On the Commoditization of Artificial Intelligence

2021

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“…Much less critical attention has been paid to algorithms for discovery (Keyes, Hitzig, and Blell 2021). Unlike algorithms for production, they do not have their output directly plugged into decisions like what price to quote or whether to offer medical treatment.…”

Section: Algorithms For Discoverymentioning

confidence: 99%

Because the Machine Can Discriminate: How Machine Learning Serves and Transforms Biological Explanations of Human Difference

Lockhart¹

2021

Preprint

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Algorithms and society research has focused largely on algorithms for production, resulting in constrained theoretical understanding of the full range of ways algorithms may reinforce social structures of inequality. Algorithms for discovery illustrate an alternative to the prevailing emphasis on inattention and thoughtlessness in algorithms and society literature and the scientific bias literature more generally. Using the case of machine learning algorithms trained to predict sex from brain scans, I argue that increasing intentionality and theory-method alignment in algorithmic systems are not necessarily cures. Instead, scientists design and interpret classification algorithms to reinforce conservative notions of sex and gender.

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“…ethnic groups, gender or sexual minorities, or other groups identified and targeted for public health intervention). 1 In England, the National Health Service (NHS England) holds personal data about more than 55 million people in electronic health records of one form or another, although there is no single integrated system at present. The volume of data increases daily because the service deals with over 1 million patients every 36 hours, 2 and increasingly this includes genomic data.…”

Section: Introductionmentioning

confidence: 99%

Beyond trust: Amplifying unheard voices on concerns about harm resulting from health data-sharing

Mulrine

Blell

Murtagh

2021

Medicine Access @ Point of Care

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Background: The point of care in many health systems is increasingly a point of health data generation, data which may be shared and used in a variety of ways by a range of different actors. Aim: We set out to gather data about the perspectives on health data-sharing of people living in North East England who have been underrepresented within other public engagement activities and who are marginalized in society. Methods: Multi-site ethnographic fieldwork was carried out in the Teesside region of England over a 6-month period in 2019 as part of a large-scale health data innovation program called Connected Health Cities. Organizations working with marginalized groups were contacted to recruit staff, volunteers, and beneficiaries for participation in qualitative research. The data gathered were analyzed thematically and vignettes constructed to illustrate findings. Results: Previous encounters with health and social care professionals and the broader socio-political contexts of people’s lives shape the perspectives of people from marginalized groups about sharing of data from their health records. While many would welcome improved care, the risks to people with socially produced vulnerabilities must be appreciated by those advocating systems that share data for personalized medicine or other forms of data-driven care. Conclusion: Forms of innovation in medicine which rely on greater data-sharing may present risks to groups and individuals with existing vulnerabilities, and advocates of these innovations should address the lack of trustworthiness of those receiving data before asking that people trust new systems to provide health benefits.

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Truth from the machine: artificial intelligence and the materialization of identity

Cited by 34 publications

References 76 publications

On the Commoditization of Artificial Intelligence

On the Commoditization of Artificial Intelligence

Because the Machine Can Discriminate: How Machine Learning Serves and Transforms Biological Explanations of Human Difference

Beyond trust: Amplifying unheard voices on concerns about harm resulting from health data-sharing

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