Artificial Intelligence and Human Enhancement: Can AI Technologies Make Us More (Artificially) Intelligent?

Nyholm, Sven

doi:10.1017/s0963180123000464

Cited by 10 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In accordance with conceptual and empirical analyses from previous studies on the ethical implications of using AI-DSSs in healthcare (eg, [24,[38][39][40][41]), care professionals' comments encompass interrelated concerns about an excessive reliance of caregivers on technology, potentially eroding caregivers' critical decision-making capabilities and causing misguidance towards unsuitable care strategies. These viewpoints tie in with how Nyholm [42] sketches the dual effects of AI on human intelligence; the prospect that AI technologies might serve as a form of cognitive enhancement and the cautionary notion that heavy reliance on AI technologies might also make people less intelligent. Other risks frequently discussed by care professionals are that AI-DSSs may cause a false sense of security, as it may overlook important issues and care needs, or that it could contribute to overproblematization of health and aging by flagging many trends in data as potentially problematic.…”

Section: Main Findingsmentioning

confidence: 94%

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation (Preprint)

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

Preprint

View full text Add to dashboard Cite

BACKGROUND While use of artificial intelligence (AI)-based technologies such as decision-support systems (AI-DSSs) could help sustaining and improving the quality and efficiency of care, their deployment also creates ethical and social challenges. In recent years, there has been a growing prevalence of high-level guidelines and frameworks to provide guidance on responsible AI innovation. However, few studies specify how AI-based technologies such as AI-DSSs can be responsibly embedded in specific contexts such as the nursing process in the long-term care (LTC) for older adults. OBJECTIVE Opportunities and prerequisites for responsible AI-assisted decision-making in the nursing process were explored from the perspectives of nurses and other professional stakeholders in LTC. METHODS Semi-structured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists and care centralists. Two imaginary scenarios about the future use of AI-DSSs were developed beforehand and used to enable participants to articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. After first openly discussing opportunities and possible risks associated with both scenarios, six high-level principles for responsible AI were used as probing themes to evoke further consideration on risks of using AI-DSSs in LTC. Further, participants were asked to brainstorm about possible strategies and actions in the design, implementation and use of AI-DSSs to address or mitigate the mentioned risks. A thematic analysis was carried out to identify opportunities and prerequisites for responsible innovation in this area. RESULTS Professionals’ stance towards the use of AI-DSSs is not a matter of purely positive or negative expectations, but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of opportunities and prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to early identification of care needs, guidance in devising care strategies, shared decision-making, and caregivers’ workload and work experience. To optimally balance opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in the nursing process were identified: (1) regular deliberation on data collection, (2) a balanced proactive nature of AI-DSSs, (3) incremental advancements aligned with trust and experience, (4) customization for all user groups including clients and caregivers, (5) measures to counteract bias and narrow perspectives, (6) human-centric learning loops, and (7) routinization of using AI-DSSs. CONCLUSIONS Opportunities of AI-assisted decision-making in the nursing process could turn into drawbacks, depending on the specific shaping of the design and the deployment of AI-DSSs. Therefore, we recommend viewing the responsible use of AI-DSSs as a balancing act. Moreover, given the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address different factors important to the responsible embedding of AI-DSSs in practice.

show abstract

Section: Main Findingsmentioning

confidence: 94%

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation (Preprint)

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Ultimately, caregivers who heavily rely on AI-DSSs might be led astray towards unsuitable care strategies. These perspectives tie in with how Nyholm [47] sketches the dual effects of AI on human intelligence: the prospect that AI technologies might serve as a form of cognitive enhancement and the cautionary notion that heavy reliance on AI technologies might also make people less intelligent. Furthermore, in our study, concerns were expressed related to privacy infringements, conflicts of interest, and the deterioration of the work experience of caregivers owing to increased cognitive load or a reduced sense of professional fulfillment.…”

Section: Main Findingsmentioning

confidence: 97%

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

JMIR Nursing

View full text Add to dashboard Cite

Background: While use of artificial intelligence (AI)-based technologies such as decision-support systems (AI-DSSs) could help sustaining and improving the quality and efficiency of care, their deployment also creates ethical and social challenges. In recent years, there has been a growing prevalence of high-level guidelines and frameworks to provide guidance on responsible AI innovation. However, few studies specify how AI-based technologies such as AI-DSSs can be responsibly embedded in specific contexts such as the nursing process in the long-term care (LTC) for older adults.Objective: Opportunities and prerequisites for responsible AI-assisted decision-making in the nursing process were explored from the perspectives of nurses and other professional stakeholders in LTC.Methods: Semi-structured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists and care centralists. Two imaginary scenarios about the future use of AI-DSSs were developed beforehand and used to enable participants to articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. After first openly discussing opportunities and possible risks associated with both scenarios, six high-level principles for responsible AI were used as probing themes to evoke further consideration on risks of using AI-DSSs in LTC. Further, participants were asked to brainstorm about possible strategies and actions in the design, implementation and use of AI-DSSs to address or mitigate the mentioned risks. A thematic analysis was carried out to identify opportunities and prerequisites for responsible innovation in this area.Results: Professionals' stance towards the use of AI-DSSs is not a matter of purely positive or negative expectations, but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of opportunities and prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to early identification of care needs, guidance in devising care strategies, shared decision-making, and caregivers' workload and work experience. To optimally balance opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AIassisted decision-making in the nursing process were identified: (1) regular deliberation on data collection, (2) a balanced proactive nature of AI-DSSs, (3) incremental advancements aligned with trust and experience, (4) customization for all user groups including clients and caregivers, (5) measures to counteract bias and narrow perspectives, (6) human-centric learning loops, and (7) routinization of using AI-DSSs.Conclusions: Opportunities of AI-assisted decision-making in the nursing process could turn into drawbacks, depending on the specific shaping of the design and the deployment of AI-DSSs. Therefore, we recommend viewing the responsible use of AI-DSSs as a balancing act. Moreover, given the interrelatedness of the ide...

show abstract

“…The latter has diminished the imperative nature of retaining factual information, thereby potentially compromising our memory capabilities-a phenomenon some have referred to as the "Google effect" [31]. There seems to be a worry among many, as Nyholm [32] observes, that AI "might make us less intelligent-or perhaps less prone to use or develop our cognitive abilities" [32].…”

Section: Losing Skills Via Substitutionmentioning

confidence: 99%

Vox Populi, Vox ChatGPT: Large Language Models, Education and Democracy

Zuber,

Gogoll

2024

Philosophies

View full text Add to dashboard Cite

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them.

show abstract

Artificial Intelligence and Human Enhancement: Can AI Technologies Make Us More (Artificially) Intelligent?

Cited by 10 publications

References 21 publications

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation (Preprint)

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation (Preprint)

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

Vox Populi, Vox ChatGPT: Large Language Models, Education and Democracy

Contact Info

Product

Resources

About