Optimizing human-centered AI for healthcare in the Global South

Okolo, Chinasa T.

doi:10.1016/j.patter.2021.100421

Cited by 22 publications

(15 citation statements)

References 36 publications

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“…43 If the training data for medical AI tools is predominantly sourced from the Global North, there is a risk that algorithms are performing poorer in recognizing certain conditions inherent to other patient demographics. 42,44,45 This lack of inclusivity and potential bias in AI may hamper its applicability and success in varied global settings.…”

Section: Discussionmentioning

confidence: 99%

Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals

Busch,

Keller,

Rueger

et al. 2023

Acta Radiologica Open

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Background The growing role of artificial intelligence (AI) in healthcare, particularly radiology, requires its unbiased and fair development and implementation, starting with the constitution of the scientific community. Purpose To examine the gender and country distribution among academic editors in leading computer science and AI journals. Material and Methods This cross-sectional study analyzed the gender and country distribution among editors-in-chief, senior, and associate editors in all 75 Q1 computer science and AI journals in the Clarivate Journal Citations Report and SCImago Journal Ranking 2022. Gender was determined using an open-source algorithm (Gender Guesser™), selecting the gender with the highest calibrated probability. Result Among 4,948 editorial board members, women were underrepresented in all positions (editors-in-chief/senior editors/associate editors: 14%/18%/17%). The proportion of women correlated positively with the SCImago Journal Rank indicator (ρ = 0.329; p = .004). The U.S., the U.K., and China comprised 50% of editors, while Australia, Finland, Estonia, Denmark, the Netherlands, the U.K., Switzerland, and Slovenia had the highest women editor representation per million women population. Conclusion Our results highlight gender and geographic disparities on leading computer science and AI journal editorial boards, with women being underrepresented in all positions and a disproportional relationship between the Global North and South.

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Section: Discussionmentioning

confidence: 99%

Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals

Busch,

Keller,

Rueger

et al. 2023

Acta Radiologica Open

View full text Add to dashboard Cite

show abstract

“…Institutions will differ in how they trust clinical AI. Okolo emphasizes this further in pointing out differences in trusting processes between clinicians in the Global North and the Global South: there does not seem to be a one size fits all solution [124]. We share the emphasis on the need for a participatory, iterative design process to successfully integrate into clinical workflows [124].…”

Section: Discussionmentioning

confidence: 99%

Trust in Clinical AI: Expanding the Unit of Analysis

Browne

Bakker

et al. 2022

HHAI2022: Augmenting Human Intellect

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From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.

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“…Few human-centric applications and studies have been conducted despite the cross-disciplinary challenge of building XAI (Evans et al 2022). In contrast to real-world scenarios, AI solutions are being implemented in a controlled setting (Okolo 2022). There is a need for AI systems to make the mechanics underlying their decision comprehensible to affected humans.…”

Section: Human-centric Explanationsmentioning

confidence: 99%

Human-centric and Semantics-based Explainable Event Detection: A Survey

Kolajo

Daramola

2023

Preprint

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In recent years, there has been a surge in interest in artificial intelligent systems that can provide human-centric explanations for decisions or predictions. No matter how good and efficient a model is, users or practitioners find it difficult to trust such model if they cannot understand the model or its behaviours. Incorporating explainability that is human-centric in event detection systems is significant for building a decision-making process that is more trustworthy and sustainable. Human-centric and semantics-based explainable event detection will achieve trustworthiness, explainability, and reliability, which are currently lacking in AI systems. This paper provides a survey on the human-centric explainable AI, explainable event detection, and semantics-based explainable event detection by answering some research questions that bother on the characteristics of human-centric explanations, the state of explainable AI, methods for human-centric explanations, the essence of human-centricity in explainable event detection, research efforts in explainable event solutions, and the benefits of integrating semantics into explainable event detection. The findings from the survey show the current state of human-centric explainability, the potential of integrating semantics into explainable AI, the open problems, and the future directions which can serve as steppingstones for researchers in the explainable AI domain.

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Optimizing human-centered AI for healthcare in the Global South

Cited by 22 publications

References 36 publications

Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals

Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals

Trust in Clinical AI: Expanding the Unit of Analysis

Human-centric and Semantics-based Explainable Event Detection: A Survey

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