Special Issue on Human-AI Teaming and Special Issue on AI in Healthcare

Endsley, Mica R.; Cooke, Nancy J.; McNeese, Nathan J.; Bisantz, Ann M.; Militello, Laura G.; Roth, Emilie M.

doi:10.1177/15553434221133288

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Recent research has been exploring the role of agent reliability on human trust, the methods of communicating intent between human and AI agents, and ways that AI agents either work together with humans or work as trainers of humans [42]. New approaches that pave the way for innovative research in this direction include for example considering coordination in hybrid teams of humans and autonomous agents in many-to-many situations (multiple humans and multiple agents) with the use of trustworthy interaction patterns [97], modelling the maturity of collaborative human-AI ecosystems [106] separating the design choices in terms of the different decision tasks and evaluating the efficacy, usability, and reliance of approaches [84].…”

Section: Shift From Human-in-the-loop To Human-ai Teamsmentioning

confidence: 99%

Exploring the landscape of trustworthy artificial intelligence: Status and challenges

Mentzas,

Fikardos,

Lepenioti

et al. 2024

IDT

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Artificial Intelligence (AI) has pervaded everyday life, reshaping the landscape of business, economy, and society through the alteration of interactions and connections among stakeholders and citizens. Nevertheless, the widespread adoption of AI presents significant risks and hurdles, sparking apprehension regarding the trustworthiness of AI systems by humans. Lately, numerous governmental entities have introduced regulations and principles aimed at fostering trustworthy AI systems, while companies, research institutions, and public sector organizations have released their own sets of principles and guidelines for ensuring ethical and trustworthy AI. Additionally, they have developed methods and software toolkits to aid in evaluating and improving the attributes of trustworthiness. The present paper aims to explore this evolution by analysing and supporting the trustworthiness of AI systems. We commence with an examination of the characteristics inherent in trustworthy AI, along with the corresponding principles and standards associated with them. We then examine the methods and tools that are available to designers and developers in their quest to operationalize trusted AI systems. Finally, we outline research challenges towards end-to-end engineering of trustworthy AI by-design.

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Section: Shift From Human-in-the-loop To Human-ai Teamsmentioning

confidence: 99%

Exploring the landscape of trustworthy artificial intelligence: Status and challenges

Mentzas,

Fikardos,

Lepenioti

et al. 2024

IDT

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“…Dorton and Harper (2022) provide a naturalistic exploration of trust and AI from the perspective of intelligence analysts. They identify that the performance of the system coupled with its explicability and perceived utility were important elements in enabling them to achieve their mission (Endsley et al, 2022b).…”

Section: Assured Autonomymentioning

confidence: 99%

Grand challenges in intelligent aerospace systems

Cohen

2023

Front. Aerosp. Eng.

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Human-AI teaming (HAT) or human-AI collaboration represents the integration of humans and AI systems to cooperatively perform tasks and achieve goals. HAT provides synergy between humans and AI, creating a relationship that utilizes the unique strengths of each entity. Collaboration may occur between one or more humans on one side and one or more different AI systems on the other. The main idea is Frontiers in Aerospace Engineering frontiersin.org 02

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“…Instead, a human-AI teaming approach is preferred, where experts can observe, control, and update the labeling process. However, little work has concentrated on how to enhance this human-AI Teaming 1 [12], [25] to assist experts in the decision-making process, rather than focus their efforts on improving a given model's performance. To fill this gap, we visually represent the different ML predictions to assist the biologists, allowing them to better understand the classifiers and enabling them to efficiently derive the correct lineage.…”

Section: Introductionmentioning

confidence: 99%

Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage

Hong,

Maciejewski,

Trubuil

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright

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Special Issue on Human-AI Teaming and Special Issue on AI in Healthcare

Cited by 8 publications

References 14 publications

Exploring the landscape of trustworthy artificial intelligence: Status and challenges

Exploring the landscape of trustworthy artificial intelligence: Status and challenges

Grand challenges in intelligent aerospace systems

Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage

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