Dependable Integration Concepts for Human-Centric AI-Based Systems

Macher, Georg; Akarmazyan, Siranush; Armengaud, Eric; Bacciu, Davide; Calandra, Calogero; Danzinger, Herbert; Dazzi, Patrizio; Davalas, Charalampos; Gennaro, Maria Carmela De; Dimitriou, Angela; Dobaj, Juergen; Dzambic, Maid; Giraudi, Lorenzo; Girbal, Sylvain; Michail, Dimitrios; Peroglio, Roberta; Potenza, Rosaria; Pourdanesh, Farank; Seidl, Matthias; Sardianos, Christos; Tserpes, Konstantinos; Valtl, Jakob; Varlamis, Iraklis; Veledar, Omar

doi:10.1007/978-3-030-83906-2_1

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Besides the required compliance with the existing standards, the monitoring of the human state during the interaction with the AD functions also provides precious feedback on the machine's performance which can be used for improving the intelligent machine itself [19] and provides the foundations for further human-centred development with the flair of a humanistic AI control. Considering that ensuring dependability of such systems relies on AI approaches, it is still an open issue that lacks standard industrialisation solutions [15]. Ensuring dependability, despite the complexity and changing nature of the systems due to adaptation and learning, is a precondition for public trust and acceptance [58].…”

Section: Discussionmentioning

confidence: 99%

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Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

et al. 2022

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As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success.

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Section: Discussionmentioning

confidence: 99%

“…However, the established safety engineering methods are somewhat failing to support these changes entirely. Thus, extensive effort is invested in developing safety engineering methods, safety design approaches and safety development processes that support this technological transformation [15].…”

Section: The Technology Challengesmentioning

confidence: 99%

Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

et al. 2022

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“…Concepts like "human-in-the-loop" (HITL) are getting established as mechanisms for AI systems that better serve human needs (Zanzotto, 2019). An HITL mechanism describes systems in which human involvement is integrated with automated processes to improve overall performance, reliability, and decision-making (Fu et al, 2021;Macher et al, 2021;Herrmann, 2022). Furthermore, several technology companies have defined their own sets of responsible AI guidelines with a human-centered foundation (Wright et al, 2020).…”

Section: Ai In Public Service Deliverymentioning

confidence: 99%

Exploring citizens’ stances on AI in public services: A social contract perspective

Schmager,

Grøder,

Parmiggiani

et al. 2024

Data & Policy

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This paper explores citizens’ stances toward the use of artificial intelligence (AI) in public services in Norway. Utilizing a social contract perspective, the study analyzes the government–citizen relationship at macro, meso, and micro levels. A prototype of an AI-enabled public welfare service was designed and presented to 20 participants who were interviewed to investigate their stances on the described AI use. We found a generally positive attitude and identified three factors contributing to this: (a) the high level of trust in government (macro level); (b) the balanced value proposition between individual and collective needs (meso level); and (c) the reassurance provided by having humans in the loop and providing transparency into processes, data, and model’s logic (microlevel). The findings provide valuable insights into citizens’ stances for socially responsible AI in public services. These insights can inform policy and guide the design and implementation of AI systems in the public sector by foregrounding the government–citizen relationship.

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“…5. Dependable and Privacy Estimation: Finally, we focused on the offering of dependable applications that can work within defined guarantees and are aware of the privacy leaked through learning, potentially offering a better tunable trade-off between privacy and performance [13,14]. Without the pretense of completeness, such off-the-shelf functionalities constitute just a variegate example of what it is possible to build on top of the AI-Toolkit and the TEACHING platform microservices infrastructure.…”

Section: Human-centric Personalization Via Reinforcementmentioning

confidence: 99%

AI-Toolkit: A Microservices Architecture for Low-Code Decentralized Machine Intelligence

Lomonaco

Gallicchio

et al. 2023

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

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Artificial Intelligence and Machine Learning toolkits such as Scikit-learn, PyTorch and Tensorflow provide today a solid starting point for the rapid prototyping of R&D solutions. However, they can be hardly ported to heterogeneous decentralised hardware and real-world production environments. A common practice involves outsourcing deployment solutions to scalable cloud infrastructures such as Amazon SageMaker or Microsoft Azure. In this paper, we proposed an open-source microservices-based architecture for decentralised machine intelligence which aims at bringing R&D and deployment functionalities closer following a low-code approach. Such an approach would guarantee flexible integration of cutting-edge functionalities while preserving complete control over the deployed solutions at negligible costs and maintenance efforts.

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Dependable Integration Concepts for Human-Centric AI-Based Systems

Cited by 8 publications

References 11 publications

Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

Exploring citizens’ stances on AI in public services: A social contract perspective

AI-Toolkit: A Microservices Architecture for Low-Code Decentralized Machine Intelligence

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