AI-Driven Automation in a Human-Centered Cyber World

Smith, Norris Lee; Teerawanit, Jirunya; Hamid, Oussama H.

doi:10.1109/smc.2018.00551

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…Data could be in any of different forms (for example, text, audio, image, and/or video). ML is a subfield of AI that characterises the system's capacity to spot patterns that are otherwise undetectable by humans [16]. It achieves this by using general-purpose procedures, which enable the AI system to solve the problem at hand without being explicitly programmed to do so [17].…”

Section: Introductionmentioning

confidence: 99%

“…It achieves this by using general-purpose procedures, which enable the AI system to solve the problem at hand without being explicitly programmed to do so [17]. Yet, for this to occur, the system needs to process data in a way that facilitates interpretation and provides context [16]. Typically, this is made possible by letting human experts label the data as part of the data preparation process, which includes collecting, curating, cleaning, and transforming raw data before processing and analysing it.…”

Section: Introductionmentioning

confidence: 99%

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Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

Hamid

2023

Applied Sciences

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Despite its dominance over the past three decades, model-centric AI has recently come under heavy criticism in favor of data-centric AI. Indeed, both promise to improve the performance of AI systems, yet with converse points of focus. While the former successively upgrades a devised model (algorithm/code), holding the amount and type of data used in model training fixed, the latter enhances the quality of deployed data continuously, paying less attention to further model upgrades. Rather than favoring either of the two approaches, this paper reconciles data-centric AI with model-centric AI. In so doing, we connect current AI to the field of cybersecurity and natural language inference, and through the phenomena of ‘adversarial samples’ and ‘hypothesis-only biases’, respectively, showcase the limitations of model-centric AI in terms of algorithmic stability and robustness. Further, we argue that overcoming the alleged limitations of model-centric AI may well require paying extra attention to the alternative data-centric approach. However, this should not result in reducing interest in model-centric AI. Our position is supported by the notion that successful ‘problem solving’ requires considering both the way we act upon things (algorithm) as well as harnessing the knowledge derived from data of their states and properties.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

Hamid

2023

Applied Sciences

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“…Furthermore, digital kiosks can be more appealing to people who seek social interaction or assistance on how to consume a service. This makes digital kiosks a good example of a human-based technology [8]. On the other side of the spectrum, mobile phone applications are designed to be used by individuals with the need for limited interaction for the completion of a service.…”

Section: From the Service Consumer Point Of Viewmentioning

confidence: 99%

Navigating the Divide: Digital Kiosks and Mobile Apps as Complementary Human-Centered Self-Service Technologies

Aljohi,

Alzaabi,

Almahri

et al. 2024

RAiSE-2023

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“…The thematic foci vary depending on the discipline and field of use. While researchers from the human-computer interaction (HCI) community describe human-centered AI as an issue of AIs' trustworthiness and related safety culture (Shneiderman, 2022) and thus combine technological characteristics with organizational characteristics, researchers in psychology consider human-centricity as an issue of job design where AI applications support operators' authority and wellbeing (e.g., De Cremer and Kasparov, 2021), which means that they combine organizational and individual characteristics. Researchers in engineering and manufacturing most likely address AI-based assistance to compensate for individual weaknesses in the production flow (Mehta et al, 2022) and thus relate technological and organizational characteristics to the individual, but with another concept of man than prevalent in psychology (Wilkens et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Configurations of human-centered AI at work: seven actor-structure engagements in organizations

Wilkens,

Lupp,

Langholf

2023

Front. Artif. Intell.

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PurposeThe discourse on the human-centricity of AI at work needs contextualization. The aim of this study is to distinguish prevalent criteria of human-centricity for AI applications in the scientific discourse and to relate them to the work contexts for which they are specifically intended. This leads to configurations of actor-structure engagements that foster human-centricity in the workplace.Theoretical foundationThe study applies configurational theory to sociotechnical systems’ analysis of work settings. The assumption is that different approaches to promote human-centricity coexist, depending on the stakeholders responsible for their application.MethodThe exploration of criteria indicating human-centricity and their synthesis into configurations is based on a cross-disciplinary literature review following a systematic search strategy and a deductive-inductive qualitative content analysis of 101 research articles.ResultsThe article outlines eight criteria of human-centricity, two of which face challenges of human-centered technology development (trustworthiness and explainability), three challenges of human-centered employee development (prevention of job loss, health, and human agency and augmentation), and three challenges of human-centered organizational development (compensation of systems’ weaknesses, integration of user-domain knowledge, accountability, and safety culture). The configurational theory allows contextualization of these criteria from a higher-order perspective and leads to seven configurations of actor-structure engagements in terms of engagement for (1) data and technostructure, (2) operational process optimization, (3) operators’ employment, (4) employees’ wellbeing, (5) proficiency, (6) accountability, and (7) interactive cross-domain design. Each has one criterion of human-centricity in the foreground. Trustworthiness does not build its own configuration but is proposed to be a necessary condition in all seven configurations.DiscussionThe article contextualizes the overall debate on human-centricity and allows us to specify stakeholder-related engagements and how these complement each other. This is of high value for practitioners bringing human-centricity to the workplace and allows them to compare which criteria are considered in transnational declarations, international norms and standards, or company guidelines.

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AI-Driven Automation in a Human-Centered Cyber World

Cited by 10 publications

References 27 publications

Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

Navigating the Divide: Digital Kiosks and Mobile Apps as Complementary Human-Centered Self-Service Technologies

Configurations of human-centered AI at work: seven actor-structure engagements in organizations

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