Beyond Open vs. Closed

Artificial Intelligence (AI) is seen as the most promising among Industry 4.0 advancements for businesses. Artificial intelligence, defined as computer models that mimic intelligent behavior, is poised to unleash the next wave of digital disruption and bring a competitive advantage to the industry. The value of AI lies not in its models, but in the ways in which we can harness them. It is becoming more common for industry objects to be converted into intelligent objects that can sense, act, adapt, and behave in a given environment. Leaders in the industry will need to make deliberate choices about how, when, and where to deploy these technologies. Our work highlights some of the primary AI emerging trends in Industry 4.0. We also discuss the advantages, challenges, and applications of AI in Industry 4.0.

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“…Strong privacy guarantees, competitive advantages, and discriminatory policies [29] Difficulty collecting and labeling data…”

Section: Quantity Of Datamentioning

confidence: 99%

A Review on Artificial Intelligence in the Context of Industry 4.0

Banitaan¹,

Al-Refai²,

Almatarneh³

et al. 2023

IJACSA

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“…Models of safe and high-quality data linkage from multiple agencies necessitate a high level of interdisciplinarity (Jacobs and Popma, 2019) wider than the conventional boundaries of medicine and social care (Ford et al, 2019; Sharon and Lucivero, 2019). To address this, the SDF model has adopted a sociotechnical approach 13 to governing data (e.g., Young et al, 2019) where the multidisciplinary aspects (including, ethical, healthcare, legal, social care, social–cultural, and technical issues) of safe linkage for health and social care transformation are considered collectively and holistically from the outset.…”

Section: The Sdf Modelmentioning

confidence: 99%

The Social Data Foundation model: Facilitating health and social care transformation throughdatatrust services

et al. 2022

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Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.

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“…“Transportation data from any number of sources are used to develop models, but encoding the data as trained weights in a model ends up ‘laundering’ it—that is, it is no longer transparent to trace the source of the data through to the decisions reached by the model. A trust mitigates the lack of algorithmic accountability in part by emphasizing the use of synthetic datasets whenever possible during research and development; once a proof of concept is established, and access to the raw data is requested, ongoing data-sharing relationships mediated through data-sharing agreements present an opportunity to enforce provenance” (Young et al, 2019).…”

Section: Data Trustsmentioning

confidence: 99%

Can we trust trust-based data governance models?

Sloot

Keymolen

2022

Data & Policy

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Fiduciary agents and trust-based institutions are increasingly proposed and considered in legal, regulatory, and ethical discourse as an alternative or addition to a control-based model of data management. Instead of leaving it up to the citizen to decide what to do with her data and to ensure that her best interests are met, an independent person or organization will act on her behalf, potentially also taking into account the general interest. By ensuring that these interests are protected, the hope is that citizens’ willingness to share data will increase, thereby allowing for more data-driven projects. Thus, trust-based models are presented as a win–win scenario. It is clear, however, that there are also apparent dangers entailed with trust-based approaches. Especially one model, that of data trusts, may have far-reaching consequences.

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Beyond Open vs. Closed

Cited by 33 publications

References 42 publications

A Review on Artificial Intelligence in the Context of Industry 4.0

A Review on Artificial Intelligence in the Context of Industry 4.0

The Social Data Foundation model: Facilitating health and social care transformation throughdatatrust services

Can we trust trust-based data governance models?

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