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  • Call for Papers: Ethical and Responsible Commitments for Sharing (FAIR) Data in Engineering Sciences

    Call for Papers: Ethical and Responsible Commitments for Sharing (FAIR) Data in Engineering Sciences

    Posted by Izadora Silva Pimenta on 2024-06-25


As Artificial Intelligence (AI) continues to evolve, researchers are increasingly grappling with its implications. For instance, psychologists and neuroscientists are working on explainable AI (XAI) to understand how AI systems "think," with Large Language Models (LLMs) posing significant challenges [1][2]. 

Additionally, there is concern about how the transformation of data in its outcomes can affect data sovereignty, considering issues of data usage, bias, and transparency. When we initiate data cycles, we must consider the implications of the data we share with the world. This process has significant social implications, and we bear responsibility for its consequences [3]. 

Reflecting on these issues, ing.grid would like to invite scholars to contribute to the conversation on ethical and responsible data sharing in the Engineering Sciences.

Expanding what FAIR Data means

What truly makes our data FAIR – if we consider the original meaning of this acronym? Sharing data with unrestricted openness can have other implications. For example, the CARE principles [4], which address handling indigenous data, highlight the importance of the collective benefit of data. How can we protect the rights and responsibilities of our data throughout its life cycle? [5] How should we ensure compliance with regulations such as the General Data Protection Regulation (GDPR)? How can we avoid misuse or unethical applications of our data?

We need to ask ourselves: What makes data truly reusable? How can FAIR Data facilitate and enhance responsible data reuse [6]? How can we ensure methodological clarity so that others understand the type of data they are reusing [7]? How can we achieve ethical data sharing amidst the growing use of AI to enhance results? Are our data being considered with an intersectional lens before initiating their life cycle? [8]

Call for Contributions

We seek submissions that explore how we can rethink current standards in the Engineering Sciences to promote ethical and responsible data sharing. We welcome:

•    Manuscripts: Detailed discussions on methodology and advancements in ethical data sharing.
•    Data Descriptors: Descriptions of data sets that emphasise clarity and responsibility in their methodological aspects.
•    Software: Tools that support responsible data sharing and reuse.

We are particularly interested in contributions that advance the discussion on data sovereignty in the European Union, its relationships with AI and LLMs, possible issues with dual use of research data in Engineering Sciences [9] and intersectional approaches for Engineering Sciences data. However, any other topics that can contribute to this discussion are welcome.

Contributions for this issue will be accepted on a rolling basis until March 31st, 2025. 

ing.grid is still and always open for unsolicited submissions regarding other topics that fit our focus and scope.

Data Management Letters

We also invite suggestions for Data Management Letters on this topic. Data Management Letters are invitation-only submissions that are not peer-reviewed. They can take the form of opinion pieces, interviews, or short reports.

If you wish to contribute, please submit an abstract (max 200 words) to editors@inggrid.org.

We look forward to your contributions and to advancing the conversation on ethical data sharing in our field.

Responsible editors for this issue:

Prof. Peter Pelz - Chair of Fluid Systems - Technische Universität Darmstadt

Prof. Petra Gehring - Theoretical Philosophy - Technische Universität Darmstadt; scientific director of the Centre for Responsible Digitality (ZEVEDI)

Further reading:

•    Petra Gehring - Sharing Data - Is It All About An "Openness" Economy?
•    Izadora Silva Pimenta - Critically thinking about the reusability of (meta)data
•    Understanding the provenance and quality of methods is essential for responsible reuse of FAIR data
•    How does ChatGPT ‘think’? Psychology and neuroscience crack open AI large language models
•    AI Intersections Database
•    A critical field guide for working with machine learning datasets
•    Advancing data justice research and practice

References

[1] https://www.nature.com/articles/d41586-024-01314-y 
[2] https://www.hhi.fraunhofer.de/en/departments/ai/research-groups/explainable-artificial-intelligence/research-topics/applications-of-xai.html 
[3] https://www.inggrid.org/article/id/3945/ 
[4] https://www.gida-global.org/care
[5] https://op.europa.eu/en/publication-detail/-/publication/c016d11f-7f52-11ed-9887-01aa75ed71a1/language-en
[6] https://www.nature.com/articles/s41591-024-02879-x 
[7] https://osf.io/preprints/osf/x85gh


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