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Identifying and discussing key topics in ethics for the RDA Community: Developing together a revised agenda for the Ethical and Social Aspects of Data (ESAD) Interest Group

  • Creator
    Discussion
  • #133941

    Oya Beyan
    Member

    Short talks: 

    Edit Herczog, The role of ethics in data science and in the RDA.
    “Ethics is a main component of the RDA mission. This includesbuilding technical and social bridges. This talk discusses why ethics is important as a systematic horizontal issue for RDA activities and how ethics supports the global challenges presented by data collection, data curation, and data re-use. It discussions ethics as part of the data ecosystem and why ethics is of importance to data scientists, policymakers, and funders.”

    Maria Christoforaki, Towards an ELSA Curriculum for Data Scientists.
    “The explosion of data driven applications in recent years has put Ethical, Legal, and Societal Aspects (ELSA) of data science in the spotlight. One way to tackle these issues is by raising awareness via education. In this short talk, I will present the work done so far towards an ELSA curriculum for data scientists that is currently in development in the framework of the FAIR DataSpaces project.”

    Ingvill Constanze Ødegaard, Collecting and sharing data in times of crises. 
    “Short description: wars, conflicts, environmental emergencies etc. have extreme impacts on humans and last over a long period of time. In order to develop evidence-based interventions data is required and often such data is collected by actors in the field whose primary aim is not research. However, this data is often highly important and this presentation will discuss the need to share this data, both for quality assessment and for ethical reasons.”

    P. Elif Ekmekci, Revisiting human rights, consent, and ethics review for data science: outcomes of the EOSC-Future/RDA Artificial Intelligence and Data Visitation Working Group (AIDV-WG)
    “This talk highlights the need for concrete guidance in data science for the application of AI, including data visitation. It highlights the results of the EOSC-Future AIDV-WG and how these results can be applied to the aims and outcomes of the RDA community. Data scientists are confronted with ethical considerations encompassing data privacy, consent, fairness, transparency, accountability, and data integrity. This presentation underscores the importance of establishing a comprehensive ethical framework when working with data and AI. The remarkable advancements in generative AI and data visualization demonstrate that current approaches to data ethics are inadequate in addressing the ethical challenges arising from their potential benefits and risks. This presentation will initially examine the fundamental distinctions in ethical issues and dilemmas arising from the utilization of (generative) AI as opposed to conventional technologies in data science. Subsequently, it will expound on how these ethical concerns can be effectively tackled through a broad ethical perspective, drawing upon the outcomes of the AIDV-WG.”

    Francis P. Crawley, Data in situ: context and ethics in data science
    “Context plays a critical role in understanding and utilising data effectively. This talk explores the significance of context as a key component of data utility and ethics in data science, examining the critical (though often overlooked or under-appreciated) role context has in responsible data practices. It also looks at how data context impacts AI, its applications and its effects on individuals and society.  Data science often focuses on extracting insights from vast amounts of data without explicitly considering the context in which the data is situated. However, context plays a pivotal role in shaping the meaning and interpretation of data. By examining data in situ, taking into account its origin, collection methods, biases, and underlying social, cultural, and political factors, we can develop a richer appreciation of its limitations, strengths, and potential implications. This is not only an ethics consideration, but also of principle importance for appreciating the full value of data and realising its ongoing and fullest potential.This talk will explore how context influences data interpretations, the potential consequences of overlooking context, and the ethical challenges that arise in data-driven decision-making processes. Practical approaches and guidelines for incorporating context and ethics into data science workflows will also be discussed.By emphasising the significance of context and ethics in data science, this talk aims to promote a more holistic and responsible approach to working with data. It encourages data scientists, researchers, and practitioners to consider the broader implications of their work and engage in ethical discussions surrounding data collection, analysis, and utilisation. Ultimately, by embracing context and ethics, we can foster a more inclusive, fair, and ethical data science practice that addresses the needs and values of individuals and society as a whole.”

    Daniel Mietchen, Making ethics workflows FAIR.
    “Short description: As ethics and data are interacting in ever more ways, the time is ripe for complementing the ethical review of data-related workflows with a FAIRification of ethics workflows and for establishing feedback loops that connect both ends and help improve them together. For instance, ethical oversight of AI could be designed in a way that both humans and machines can fully participate in the process, e.g. a user (human or machine) that interacts with an AI can check whether that AI actually has an ethics certificate that is valid for the ways in which the user intends to interact with the AI, the AI can check whether the user is authorized for the interaction, and if a publication results from these interactions, then the reviewer or reader (again, human or machine) of that publication also has some means to validate that these interactions have taken place on the basis of properly documented authorizations.”

    Dawei Lin, Ensuring long-term ethical data use through TRUST Principles.
    “The TRUST Principles for digital repositories provide a helpful framework to ensure user-focused data ethics over the long term. Here’s how each principle contributes to fostering user-focused data ethics:

    Transparency: Transparency serves as the foundation for building trust with users. Repositories should provide the clear information about the data collected, Its purposes of use, any third-party partnerships involved.
    Responsibility: Repositories should take responsibility for safeguarding user data and ensuring its ethical use.
    User focus: Upholding user-focused data ethics means prioritizing users’ interests, preferences, and rights.
    Sustainability: Long-term sustainability is crucial for maintaining user-focused data ethics, which involves securing sufficient resources to uphold data integrity, security, and privacy protections over time.
    Technology: Providing adequate technological support is essential to accomplish the mission of repositories.
    By adhering to the TRUST Principles, digital repositories can create an environment where user-focused data ethics are valued and upheld in the long term. Also, in turn, it helps build trust, promotes user confidence, and fosters responsible and ethical data management practices.”
    The talk includes reference to the  FAIR CARE and TRUST principals. 

     
    Discussion of Group Goals and Organization: 

    What are the shared interests in ethics by the RDA community? What should we focus on in ethics to bring more to the RDA community? How can others benefit from our work?  

    Brief analysis of what has impeded activity in recent years, and how to handle such impediments going forward.

    How to proceed: candidates for co-chairs

    How The IG can help existing  RDA WG-s and initiate new WG-s on concrete ethics related challenges

    Applicable Pathways
    FAIR, CARE, TRUST – Principles, Training, Stewardship, and Data Management Planning, Data Lifecycles – Versioning, Provenance, Citation, and Reward

    Avoid conflict with the following group (1)
    FAIR Data Maturity Model WG

    Brief introduction describing the activities and scope of the group
    The ESAD group was established in the Dublin plenary meeting with the goal of bringing researchers together to identify the ethical and social challenges of data reuse. The topics covered broad scale including social aspects, such as bias and potential harms at subpopulation / social groups level. In recent years, the group focused on data science ethics, aiming to create awareness and move towards solutions.  
     

    Estimate of the required room capacity
    20-30

    I Understand a Chair Must be Present at the Event to Hold the Breakout Session
    Yes

    Meeting objectives
     
    Click here for the collaborative session notes
     
    The aim of this group meeting is to reach out to the RDA community to collect shared interests for ethical and social aspects of data including data (ESAD). Though the RDA community is aware of the importance of the topic, the group was inactive in the last few years. In this meeting, we will bring RDA members who are working on or have an interest to work on ethical aspects of data and define new and emerging topics for the group to take forward in the next two years.
    In the first part, we will have short presentations reflecting the ongoing work of members. This will be followed by an open discussion in which the interest group’s goals, work program, and organization are revisited.

    Please indicate at least (3) three breakout slots that would suit your meeting.
    Breakout 2, Breakout 3, Breakout 4

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