AI Ethics and Societal Aspects of Biomedical Informatics

Introduction: Stakeholder management (ShM) is essential for healthcare

projects, particularly for medical data platform development, which involves diverse

stakeholders and complex socio-technical challenges spanning ethical, legal,

technical, and human and organizational dimensions. While theoretical frameworks

exist, their practical implementation often remains unclear. Operational ShM

frameworks that provide actionable guidance are critical to ensure that project

managers can effectively engage stakeholders to improve project outcomes.

Methods: A scoping literature search in Google Scholar and Scopus screened the

top 30 results from each database for studies presenting operational ShM,

stakeholder analysis (ShA), or engagement (ShE) frameworks in healthcare research

or institutions. Ten studies were included. Results: Frameworks varied in scope,

methods, and completeness; four focused on ShA, four on ShE, and two covered the

full ShM process. Only one study provided a comprehensive guideline spanning all

steps and including continuous stakeholder management. Conclusion: Operational

ShM frameworks in healthcare remain scarce, highlighting the need to develop a

practical, actionable framework providing clear guidance for stakeholder

management. Such a framework is critical for effectively coordinating diverse

stakeholders and supporting project managers in implementing robust, context-sensitive

digital health infrastructures.

Dataset and Study Report

The upload consists of the two following files: 

1. The study report on the Ethical, Legal, and Societal Aspects of Data Science, as manifested through a series of Interviews conducted within the framework of NFDI4DataScience Task Area 1: Community and Training. In the interviews, data science researchers and practitioners were asked to identify and analyse the most common legal and ethical challenges encountered in DS projects and how they address them. The objective was to use the collected information to create ELSA guidelines for Data Scientists. The file consists of the study guide itself and the following supplementary material:

   1. Call for participants email

   2. Call for participants email

   3. Email to the interviewees explaining the process

   4. Consent form template

   5. Anonymity Policy Clarification email

   6. Anonymisation Protocol

2. The anonymized transcripts of the interviews

Attention deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder. Gender disparities in the diagnosis of ADHD have been reported, suggesting that females tend to be diagnosed later in life than males are. The delayed diagnosis in females has been attributed to an inequality in the diagnostic criteria, failing to focus on the gender differences regarding symptomatology, comorbidity, and societal factors contributing to this disparity.

In this study, we introduced debiased classifiers for the diagnosis of ADHD via different bias mitigation algorithms of the AI Fairness 360 toolbox on a training dataset of 400 children and adolescents with and without ADHD (98 females, 25 ADHD patients, 73 typically developing females), a subsample of the Child Mind Institute dataset. Test data were acquired in an earlier study. Two datasets were used, one including personal characteristic features, scores of the clinical questionnaire Child Behavior Checklist, and wavelet variance coefficients as quantifiers of neural dynamics (fMRI), a second dataset included personal characteristic features, scores of the clinical questionnaire Child Behavior Checklist, and radiomic features of neural structure (sMRI).

We found that the reweighed XGBoost model achieved the best accuracy and highest fairness in both datasets. Using model explanation, we showed how reweighing influenced feature importance at the global and local levels.

Based on methodological characteristics and insights from global and local model explana-tion, we discuss the reasons of these findings and conclude, that using the combination of bias mitigation and model explanation, improved classification models can be achieved.

Data Science (DS) and Artificial Intelligence (AI) are transforming research, industry and society at an unprecedented pace, enabling advances in areas such as healthcare, finance, e-commerce and beyond. Despite their potential, the rapid development and widespread use of DS and AI raise (novel) issues of reliability, accuracy, copyright and data protection, and bias and discrimination, among others, see for instance [1], [2], [3], [4]. It is therefore vital for data scientists to acknowledge the Ethical, Legal and Societal Aspects (ELSA) encountered in DS and AI projects, as this can promote critical thinking and reflection, thereby ensuring that data-driven systems and their underlying technologies are developed, deployed and used responsibly. In the framework of NFDI4DataScience, specifically in the Community and Training task area, we aim to develop ELSA guidelines for data scientists [5]. In order to achieve this objective, we tried to assess the landscape by conducting interviews with researchers and practitioners in the field, aiming to identify and analyse the most common ELSA challenges encountered in DS/AI projects and how to cope with them. The interviews were semi-structured interviews as this form is well suited to our purpose of collecting experiences, reflections and opinions from the participants [6]. A total of 30 were conducted between November 2022 and February 2024. The participants came mainly from academia, but the industry was also well represented. The application domains included a.o., healthcare, finance, engineering and digital humanities. In order to systematically interpret the material for manifest and latent meanings, we used qualitative content analysis [7]. Consequently, a categorisation of the material was developed to provide the basis for this interpretation [8, p. 33]. Initial categories were developed deductively, derived from the interview guide, which itself was based on existing theory and research. Subcategories were created inductively from the interviews following initial coding with the main categories. The categories reflected the key ELSA challenges faced by data scientists, including data protection, but also more specifically fairness, transparency, consent, intellectual property, and data scientists' knowledge (and also attitudes) towards ELSA challenges and how they influence their decision-making processes in the project. The results of our analysis reveal that data scientists are generally aware of ELSA issues, some more acutely than others; for example, legal issues, especially data protection, are more prominent, especially in application domains such as healthcare; bias is considered more during the data collection and less in connection to the model used or the system deployment; issues of transparency and explainability are also crucial although not prevalent. Insight was also provided regarding interdisciplinary cooperation, institutionalised ELSA support, and project documentation. Additionally, we have recorded critical assessments of the practices followed, spanning from issues with the application of laws to the responsibility and accountability of practitioners during a project life cycle. Finally, our findings emphasise the necessity of enhancing ELSA literacy and establishing and providing a strong foundational understanding of ethical and legal principles to data scientists. Developing recommendations/best practices for data scientists was regarded as a positive first step towards this goal

Abstract

The use of artificial intelligence (AI) applications in a growing number of domains in recent years has put into focus the ethical, legal, and societal aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA curriculum for data scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts in order to cooperate to find appropriate solutions, and finally, incorporate ELSA in the data science workflow. ELSA should not be seen as an impediment or a superfluous artefact but rather as an integral part of the Data Science Project Lifecycle. The proposed curriculum uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) model as a backbone to define a vertical partition expressed in modules corresponding to the CRISP-DM phases. The horizontal partition includes knowledge units belonging to three strands that run through the phases, namely ethical and societal, legal and technical rendering knowledge units (KUs). In addition to the detailed description of the aforementioned KUs, we also discuss their implementation, issues such as duration, form, and evaluation of participants, as well as the variance of the knowledge level and needs of the target audience.

Abstract

The explosion of data-driven applications using Artificial Intelligence (AI) in recent years has given rise to a variety of ethical issues regarding data collection, annotation, and processing using mostly opaque algorithms, as well as the interpretation and employment of the results of the AI pipeline. The ubiquity of AI applications negatively impacts a variety of sensitive areas, ranging from discrimination against vulnerable populations to privacy invasion and the environmental cost that these algorithms entail, and puts into focus on the ever present domain of AI ethics. In this review article we present a bird’s eye view approach of the AI ethics landscape, starting from a historical point of view, examining the moral issues that were introduced by big datasets and the application of non-symbolic AI algorithms, the normative approaches (principles and guidelines) to these issues and the ensuing criticism, as well as the actualization of these principles within the proposed frameworks. Subsequently, we focus on the concept of responsibility, both as personal responsibility of the AI practitioners and sustainability, meaning the promotion of beneficence for both the society and the domain, and the role of professional certification and education in averting unethical choices. Finally, we conclude with indicating the multidisciplinary nature of AI ethics and suggesting future challenges.