Goal (Instruction Goal)
From The Embassy of Good Science
0
Members of The Embassy of Good Science have developed a set of six user-friendly, accessible methods for analysing research ethics and research integrity cases.
These methods have been identified, adapted and presented so that they can be appropriated by all users, without prior philosophical knowledge, in local contexts. +
02 - The Seven Steps Method: A Method for Analysing Cases in Research Ethics and Research Integrity +
Members of The Embassy of Good Science have developed a set of six user-friendly, accessible methods for analysing research ethics and research integrity cases.
These methods have been identified, adapted and presented so that they can be appropriated by all users, without prior philosophical knowledge, in local contexts. +
03 - Four Quadrant Approach: A Method for Analysing Cases in Research Ethics and Research Integrity +
Members of The Embassy of Good Science have developed a set of six user-friendly, accessible methods for analysing research ethics and research integrity cases.
These methods have been identified, adapted and presented so that they can be appropriated by all users, without prior philosophical knowledge, in local contexts. +
04 - Moral Case Deliberation: A Method for Analysing Cases in Research Ethics and Research Integrity +
Members of The Embassy of Good Science have developed a set of six user-friendly, accessible methods for analysing research ethics and research integrity cases.
These methods have been identified, adapted and presented so that they can be appropriated by all users, without prior philosophical knowledge, in local contexts. +
Members of The Embassy of Good Science have developed a set of six user-friendly, accessible methods for analysing research ethics and research integrity cases.
These methods have been identified, adapted and presented so that they can be appropriated by all users, without prior philosophical knowledge, in local contexts. +
Members of The Embassy of Good Science have developed a set of six user-friendly, accessible methods for analysing research ethics and research integrity cases.
These methods have been identified, adapted and presented so that they can be appropriated by all users, without prior philosophical knowledge, in local contexts. +
A
The aim of this module is to facilitate reflection upon the ethics issues associated with the development and use of non-human gene editing in a research project.
'''Learning outcomes'''
At the end of this module, learners will be able to:
#Identify and analyse the ethics issues and dilemmas associated with an example research proposal.
#Make suggestions for how the ethics issues might be addressed.
#Identify ethics guidelines and policies that are relevant to the proposed research. +
The aim of this module is to facilitate reflection upon the cross-cutting ethics issues associated with a research proposal to use AI-driven analytics to understand malaria transmission patterns in rural Sub-Saharan Africa.
== Learning outcomes ==
At the end of this module, learners will be able to:
# Identify and analyse the ethics issues and dilemmas associated with a hypothetical research proposal.
# Make suggestions for how the ethics issues, including ethics dumping, might be addressed.
# Identify ethics guidelines and policies that are relevant to the proposed research. +
Module B introduces the AI Ethics Operationalisation Workflow as the central operational framework of the AIOLIA training programme. It translates high-level AI ethics principles into a structured, step-by-step workflow that supports the practical assessment, identification, and mitigation of ethics issues in AI systems across their lifecycle. The module builds on three core building blocks—ethics requirements, practical measures, and ethics tensions—which collectively support the operationalisation of the seven ALTAI requirements in real-world contexts. Illustrated through a practical use case scenario, it guides learners through a 5-step iterative process and establishes the operational backbone of the AIOLIA methodology.
'''Learning Goals'''
* Understand the 5-step AI Ethics Operationalisation Workflow and its role in bridging theory and practice
* Use Section 0 of the AIOLIA Issues Identification Checklist to establish the operational context of an AI system
* Identify potential AI ethics risks and map them under the seven ALTAI requirements using Sections 1–7 of the checklist
* Navigate, select, and adapt context-appropriate measures from the AIOLIA Portfolio of Measures (122 technical and organizational measures)
* Recognize and analyze ethics tensions and trade-offs that arise between competing ALTAI requirements
* Perform an Ethics Check to validate findings, ensuring completeness, traceability, and auditable documentation +
The aim of this module is to support research ethics reviewers in learning about AI technologies for the review of projects and proposals that develop and/or use AI for healthcare.
The content focuses on key technology basics in a succinct manner, and signposts further learning opportunities for those who require more in-depth knowledge.
'''Learning outcomes'''
At the end of this module, learners will be able to:
#Identify AI systems and how they are built.
#Discuss some key applications of AI-based systems in healthcare.
#Discuss the primary implications of the use of AI in healthcare. +
This module introduces the definition of an AI system according to the EU AI Act. It helps learners understand the key elements of the definition and recognise what distinguishes an AI system from other types of software and automated systems.
The module explores the main characteristics of AI systems, including their machine-based nature, varying levels of autonomy, potential adaptiveness, objectives, ability to infer how to generate outputs, and interaction with physical or virtual environments. Examples and practical scenarios are used to illustrate how these elements apply to different types of AI systems.
'''Learning Goals'''
* Understand the definition of an AI system under the EU AI Act
* Identify the main elements of the EU AI Act definition of an AI system
* Distinguish AI systems from traditional rule-based or automated systems
* Understand the concepts of autonomy and adaptiveness in AI systems
* Recognise different types of AI system outputs, including predictions, content, recommendations, and decisions
* Understand how AI systems can influence physical and virtual environments +
The aim of this module is to support Research Ethics Committee Member in learning about and reflecting upon the ethics issues associated with the development and use of AI technologies in healthcare.
'''Learning outcomes'''
At the end of this module, learners will be able to:
#Explain the relevance of informed consent, transparency and explainability for AI in healthcare.
#Describe the data-related ethics issues for AI in healthcare.
#Reflect upon broader ethics issues (like social values and the environmental impact) related to AI in healthcare.
#Access relevant guidelines and regulations for the use of AI in healthcare (e.g., the European Commission Guidelines for AI Research) +
This module will help you to add your project outputs as resources on the Embassy! Adding resources to the Embassy is the best way to highlight your educational materials, guidelines, and project deliverables in a way that makes them accessible to users across the platform +
This module will teach you how to add a module to the Embassy of good science. +
This module provides an overview of the different functionalities available to users of the Embassy, with videos showing you how you can contribute! +
This micromodule introduces key ethical principles that are relevant to environmental justice in research practices. After completing this micro module learners will be able to:
*'''<span lang="EN-US">Analyze</span>''' <span lang="EN-GB">the environmental implications of research through the lens of ethical principles related to environmental justice</span>
*'''<span lang="EN-GB">Apply</span>''' <span lang="EN-GB">each principle to research practice by responding</span> <span lang="EN-US">to questions that prompt critical reflection</span> +
This micromodule introduces Life Cycle Assessment (LCA) as a structured tool for evaluating environmental impacts across chains. It aims to develop foundational understanding of life cycle thinking, highlight sustainability challenges, and demonstrate how LCA supports evidence-based decision-making, identifies environmental hotspots, and addresses uncertainty in complex systems such as healthcare delivery and agro-food logistics.
At the end of this module, students will be able to:
*Explain the concept and purpose of Life Cycle Assessment (LCA).
*Describe the key stages of supply chains using illustrative cases from healthcare and research practice.
*Apply life cycle thinking (cradle-to-grave) to real-world products.
*Identify major environmental impacts and hotspots across supply chains.
*Understand how LCA supports sustainable and evidence-based decision-making. +
This micromodule provides an introduction to Ecofeminist Ethics and its relevance for research. A completing this module, learners will be able to:
* '''Understand''' the role of ecofeminist principles in research,
* '''Apply''' relevant ecofeminist principles to various research dilemmas. +
Bu bölümde bulunan talimatlar, katılımcılara (yüz yüze) grup oturumları arasında yapılması gereken çalışmaları nasıl organize edecekleri hususunda bilgi vermektedir. +
Bu modülde, araştırma doğruluğuyla ilgili genel ilkelere giriş yapılmakta, Araştırmalarda Dürüstlük Konusunda Avrupa Davranış Kodu (ECoC)’nun çizdiği kılavuz niteliğindeki çerçeve açıklanmakta ve sizden ECoC’u kendi karşılaştığınız durumlara uyarlamanız istenmektedir. +
