The Embassy of Good Science - Recent changes [en]

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0009-0008-5042-4836 — Tue, 22 Sep 2026 15:12:40 GMT

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{{Instruction
|Title=Ethics Issues Identification (Module C.2)
|Has Related Initiative=Initiative:F4d945c9-461b-44f7-91ba-db70ca6332a1
|Instruction Goal=Module C introduces the AI Ethics Issues Identification process as the practical evaluation framework of the AIOLIA training programme. It translates the seven ALTAI requirements into an actionable, checklist-based methodology that enables learners to systematically detect, evaluate, and document ethical risks across real-world AI applications.

Building upon the core operational workflow established in Module B, this module anchors abstract ethics principles to concrete evaluation steps. Through guided theory, illustrative vignettes, and applied use case scenarios, it trains learners to move beyond passive awareness and rigorously analyze how complex issues—such as automation bias, proxy discrimination, data privacy erosion, black-box opacity, and loss of human agency—manifest across diverse operational contexts.

Learning Goals

* Master the 7 Assessment List for Trustworthy Artificial Intelligence (ALTAI) requirements and their core ethical dimensions.
* Apply Section 0 of the AIOLIA Issues Identification Checklist to define system scope, inputs/outputs, and affected stakeholder groups.
* Systematically evaluate AI systems across Sections 1–7 of the checklist using structured Likert-scale assessments and narrative scenario criteria.
* Distinguish between technical, organizational, and societal root causes of ethical risks within real-world deployments.
* Formulate clear, auditable issue statements to document identified risks, creating a reliable foundation for downstream mitigation strategies.
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|Instruction Step Title=Ethics Issues Identification
|Instruction Step Text=The practical identification of AI ethics issues requires applying the seven Assessment List for Trustworthy Artificial Intelligence (ALTAI) requirements to concrete operational settings using the AIOLIA Issues Identification Checklist. The evaluation process begins by Establishing Operational Context (Section 0), where practitioners define system parameters—including input/output specifications, user profiles, decision autonomy, and affected stakeholder groups—before assessing risk. Grounding risk identification in specific operational contexts prevents vague, generic evaluations and ensures that downstream risk assessments reflect actual deployment realities.

Across Sections 1–7 of the checklist, practitioners systematically evaluate real-world scenarios to detect failure modes and ethical vulnerabilities. Evaluated risks include subtle loss of student agency caused by over-reliance on adaptive AI tutoring prompts, severe safety hazards stemming from data poisoning in industrial monitoring tools, and privacy violations arising from unverified training data in organizational assistants. The evaluation further examines black-box opacity in automated risk scoring platforms, algorithmic bias and proxy discrimination in content moderation systems, skills degradation among domain experts, and unclear legal liability during clinical decision-support failures.

To complete the identification process, qualitative observations and Likert-scale evaluations are synthesized into Structured, Auditable Issue Statements. Each statement explicitly documents the affected ALTAI requirement, the precise operational context, the root cause of the vulnerability, and the potential severity of harm. This standardized documentation creates a clear audit trail, establishing the necessary empirical foundation for selecting targeted technical and organizational mitigation measures.
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0009-0008-5042-4836 — Tue, 22 Sep 2026 15:10:06 GMT

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 The framework executes through a '''5-Step Operational Workflow''':
-# '''Establish Context:''' Define the system's role, data sources, users, and affected stakeholders using Section 0 of the Issues Identification Checklist.
+#'''Establish Context:''' Define the system's role, data sources, users, and affected stakeholders using Section 0 of the Issues Identification Checklist.
-# '''Identify Ethics Issues:''' Systematically evaluate risks across problem formulation, data engineering, model development, deployment, and monitoring using Sections 1–7 of the checklist.
+#'''Identify Ethics Issues and Map them under ALTAI Requirements:''' Systematically evaluate risks across problem formulation, data engineering, model development, deployment, and monitoring using Sections 1–7 of the checklist.
-# '''Propose & Prioritise Practical Measures:''' Browse, assess, adapt, and combine technical measures (for developers) and organizational measures (for management) from the Portfolio of Measures.
+#'''Propose & Prioritise Practical Measures:''' Browse, assess, adapt, and combine technical measures (for developers) and organizational measures (for management) from the Portfolio of Measures.
-# '''Identify Ethics Tensions:''' Analyze and navigate unavoidable trade-offs between competing requirements, such as Privacy vs. Fairness or Technical Robustness vs. Transparency.
+#'''Identify Ethics Tensions:''' Analyze and navigate unavoidable trade-offs between competing requirements, such as Privacy vs. Fairness or Technical Robustness vs. Transparency.
-# '''Perform an Ethics Check:''' Validate findings and assemble auditable, traceable documentation of all decisions and residual risks.
+#'''Perform an Ethics Check:''' Validate findings and assemble auditable, traceable documentation of all decisions and residual risks.
 This workflow applies directly to real-world scenarios, such as the '''PhiShield''' case study. When an automated system flags an engineer (Marcus) with an 89% phishing vulnerability score after failing a test during an emergency 14-hour shift, it demonstrates how context-blind evaluations lead to unfair promotion blocks. Resolving such incidents requires applying technical sensitivity analysis (e.g., MS85), establishing a formal Right to Challenge (e.g., MS92), and enforcing mandatory human oversight before taking adverse personnel actions.

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 * 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
-|Has Duration=3
+|Has Duration=2
 |Important For=Doctoral Student; Educator / Lecturer; Ethics reviewers; IT / Technical Professional; PhD candidates; Policy Maker / Policy Maker / Regulator; Postdoctoral Researcher; Research Ethics Committee Member; Research Ethics Expert; Researcher; Undergraduate Student / Graduate Student
 }}

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 |Title=HLEG Guidelines for Trustworthy AI (Module A.4)
 |Has Related Initiative=Initiative:F4d945c9-461b-44f7-91ba-db70ca6332a1
-|Instruction Goal=This module introduces the ethics foundations of artificial intelligence (AI) within European policy frameworks. It covers the definition of an AI system according to the EU AI Act, along with core AI concepts and techniques.
+|Instruction Goal=This module introduces the European approach to Trustworthy Artificial Intelligence (AI) and the Ethics Guidelines for Trustworthy AI developed by the High-Level Expert Group on Artificial Intelligence (HLEG). It presents the four ethical principles and the seven key requirements for trustworthy AI, and explains how these principles can be translated into practical considerations for the design, development, deployment, and use of AI systems.
-It further explores the ethics-by-design approach, showing how ethics principles can be embedded throughout the AI lifecycle. The module also presents the requirements for trustworthy AI as defined by the High-Level Expert Group on AI and introduces the ALTAI framework as a practical self-assessment tool.
+The module also introduces the Assessment List for Trustworthy AI (ALTAI) as a practical self-assessment tool and presents the AIOLIA Ethics Readiness Levels (ERLs) methodology for supporting continuous ethics-by-design. It further provides an overview of relevant aspects of the EU AI Act, including its scope, risk levels, prohibited practices, and high-risk AI systems.
-Overall, this module equips learners with the essential prerequisite knowledge to understand AI fundamentals and serves as an introductory module to the course.
+'''Learning Goals'''
-Learning Goals
+* Understand the European approach to Trustworthy AI
+* Understand the four ethical principles of the HLEG Ethics Guidelines
-* Understand the definition of an AI system under the EU AI Act
+* Identify the seven key requirements for Trustworthy AI
 * Understand the purpose and basic use of the ALTAI framework
 |Has Duration=1
 |Important For=Policy Maker / Policy Maker / Regulator
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 |Instruction Step Text=AI System Definition under the EU AI Act
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 |Title=Core AI Concepts (Module A.2)
 |Has Related Initiative=Initiative:F4d945c9-461b-44f7-91ba-db70ca6332a1
-|Instruction Goal=This module introduces the ethics foundations of artificial intelligence (AI) within European policy frameworks. It covers the definition of an AI system according to the EU AI Act, along with core AI concepts and techniques.
+|Instruction Goal=This module introduces the core concepts and techniques underlying modern artificial intelligence (AI) systems. It explains the role of data in AI and introduces different machine learning approaches, neural networks and deep learning, large language models (LLMs), generative AI, and AI agents and agentic AI.
-It further explores the ethics-by-design approach, showing how ethics principles can be embedded throughout the AI lifecycle. The module also presents the requirements for trustworthy AI as defined by the High-Level Expert Group on AI and introduces the ALTAI framework as a practical self-assessment tool.
+The module also highlights important characteristics and limitations of these technologies, including the relationship between data quality and bias, the explainability challenges of neural networks, hallucinations in generative AI, and the increasing autonomy of AI agents.
-Overall, this module equips learners with the essential prerequisite knowledge to understand AI fundamentals and serves as an introductory module to the course.
+'''Learning Goals'''
-Learning Goals
+* Understand the role of data in AI systems
+* Become familiar with the main approaches to machine learning
-* Become familiar with core AI concepts and terminology
+* Understand the basic concepts of neural networks and deep learning
-|Has Duration=1
+* Understand the basic concepts of large language models and generative AI
 |Important For=Ethics reviewers
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+{{Instruction Perspective Trainee
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+|Title=AI System Definition: Core Concepts Check
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 |Title=AI System Definition (Module A.1)
 |Has Related Initiative=Initiative:F4d945c9-461b-44f7-91ba-db70ca6332a1
-|Instruction Goal=This module introduces the ethics foundations of artificial intelligence (AI) within European policy frameworks. It covers the definition of an AI system according to the EU AI Act, along with core AI concepts and techniques.
+|Instruction Goal=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.
-It further explores the ethics-by-design approach, showing how ethics principles can be embedded throughout the AI lifecycle. The module also presents the requirements for trustworthy AI as defined by the High-Level Expert Group on AI and introduces the ALTAI framework as a practical self-assessment tool.
+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
+* Understand the definition of an AI system under the EU AI Act
-*Become familiar with core AI concepts and terminology
+* Identify the main elements of the EU AI Act definition of an AI system
-*Explain the ethics-by-design approach
+* Distinguish AI systems from traditional rule-based or automated systems
-*Identify the key requirements for trustworthy AI
+* Understand the concepts of autonomy and adaptiveness in AI systems
-*Understand the purpose and basic use of the ALTAI framework
+* Recognise different types of AI system outputs, including predictions, content, recommendations, and decisions
 |Has Duration=1
 |Important For=Ethics reviewers; Policy Maker / Policy Maker / Regulator
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 {{Instruction Steps Foldout Trainee}}
 {{Instruction Perspective Trainee
-|Is About=This module introduces the basic concepts and ethics foundations of artificial intelligence (AI) within the European context. It explains what AI systems are according to the EU AI Act, presents key AI concepts, and explores how ethics requirements can be integrated into AI development through an ethics-by-design approach.
+|Is About=This module explains what an AI system is according to the EU AI Act. It introduces the key elements of the definition, including autonomy, adaptiveness, objectives, inference, outputs, and interaction with physical or virtual environments, using practical examples to illustrate these concepts.
+|Important Because=Understanding what constitutes an AI system is essential for recognising when the EU AI Act applies and for identifying the characteristics of systems that may raise ethical and regulatory considerations. This module provides the foundational understanding needed to explore AI ethics and the responsible development and use of AI systems in the following modules.
 }}
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 |Instruction Step Text=Based on Emilio Beladiez's interview (European Director of Plastic Oceans). Emilio defines environmental ethics as "coherence between what you do, what you want to achieve, and the methods you use". He also challenges us to stop focusing only on "downstream" consequences (cleaning up) and start acting "upstream" (changing policies, habits, and systems).
-Round 1: The Sorting Arena (Upstream or Downstream?)
+'''Round 1: The Sorting Arena (Upstream or Downstream?)'''
 Drag and drop the following laboratory actions into their correct strategic bin. Remember: Upstream targets the root cause/prevention, while Downstream deals with the waste already created.
-Action A: Organizing a waste-sorting workshop to ensure lab members separate hazardous, recyclable, and organic materials correctly.
+* '''Action A:''' Organizing a waste-sorting workshop to ensure lab members separate hazardous, recyclable, and organic materials correctly.
+* '''Action B:''' Switching our main chemical and consumable suppliers to certified "green" distributors who offer plastic-free packaging.
-· Action B: Switching our main chemical and consumable suppliers to certified "green" distributors who offer plastic-free packaging.
+* '''Action C:''' Running a monthly inventory audit to avoid over-ordering single-use plastic pipettes and tubes.
+* '''Action D:''' Putting plastic caps and non-contaminated lab plastics into specialized recycling collection bins.
-· Action C: Running a monthly inventory audit to avoid over-ordering single-use plastic pipettes and tubes.
+* '''Action E:''' Redesigning experimental protocols to replace plastic multi-well plates with reusable glass alternatives wherever scientifically viable.
 Exercise the following activity:
@@ Line 93: / Line 89: @@
 |Instruction Step Text=In his interview, Emilio Beladiez introduces two vital concepts for environmental action:
-. Environmental Ethics as Coherence: He defines ethics as the coherence between what you do, what you want to achieve, and the methods you use to get there.
+'''1. Environmental Ethics as Coherence''': He defines ethics as the coherence between what you do, what you want to achieve, and the methods you use to get there.
-. Upstream vs. Downstream Action: He distinguishes between downstream actions (tackling the visible consequences, like beach cleanups or waste recycling) and upstream actions (targeting the source of the problem, such as influencing policies, negotiating treaties, and changing how organizations operate). Exercise the following activity:
+'''2. Upstream vs. Downstream Action:''' He distinguishes between downstream actions (tackling the visible consequences, like beach cleanups or waste recycling) and upstream actions (targeting the source of the problem, such as influencing policies, negotiating treaties, and changing how organizations operate). Exercise the following activity:
 |Instruction Step Interactive Content=Resource:H5P-659
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 |Instruction Step Text='''<span lang="EN-GB">Instructions</span>'''
-# <span lang="EN-GB">Read the profiles of the four stakeholders below (A, B, C, and D).</span>
+#<span lang="EN-GB">Read the profiles of the four stakeholders below (A, B, C, and D).</span>
-# '''<span lang="EN-GB">Drag and drop</span>''' <span lang="EN-GB">each stakeholder into their correct     Stakeholder Position in the table:</span>
+#'''<span lang="EN-GB">Drag and drop</span>''' <span lang="EN-GB">each stakeholder into their correct     Stakeholder Position in the table:</span>
-#* '''<span lang="EN-GB">Core Actors:</span>''' <span lang="EN-GB">Those positioned at the heart of the      research process.</span>
+#*'''<span lang="EN-GB">Core Actors:</span>''' <span lang="EN-GB">Those positioned at the heart of the      research process.</span>
-#* '''<span lang="EN-GB">Strategic Actors:</span>''' <span lang="EN-GB">Those who influence      the broader structural, regulatory, or financial framework.</span>
+#*'''<span lang="EN-GB">Strategic Actors:</span>''' <span lang="EN-GB">Those who influence      the broader structural, regulatory, or financial framework.</span>
-#* '''<span lang="EN-GB">Contextual Actors:</span>''' <span lang="EN-GB">Those who represent      the local environment or demographic reality.</span>
+#*'''<span lang="EN-GB">Contextual Actors:</span>''' <span lang="EN-GB">Those who represent      the local environment or demographic reality.</span>
-# <span lang="EN-GB">Identify whether their role is primarily '''Affecting'''     (exercising power/control), '''Affected''' (vulnerable/impacted), or '''Both'''</span>
+#<span lang="EN-GB">Identify whether their role is primarily '''Affecting'''     (exercising power/control), '''Affected''' (vulnerable/impacted), or '''Both'''</span>
 '''<span lang="EN-US">Stakeholders to Categorize:</span>'''
-<span lang="EN-US">·        '''A.''' Community members "living on the edge of survival".</span>
+* <span lang="EN-US">'''A.''' Community members "living on the edge of survival".</span>
+* <span lang="EN-US">'''B.''' Research funders and government agencies.</span>
-<span lang="EN-US">·        '''B.''' Research funders and government agencies.</span>
+* <span lang="EN-US">'''C.''' The primary Research Team and study participants.</span>
+* '''<span lang="EN-GB">D.</span>''' <span lang="EN-GB">Junior colleagues, students, and international research partners</span><span lang="EN-US">.</span>
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 |Instruction Goal=To equip learners with the knowledge and practical skills to understand, participate in, and critically evaluate citizen science initiatives that contribute to climate and environmental research, monitoring, and evidence-based decision-making.
 |Has Duration=0.5
-|Important For=Students; Citizen Scientists; Researchers
+|Important For=Citizen Scientists; Researchers; Students
 }}
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 |Instruction Step Text='''Card 1: Bioindicators of Environmental Change'''
-· Front: Can observing specific animal species, like dragonflies or marine intertidal organisms, help us understand water quality and climate change?
+* '''Front''': Can observing specific animal species, like dragonflies or marine intertidal organisms, help us understand water quality and climate change?
+* '''Back''': Yes. These species act as bioindicators. By tracking their distribution and phenology (the timing of seasonal events), citizen scientists provide crucial data that reveals how local aquatic and marine ecosystems are shifting due to climate change and human impact
 '''Card 2: Recognition and Authorship'''
-· Front: Is a simple "thank you" in the acknowledgements sufficient for citizen scientists who provided the project's data?
+* '''Front:''' Is a simple "thank you" in the acknowledgements sufficient for citizen scientists who provided the project's data?
+* '''Back:''' While common in academic journals, Dave Wall argues that citizen scientists should ideally be included in the authorship list. He believes it is "their data" and researchers are simply the ones presenting it; therefore, proper acknowledgement is a matter of scientific integrity.
 '''Card 3: The Power of Open-Access Data'''
-· Front: Should citizen science data be restricted only to the primary research team or kept behind academic paywalls?
+* '''Front:''' Should citizen science data be restricted only to the primary research team or kept behind academic paywalls?
+* '''Back:''' No. To maximize its value, citizen science data should be open access. Making validated datasets freely available online allows for broad collaborations with other research institutes, state agencies, and environmental managers who can reuse the data to protect ecosystems
 '''Card 4: Expanding the Network'''
-· Front: If your citizen science volunteers are mostly well-educated, retired individuals, should you change your recruitment strategy?
+* '''Front:''' If your citizen science volunteers are mostly well-educated, retired individuals, should you change your recruitment strategy?
+* '''Back:''' Yes. While these groups provide valuable data, there is a constant need to reach underrepresented groups, such as inner-city communities or youth who have fallen out of formal education. This ensures the research is inclusive and reflects a wider range of societal perspectives.
 '''Card 5: Influencing Policy and Conservation'''
-· Front: Is the ultimate goal of citizen science solely to raise public awareness and change individual behaviors?
+* '''Front:''' Is the ultimate goal of citizen science solely to raise public awareness and change individual behaviors?
+* '''Back:''' No, though raising awareness is vital. The data collected by citizen scientists can directly influence public policy and conservation, such as being integrated into ecological sensitivity analyses to design and implement new Marine Protected Areas.
 |Instruction Step Interactive Content=Resource:H5P-655
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 |Guide Step Type Trainee=Instruction
 |Guide Step Element=Instruction:730cb733-bc5a-4f97-b235-1a00a30c8815
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 {{Guide Step Trainee

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 |Instruction Step Title=Ethics-by-design Concept
 |Instruction Step Text=When AI systems interact with human users, they directly influence cognition, decision-making, and behavior, shaping societal values, rights, and ethical norms. Ethics-by-Design is an approach that embeds ethical considerations into AI systems proactively from the very start of design and development, rather than attempting to rectify flaws post-deployment. Based on the European Commission's High-Level Expert Group guidelines for trustworthy AI, this methodology relies on seven core requirements: human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity, non-discrimination and fairness, societal and environmental wellbeing, and accountability. Implementation spans three crucial layers—the Data Layer, Model Layer, and Deployment Layer. Failure to integrate ethical controls throughout these layers has led to severe real-world harms, including systemic algorithmic discrimination in hiring, risk assessment, healthcare allocation, and social benefit delivery. Consequently, Ethics-by-Design serves as a vital operational mechanism for complying with mandatory regulatory frameworks such as the EU AI Act (2024) and align with guidance like the ALTAI framework to ensure trustworthy AI.
-|Instruction Step Interactive Content=Resource:H5P-669
+|Instruction Step Interactive Content=Resource:H5P-695
 }}
 {{Instruction Remarks Trainee}}

@@ Line 31: / Line 31: @@
 ===='''AI That Acts - From Agents to Agentic AI'''====
 Artificial Intelligence is evolving from passive content generation to active execution, distinguished by varying degrees of autonomy. While traditional AI Agents operate reactively within a narrow scope to complete specific tasks under explicit instructions, Agentic AI exhibits proactive behavior by setting priorities, breaking down complex goals into multi-step plans, and taking independent initiative over time. As AI systems gain greater operational autonomy and require less step-by-step human guidance, the necessity for formal oversight, clear accountability, and ethical safeguards increases significantly.
-|Instruction Step Interactive Content=Resource:H5P-668
+|Instruction Step Interactive Content=Resource:H5P-694
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 {{Instruction Remarks Trainee}}

@@ Line 35: / Line 35: @@
 Source: European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 on artificial intelligence (EU AI Act). Official Journal of the European Union. Article 3(1) <u>https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689</u>
-|Instruction Step Interactive Content=Resource:H5P-667
+|Instruction Step Interactive Content=Resource:H5P-693
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 |Has Requirements=<span lang="EN-GB">Educators who would like to use this resource are encouraged to familiarize themselves with the content of the course Sustainability and Eco‑Justice in Everyday Research. Before using this material in class, educators need to adapt activities according to their specific teaching objectives, contexts, and participants’ needs.</span>
 |Has Duration=2
-|Important For=Citizen Scientists; Ethics reviewers; Students; Researchers
+|Important For=Citizen Scientists; Ethics reviewers; Researchers; Students
 }}
 {{Custom TabContent Trainee Open}}
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 |Instruction Step Title=Familiarize yourself with the practice of feminist moderation
 |Instruction Step Text=This course promotes the use of feminist moderation practices to support the creation of safer spacer for inclusive dialogues. To learn more about this we encourage you to explore topics like safe spaces, polarisation, sensitive issues, and accessibility via course below.
-|Instruction Step Interactive Content=Resource:H5P-625
 }}
 {{Instruction Step Trainer

← Older revision		Revision as of 15:29, 22 September 2026
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 |Title=Going beyond harm: A Guide to environmental and climate considerations in research ethics
 |Has Related Initiative=Initiative:2e8a4c5b-0523-49cf-bcf9-5c3e9632be5a
-|Instruction Goal=The main goal ofthis module is to integrate environmental and climate considerations into research ethics beyond the identification and mitigation of environmental harm, by applying principles of responsibility and precaution, justice and fairness, and participation and inclusion throughout the design, conduct, and review of research.
+|Instruction Goal=The main goal of this module is to integrate environmental and climate considerations into research ethics beyond the identification and mitigation of environmental harm, by applying principles of responsibility and precaution, justice and fairness, and participation and inclusion throughout the design, conduct, and review of research.
 By the end of the module, learners will be able to:
-*Recognise environmental and climate-related ethical considerations that may arise in research, including situations where significant environmental harm is not expected.
+* Identify environmental and climate-related ethical considerations in research, including broader impacts beyond direct environmental harm.
-*Distinguish between conventional harm-based environmental risk assessment and broader ethical reflection on how research may influence environmental and societal trajectories.
+* Apply principles of responsibility and precaution to research design, methods, transparency, and outputs.
-*Apply principles of responsibility and precaution when making decisions about research design, assumptions, methods, transparency, and outputs.
+* Assess questions of justice and fairness, including the distribution of research benefits, risks, and burdens across groups and generations.
-*Assess questions of justice and fairness, including how research benefits, burdens, risks, and impacts may be distributed across different groups and generations.
+* Integrate diverse perspectives, forms of knowledge, and stakeholder interests through participatory and inclusive approaches.
-*Integrate participatory and inclusive approaches, recognising diverse perspectives, forms of knowledge, and stakeholder interests in research.
+* Reflect and act on environmental and climate-related ethical decisions by documenting, monitoring, and revising them throughout the research process.
 |Has Duration=0.5
 |Important For=Research Ethics Committee Member; Research Funding Organization; Researchers; Students

@@ Line 145: / Line 145: @@
 The proposed tool should be understood as an illustrative example of the types of questions that may support ethical reflection. Importantly, a range of relevant guidance documents, self-assessment tools, and review checklists addressing environmental and sustainability aspects of research ethics already exist at European, national, and institutional levels. These resources provide a strong foundation that RECs can draw on, adapt, and combine to suit disciplinary, institutional, and national contexts. Readers are encouraged to consult the resources listed below for further guidance.
 }}
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@@ Line 109: / Line 109: @@
 If you or your organisation have strong interests related to the project topic (for example employment in a company, NGO or public authority involved in the issue, or a role in a campaign or court case), you should tell the project team. This does not disqualify you from taking part; it helps avoid misunderstandings and supports fair interpretation of data especially when environmental or climate issues affect different groups differently
 }}
 {{Instruction Remarks Trainee}}

@@ Line 1: / Line 1: @@
 {{Instruction
-|Title=TAKING PART IN RESEARCH: A GUIDE FOR CITIZEN SCIENTISTS ON  ENVIRONMENTAL AND CLIMATE CONSIDERATIONS
+|Title=Taking part in research: A guide for citizen scientists on environmental and climate considerations
 |Has Related Initiative=Initiative:2e8a4c5b-0523-49cf-bcf9-5c3e9632be5a
 |Instruction Goal=By the end of this micro-module, learners will understand how to participate responsibly and ethically as citizen scientists in environmental and climate-related research, while recognising their rights, responsibilities, and the potential environmental and social impacts of their participation.
@@ Line 6: / Line 6: @@
 By the end of this micro-module, learners will be able to:
-* Explain the role of citizen scientists in environmental and climate research.
+* Understand the role of citizen scientists in environmental and climate research.
-* Identify their rights as participants, including access to clear information, voluntary participation, and the right to stop participating.
+* Recognise their rights and responsibilities as participants.
-* Recognise their responsibilities, including following project guidance, collecting and sharing data honestly and carefully, and respecting people and the environment.
+* Identify key information they should know before participating, including the project purpose, funding, organisations involved, and use of results.
-* Identify key project information they should understand before participating, such as the project purpose, coordinating organisations, funding, and intended use of results.
+* Recognise the environmental and social impacts of research activities and results.
-* Understand the environmental and social implications that research activities or results may have on communities, ecosystems, and future generations.
+* Understand the role of the Do No Significant Harm (DNSH) principle.
-* Recognise the importance of the Do No Significant Harm (DNSH) principle when participating in research activities.
+* Know what questions or concerns to raise with the project team.
-* Identify questions and concerns to raise with a project team before or during participation.
+* Reflect on whether a research activity is safe, transparent, respectful, and ethical.
-* Reflect on whether a research activity is safe, transparent, respectful, and ethically sound before taking part.
+* Recognise when additional safeguards may be needed, such as in health, life sciences, or biosecurity research.
 |Has Duration=0.5
-|Important For=Researchers; Citizen Scientists; Students
+|Important For=Citizen Scientists; Researchers; Students
 }}
 {{Custom TabContent Trainee Open}}

@@ Line 6: / Line 6: @@
 By the end of the module, learners should be able to:
-* Understand how research can create environmental and social footprints, both directly and through its applications.
+* Recognise the environmental and social impacts of research.
-* Recognise environmental and climate responsibility as part of responsible research practice and research integrity.
+* Understand environmental and climate responsibility as part of research integrity.
-* Understand the relationship between the European Code of Conduct for Research Integrity (ECoC) and environmental/climate considerations.
+* Connect the ECoC and DNSH principle to environmental and climate considerations.
-* Understand the role and limitations of the Do No Significant Harm (DNSH) principle.
+* Apply environmental and climate considerations across the research lifecycle.
-* Apply environmental and climate considerations across the research lifecycle from research questions and methods to practices and outputs.
+* Identify responsible and potentially problematic research practices from an environmental and climate perspective.
-* Recognise what constitutes good research practice and potentially problematic or irresponsible practices from an environmental and climate perspective.
+* Balance environmental responsibility with research freedom and independence.
 |Has Duration=1
 |Important For=Research Ethics Committee Member; Research Funding Organization; Research Supervisor / Mentor; Researchers

@@ Line 30: / Line 30: @@
 At a broader level, '''Societal and Environmental Well-Being''' evaluates the systemic impacts of AI deployment, including psychological impacts, democratic discourse, and the ecological footprint of model training and inference. Finally, '''Accountability''' establishes mechanisms for auditability, risk assessment, and clear human oversight to ensure legal and moral responsibility when harms or errors occur.
 |Instruction Step Interactive Content=Resource:H5P-672
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@@ Line 1: / Line 1: @@
 {{Instruction
-|Title=Introduction to ALTAI Requirements
+|Title=Introduction to ALTAI Requirements (Module C.1)
 |Has Related Initiative=Initiative:F4d945c9-461b-44f7-91ba-db70ca6332a1
 |Instruction Goal=Module C introduces the AI Ethics Issues Identification process as the practical evaluation framework of the AIOLIA training programme. It translates the seven ALTAI requirements into an actionable, checklist-based methodology that enables learners to systematically detect, evaluate, and document ethical risks across real-world AI applications.
@@ Line 13: / Line 13: @@
 * Distinguish between technical, organizational, and societal root causes of ethical risks within real-world deployments.
 * Formulate clear, auditable issue statements to document identified risks, creating a reliable foundation for downstream mitigation strategies.
-|Has Duration=5
+|Has Duration=1
 |Important For=Educator / Lecturer; Ethics reviewers; IT / Technical Professional; Policy Maker / Policy Maker / Regulator; Research Ethics Committee Member; Research Ethics Expert; Researcher
 }}