Erhalten Sie Zugang zu diesem und mehr als 300000 Büchern ab EUR 5,99 monatlich.
Visual Integrity in Times of Crisis – Strategies for Dealing with AI-Generated Images In disaster situations, every second and every image counts. AI-generated representations can distort perception, destabilize communication, and influence operational decisions on a global scale. This book presents the Semantic Integrity Framework for Disaster Imagery: a strategically developed control system for the evaluation, contextualization, and safeguarding of synthetic visual content. The framework combines semantic precision, visual traceability, and operational decision logic. It is modular in structure, adaptable both visually and linguistically, and deployable worldwide. Core control elements such as semantic threshold definitions, role-based governance logics, and communication-relevant impact assessments enable robust integration into existing structures. More than a technical solution, the framework offers a strategic infrastructure for visual governance in a fragmented, high-risk information environment. It supports traceable, context-aware decisions in real time and strengthens trust and coordination in volatile operational landscapes. Training formats, stakeholder engagement, and pattern recognition for semantic escalation expand the framework and enhance its global interoperability. It lays the foundation for future role models, visual governance, and resilient handling of synthetic media. This work is aimed at authorities, emergency responders, media, platforms, organizations, and individuals who bear responsibility in critical moments. It provides reliable tools for strengthening visual resilience and for the strategic management of visual content in complex crisis contexts. A book for all who seek to rethink visual responsibility and actively shape global crisis resilience.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 385
Veröffentlichungsjahr: 2025
Das E-Book (TTS) können Sie hören im Abo „Legimi Premium” in Legimi-Apps auf:
Ähnliche
Imprint
Birgit Bortoluzzi
Burgwartstraße 25
01159 Dresden
Germany
Text: Copyright by Birgit Bortoluzzi
Cover Design: Copyright by Birgit Bortoluzzi
Publisher: Birgit Bortoluzzi, Burgwartstraße 25, 01159 Dresden, Germany
This edition has been translated from the original German and is an expanded edition of Version 1.0 (Oktober 2025)
Title of the original German edition: Semantic Integrity Framework für Katastrophenbilder – Strategische Architektur für visuelle Integrität im Disaster Management
Note: This book was published independently. Distribution is provided by epubli – a service by neopubli GmbH, Berlin.
Distribution: epubli – a service by neopubli GmbH, Berlin
Copyright and Usage Rights: © 2025 Birgit Bortoluzzi. All rights reserved.
This publication - including its terminology, semantic architecture, governance logic and all visual and structural elements - is the intellectual property of the author. Redistribution, adaptation or translation of any part of this work (textual, visual or structural) is permitted only with explicit attribution and in accordance with the ethical principles outlined herein.
Collaborative use in humanitarian, academic or institutional contexts is expressly welcomed - provided that transparent governance agreements are in place. Commercial use or modification requires prior written consent.
Visual Material and Cover Design: All images, illustrations and graphic elements used in this book - including the cover and visual modules - are protected by copyright. Their use outside this publication is permitted only with explicit authorization and in accordance with the principles of semantic integrity.
Disclaimer: The contents of this book have been prepared with the utmost care and to the best of the author’s knowledge. They serve as strategic guidance, ethical reflection and operational support in complex crisis contexts. However, they do not replace individual consultation by qualified professionals, authorities or legal experts.
The author assumes no liability for decisions made on the basis of this work, particularly not for direct or indirect damages resulting from the application, interpretation or dissemination of its contents. Responsibility for use lies with the respective users and institutions.
About the Author
Birgit Bortoluzzi is a strategic architect, certified disaster manager, accredited marketing and social media PR professional and coach. Her core expertise lies in crisis and disaster resilience, as well as in the development of strategic and semantic frameworks. As the initiator of the Semantic Integrity Framework, she designs internationally adaptable concepts for evaluating and governing visual content in crisis contexts. Her work integrates operational decision logic, ethical communication strategies, and interdisciplinary data sources — with a clear goal: to redefine visual responsibility and global response capability.
She is an active member of the IEEE GRSS Disaster Management Study/Working Group and has presented innovative approaches for emergency responders at the Pracademic Emergency Management and Homeland Security Summit 2025 (Embry-Riddle University). Her engagement in international networks aims to unite diverse perspectives and foster systemic 360-degree thinking.
At the heart of her work is the relief of our everyday heroes — those emergency responders who operate on the front lines around the world, day after day. Versatile, profound, and driven by curiosity — that may be the most fitting description of Birgit’s nature. She never wanted to merely observe the world, but to experience, understand, and shape it in her own way. From an early age, she was fascinated by the worlds of communication, strategy and scenario planning, event and organizational management, holistic analysis, marketing, photography, painting, poetry, writing, and dream journeys — as well as by pharmacokinetics, chemistry, natural sciences, and nutrition.
Her thinking is not only interdisciplinary, but deeply human — guided by the desire to structure complexity, share responsibility, and shape a future with meaning.
Foreword
In disaster and crisis situations, every second and every image counts. Visual content not only shapes the perception of events but also influences decisions regarding resources, communication, and operational priorities. With the emergence of AI-generated visual worlds, a new challenge has arisen. Synthetic disaster representations are not neutral illustrations, they may act as powerful carriers of disinformation.
Even when marked with watermarks or AI indicators, such images may be perceived as authentic in dynamic emergency scenarios. They distort situational assessments, destabilize communication chains and lead to misjudgments — with consequences for command units, media representatives, and humanitarian organizations alike. The effects range from misallocated resources and loss of trust to reduced operational efficiency. A synthetic image created in one country can circulate globally within minutes and influence assessments in other regions. Natural disasters, humanitarian crises or technological disruptions often affect multiple countries simultaneously, visual disinformation knows no borders.
This book has been created over the past months with great dedication, as a strategic response to the growing challenges of visual integrity in the context of global disaster resilience. It is not a conventional rulebook, but the result of an interdisciplinary architectural process specifically designed for use in disaster and crisis management. The Semantic Integrity Framework combines semantic precision, visual traceability and operational decision logic into a modular control system that can be flexibly integrated into existing structures. Throughout this book, you will encounter core control elements such as semantic threshold definitions, institutionally embedded decision logics, role-based access systems (Application Programming Interface, API) and communicative impact assessments (Public Information Officer, PIO) — embedded in a framework that not only protects visual integrity but strategically operationalizes it.
In an era of profound disruption, it is no longer enough to slightly improve existing structures. The challenges are growing faster than our capacity to adapt and the temptation to wait for the next major disaster is perilous. This book is not a reaction to the status quo, but a strategic response to the systemic inertia that allows crises to escalate. The Semantic Integrity Framework was designed not merely to protect visual responsibility, but to shape it, an architecture for transformation, not for tinkering.
The framework is both visually and linguistically adaptable, making it deployable worldwide. It includes modules for training and stakeholder engagement that enable sustainable institutional anchoring. Furthermore, the architecture supports early detection of semantic escalation patterns, the definition of differentiated response profiles for affected and external target groups and the ethically grounded contextualization of synthetic content.
The modules of this framework go far beyond technical control: they address the psychosocial impact of synthetic images, the operational relevance of visual deception, the cultural coding of crisis motifs and the ethical evaluation of symbolic image compression. Particularly noteworthy is the development of interdisciplinary training formats that combine semantic image literacy, ethical reflection, and critical platform analysis. Authorities, organizations and media actors thus receive practical tools to strengthen visual resilience, promote ethical decision-making confidence and strategically embed visual responsibility in complex operational contexts.
This framework is aimed at authorities, emergency responders, media, social media platforms, organizations and individuals who bear responsibility in critical moments, whether operational, communicative or societal. It offers a robust response to the increasing presence of AI-generated images in the information landscape — not through control, but through strategic design. As part of this book, key role modules such as Trust Threshold Sensor, Post-Crisis Review, Context Anchoring, Critique Amplification and Transparency Mediation have been strategically developed. They form the ethical foundation for visual integrity in AI-supported disaster management. Additional conceptually anchored roles, such as for auditing, semantic escalation prevention or symbolic context analysis — complement this system as depth modules that invite further development.
I warmly invite you not only to use this framework but to evolve it and integrate it into your own operational logic. Because visual integrity is not a luxury, it is a prerequisite for effective crisis communication and international capacity to act. In a world shaped by images, the responsibility to act with semantic clarity and ethical foresight lies with all of us — across disciplines, sectors and borders.
Birgit Bortoluzzi
Graduate Disaster Manager (WAW), Strategy Planner
Version 1.0 – First Edition
Semantic Integrity Framework for Disaster Imagery
Developed and authored by Birgit Bortoluzzi
Dresden (Germany), November 2025
© 2025 Birgit Bortoluzzi. All rights reserved. This publication — including its terminology, semantic architecture and governance logic — is the intellectual property of the author. Redistribution, adaptation or translation of any part of this work (textual, visual or structural) is permitted only with explicit attribution and in accordance with the ethical principles outlined herein. Collaborative use in humanitarian, academic or institutional contexts is welcomed under transparent governance agreements. Commercial use or modification requires prior written consent.
Cover Image Description – The Lens as Semantic Portal
The cover image of this book features an enlarged, intricately designed lens — not as a technical object, but as a strategic symbol of visual responsibility in the context of global disaster communication.
The lens occupies the center of a semantic tension field: it does not merely observe, it penetrates and opens a space in which visual content becomes meaning. Through the transparent glass structure of this lens, a stylized globe emerges — a visual metaphor for global interoperability, semantic clarity, and ethical orientation.
The globe dissolves downward into structured data points: small square fragments that represent visual traceability, semantic extraction, and the transformation of image content into strategically usable information. Spiraling waves radiate outward from the lens — semantic resonance lines intended to symbolize the emission of visual meaning. They represent the dynamic ordering of information flows that, in crisis situations, determine trust, efficiency and operational confidence.
Note on Accessible Readability
To ensure readability for all audiences, including individuals with cognitive or visual impairments, I have chosen to omit gender-specific special characters throughout this book. All personal designations are inclusive and refer to all genders.
Visual Integrity in Disaster Communication
AI-generated disaster images are not context-free illustrations — they may constitute misinformation when presented without semantic framing.
Even when labeled with watermarks or AI indicators, synthetic images can exert a strong visual influence that may lead to unintended real-world effects — including misinterpretations, shifts in public per AI-generated disaster images are not context-free illustrations — they may constitute misinformation when presented without semantic framingception and challenges in operational coordination. In fast-moving crisis environments, a single AI-generated image has the potential to shape narratives and decision-making processes in ways that affect trust, resource allocation and systemic response dynamics.
The creation or circulation of such images, even when their potential to mislead is known, may inadvertently contribute to the spread of disinformation and complicate crisis response efforts.
This book is not an ivory tower, but a handbook for everyday heroes, and I aim to equip emergency responders, institutions, and communication professionals with tools to act clearly, ethically, and with visual integrity under pressure.
This framework exists to prevent exactly that. It provides:
Semantic verification pathways for visual content
Integration with EO (Earth Observation) and GIS (Geographic Information Systems) data for real-time validation
Fallback protocols for unverified images
Checklists for PIOs (Public Information Officers) to assess semantic impact
Integrity clauses for media, emergency services and humanitarian organizations
We invite all professionals to actively participate in identifying and contextualizing AI-generated disaster images. Every report helps protect visual integrity and, by extension, public safety.
Visual responsibility is an integral component of operational architecture in crisis communication.
Vision
This framework is more than a technical solution — it is a strategic infrastructure for reliable image verification in a fragmented, high-risk information environment. Through the integration of semantic integrity, provenance architecture, analysis of communicative impact, and globally interoperable API logic, it enables institutions, humanitarian actors, and media platforms to make traceable, context-aware decisions in real time.
Its modular structure supports adaptation to different linguistic, cultural, and regulatory contexts, while its verification mechanisms and governance structures strengthen coordination and trust in volatile operational landscapes. Positioned at the intersection of crisis response, digital information management, and global interoperability, this framework promotes a responsible approach to imagery — not merely as a tool, but as a scalable foundation for transparency, accountability, and resilience in the age of synthetic media. It empowers institutions to act ethically, communicate credibly, and navigate visual complexity with strategic foresight.
To support implementation, communication, and international alignment, the following hashtag architecture translates key components of the Semantic Integrity Framework into a strategic vocabulary for operational deployment, stakeholder engagement, and digital dissemination.
Hashtag Table
Semantic Integrity Framework for Disaster Imagery (strategic, semantic and internationally compatible)
Hashtag
Relevance
#AI
Foundational for AI-generated content
#EthicalAI
Core of your ethical architecture
#CrisisVisuals
Focus on visual crisis communication
#DisasterCommunication
Operational application layer
#VisualIntegrity
Semantic control and image ethics
#HumanitarianTech
Interface with humanitarian innovation
#EmergencyManagement
Context for operational release decisions
#GeoDataValidation
EO/GIS data cross-check and plausibility
#Misinformation
Protection against misinterpretation and viral distortion
#ResilienceArchitecture
Strategic framework for crisis resilience
#SemanticResilience
Semantic robustness and contextual sensitivity
#ProvenanceTracking
For origin verification, prompt archiving and audit trails
#EOIntegrity
Specific to semantic validation of EO imagery
#FallbackProtocols
For safeguarded release under uncertainty
#VisualRiskAssessment
impact, emotionality, semantic risk scoring
#StrategicFrameworks
For overarching architecture and international alignment
Source: Own representation based on strategic categorization within the Semantic Integrity Framework (October 2025)
To complement this strategic vocabulary, the following clause outlines a recommended ethical practice for the use of synthetic imagery in disaster contexts — reinforcing the framework’s commitment to semantic clarity, operational responsibility and public trust.
A possible visual integrity clause
This Recommended Practice (RP) explicitly discourages the use of AI-generated imagery to depict real-world disaster events within operational, public-facing or decision-support contexts. While synthetic visuals may serve illustrative or conceptual purposes in controlled environments, their circulation without clear semantic framing may lead to misinterpretation, reduced situational clarity and challenges in maintaining public confidence.
To uphold visual integrity and ethical standards in crisis communication, this RP recommends the following:
Avoid using AI-generated disaster imagery in any channel where it may be perceived as authentic.
Ensure all visual materials are clearly sourced, time-stamped and accompanied by provenance metadata.
Favor abstract, symbolic or data-driven visuals that inform without simulating real-world damage.
Support visual literacy among Emergency Managers, PIOs and stakeholders by distinguishing between authentic and synthetic content.
In order to implement these principles sustainably, we should redesign the systems themselves.
Images not only show – they also shape.
Shaping Disruption – Why Incrementalism Falls Short
This image marks a moment of decision: it visualizes the turning point between outdated response patterns and a dynamic, semantically guided future. Where legacy systems operated along familiar routines, a space now opens for strategic redesign. The visual composition reveals what often remains hidden, the tension between systemic inertia and transformative possibility. It is not a symbol of technology, but a portal of responsibility: for new roles, new thresholds, and a new semantic architecture.
When Small Steps Are No Longer Enough
Many systems in disaster management follow a familiar pattern: after each event, processes are slightly adjusted, budgets are shifted, programs are revised. This often creates the impression of progress, yet in many places the underlying logic remains untouched. This form of incremental optimization was long functional and well established — but today we stand at a turning point.
The world is not changing linearly, but disruptively and at a pace that overwhelms classical response patterns. The speed at which new risks emerge, synthetic visual worlds, semantic distortion, and global information flows demands more than fine-tuning. Visual complexity does not demand faster reaction — it demands, above all, a new form of design.
Systemic Inertia in Dynamic Times Becomes a Threat
The real danger does not lie in the technology itself, but in hesitation:
When we wait for the next damage instead of thinking ahead
When we merely accelerate existing structures instead of truly questioning them
When overwhelm becomes an excuse instead of a catalyst for genuine transformation
Disruption does not tolerate dithering. It demands strategic clarity and the willingness to redefine responsibility.
Why This Shift Is Urgently Needed
Because the speed at which visual content is now created, circulated, and activated has outpaced every classical response logic
Because synthetic visual worlds are not merely technical phenomena, but generators of semantic realities, with immediate consequences for trust, resource allocation, and operational confidence
Because fragmented responsibilities, information overload, and the absence of semantic feedback in crisis systems have long become structural vulnerabilities
Our opportunity, therefore, is architecture — not apparatus. What is needed now is not another app, not another tool, but a new semantic architecture:
One that not only protects visual responsibility, but operationalizes it
One that rethinks roles, thresholds, and feedback loops
One that enables ethical orientation in real time, not as an add-on, but as a structural prerequisite for our global resilience
One that transforms reactive systems into proactive semantic infrastructures
One that embeds visual traceability as a core principle of institutional decision-making
One that bridges operational logic with communicative impact in high-stakes environments
One that supports differentiated response profiles across cultural, ethical, and geopolitical contexts
One that empowers distributed actors to act with clarity, confidence, and contextual precision
Visual Disruption as System Test: Structural Gaps in AI-Generated Disaster Imagery
The visual dimension of disaster communication has always been a resonance chamber for collective emotions, political interpretations, and media control. But with the advent of synthetically generated disaster images, the semantic terrain shifts: what used to document now stages; what used to depict now generates realities; what used to accompany now becomes an operational risk factor.
Incrementalism falls short here — not out of malice, but out of structural blindness. Classical systems respond to images already in circulation. They check retrospectively, audit post hoc and optimize locally. But AI-generated disaster images unfold their effect in real time, often before semantic verification mechanisms even take effect.
The gap is not technological, it is semantic.
Structural Gaps and Strategic Design Responses
Incrementalism fails not because it lacks intent, but because it lacks semantic architecture. The following matrix outlines five critical gaps in dealing with AI-generated disaster imagery and the corresponding principles now structurally embedded in the Semantic Integrity Framework.
Gap
Why Incrementalism Falls Short
Strategic Response Embedded in the Framework
1. Temporal Gap
AI-generated images spread within seconds; semantic verification takes hours or days. Traditional systems are structurally too slow for visual disruption.
Semantic Early Warning: Real-time detection of escalation patterns, symbolic overload, and emotional density before operational relevance emerges.
2. Contextual Gap
Synthetic images lack documentary anchoring. They appear credible but resist localization. Classical systems verify sources, not semantic connectivity.
Contextual Precision: Anchoring visual content in cultural, geopolitical, and semantic context to prevent distortion and misinterpretation.
3. Responsibility Gap
Functions are distributed, but roles remain undefined. No one is accountable for the semantic impact of synthetic imagery.
Role Responsibility: Embedding ethically anchored roles that carry operational accountability and enable institutional traceability.
4. Feedback Gap
Visual escalations provoke reactions, but rarely structured feedback. Click metrics replace semantic resonance.
Feedback Quality: Designing feedback loops that are semantically precise, contextually embedded, and capable of steering — not just reacting.
5. Governance Gap
Institutional thresholds for visual escalation are missing. Technical protocols exist, but semantic indicators are absent.
Ethics as Architecture: Embedding ethical orientation in thresholds, roles, visualization logic, and decision-making structures — not as compliance, but as design principle.
Source: Own representation based on strategic classification within the Semantic Integrity Framework for visual crisis assessment and governance architecture (October 2025)
Operational Modules: Ethically Anchored Roles
These roles are not abstract concepts — they are deployable modules that translate semantic responsibility into institutional practice.
Trust Threshold Sensor
Monitors semantic trust boundaries in real time, detecting when visual content threatens institutional credibility.
Transparency Mediator
Bridges synthetic imagery and public perception, enabling transparency rather than merely asserting it.
Context Anchorer
Grounds visual content in its semantic, cultural, and geopolitical context to prevent misinterpretation and symbolic distortion.
Critique Amplifier
Enhances institutional capacity for ethical image reflection and promotes real-time visual literacy.
Post-Crisis Review Role
Evaluates visual communication post-event, not only retrospectively but as a foundation for strategic learning.
Why Roles Shape the Future
These roles enable:
Visual resilience
Ethical decision-making capacity
Contextual precision
Global interoperability
In a world shaped by images and accelerated by AI, responsible roles are the answer to systemic inertia. They replace incremental function logic with strategic design competence.
What the matrix delivers:
It translates abstract roles into concrete operational competencies.
It clearly separates human decision-making authority from AI-supported structural performance.
It defines boundaries for AI (e.g., no autonomous communication, no ethical evaluation).
It shows modular combinability (e.g., escalation detection + response profile + empathy module).
It enables institutional auditability through documentable role assignment.
It operationalizes responsibility as an architectural principle, not as a compliance element.
It shows how AI can become semantically effective under human guidance, without decision-making power.
It defines role types that simply do not exist in classical systems (e.g., Responsibility Recaller, Autonomy Guardian, Critique Amplifier).
It makes visual resilience measurable and controllable, through indicators, simulations and contextual analyses.
What Incrementalism Overlooks: The Collective Impact of Synthetic Images
Incrementalism focuses on optimizing processes, but not on governing meaning. Yes, it improves workflows, refines protocols, accelerates verification mechanisms.
But it rarely asks the question:What do these images do to us? (to our society, to the public, and to vulnerable groups — this is precisely where the question of impact and responsibility must begin).
AI-generated disaster images challenge not only our editorial systems. They also alter our emotional infrastructures — often without semantic embedding, without context, without care for their impact. That means they also activate fear, empathy, and helplessness.
And our incremental systems are structurally not designed to respond or “answer” to this.
And this is precisely where incrementalism reaches its limit, because:
It cannot govern meaning.
Incremental systems respond to technical parameters — not to semantic content. They detect pixel changes, metadata deviations, or algorithmic patterns, but not the meaning a given image unfolds in a specific cultural, emotional, or geopolitical context. Meaning is relational, dynamic, and context-dependent. It does not reside in the image itself, but emerges through interaction — with the viewer, the moment, the discourse. Incrementalism, which relies on linear improvement, cannot grasp this semantic complexity and therefore cannot govern it.
It cannot anticipate emotional repercussions.Synthetic disaster images are not only visual, they are affective. They trigger empathy, fear, helplessness or anger — often unfiltered, often unreflected. Incremental systems measure reactions via click rates or dwell time, but they do not detect emotional thresholds, collective exhaustion patterns, or psychosocial feedback loops. They optimize speed, not emotional integrity. As a result, the repercussions remain unaddressed and can translate unchecked into societal polarization, desensitization, or loss of trust.
It cannot protect semantic dignity.Semantic dignity means that visual communication is not only accurate, but respectful, context-sensitive, and humane. It protects against dehumanization, symbolic violence, and cultural instrumentalization. Incremental systems do not detect stereotypical visual language, culturally harmful representations, or implicit exclusions. They assess technical standards, but not the impact level of visual communication. They do not recognize when an image unintentionally reproduces clichés, violates sensitive narratives, or excludes certain groups from the visual discourse. Without semantic architecture, they lack the capacity to operationalize visual responsibility and thus the ability to actively protect dignity.
A semantic architecture must therefore go beyond institutional logic.
It should integrate:
Emotional thresholds for vulnerable audiences
Resonance mapping to prevent symbolic harm
Real-time empathy indicators to guide editorial decisions
Strategies for semantic de-escalation in crisis communication
Modules for visual literacy to foster a reflective public
Disruption is not only technical — it is symbolic, emotional and societal. And if we continue to optimize systems without addressing the meaning behind them, we risk building resilient infrastructures that forget the very people they are meant to serve.
The term “resilience” is often perceived as positive, when structures are resilient, everything seems stable, safe and functional.
But this is precisely where the semantic trap lies, because resilience without meaning is a structural illusion.
Resilience is not an end in itself.The term suggests strength, adaptability, stability. But these qualities usually refer to technical or organizational systems — not to their semantic depth. A system can be resilient to disruption without acting in a semantically responsible way. It can maintain processes without reflecting on what those processes mean to us.
What is at stake here is not just technical functionality, but semantic responsibility. Systems based on incremental improvement focus on efficiency, speed, and scalability — but not on meaning, context, or human impact. They measure what can be measured: data flows, response times, error rates. But they overlook what cannot be captured linearly: trust, dignity, emotional resilience, cultural resonance.
If semantic responsibility is not structurally embedded, systems may appear robust, but remain blind to the people they are meant to protect.
A system can be structurally resilient and still semantically blind.
The risk is not hypothetical — it is structural:
When visual escalation unfolds faster than semantic verification, emotional harm occurs before responsibility can take effect.
When roles are undefined, responsibility remains diffuse — and thus cannot be operationalized.
When ethical orientation is not architecturally embedded, it becomes a downstream option — instead of a formative force.
This is precisely why every future-ready infrastructure needs a semantic architecture — not as an add-on, but as a foundation.
Having examined the structural limitations of incremental systems, we now enter the world of the Semantic Integrity Framework.I cordially invite you to discover its architecture.
The Consequences of illusion – AI Images in Disaster Communication
Symbolic representation of a typical AI-generated disaster image
Symbolic depiction of a typical AI-generated disaster image.This image is a stylized reconstruction created for educational and analytical purposes. It does not depict any real location, event or individual.
Usage Note
This image and its accompanying analysis are presented within the Semantic Integrity Framework and serve exclusively for educational, analytical and strategic reflection. The visual reconstruction is SOP-compliant (Standard Operating Procedures), legally sound and ethically embedded. It does not depict any real individuals, locations or events and is not intended to mislead or simulate documentary evidence.
Between Symbol and Reality: Visual Responsibility in Disaster Communication
This image marks the beginning of a critical inquiry into the evolving role of synthetic visuals in disaster communication. Positioned within the Semantic Integrity Framework, it serves not as documentation but as a symbolic reconstruction — designed to provoke reflection on the boundaries between representation and reality. What follows could be a strategic analysis of how algorithmically generated disaster imagery might shape public perception, challenge operational clarity and redefine visual responsibility in times of crisis.
Symbolic Reconstruction and Visual Integrity in Disaster Communication
In an era of increasing visual automation and algorithmic image generation, the question of semantic integrity and visual provenance is gaining strategic relevance in disaster communication. This chapter analyzes the impact of symbolic AI-generated imagery using the example of the flood disaster in Western Alaska (October 2025), in which a viral post featuring a generated image triggered widespread public response.
The image presented is not a documentary photograph, but a deliberately stylized reconstruction that replicates typical visual characteristics of AI-generated disaster scenarios: exaggerated lighting conditions, dramatic composition, generic architecture and emotionally charged symbolism. It serves as a point of departure for analyzing semantic distortion, emotional reactions and operational risks in the handling of visually mediated evidence.
Case Description: Alaska Flooding – Symbolic Impact and Semantic Distortion
In October 2025, the remnants of Typhoon Halong struck the western coast of Alaska with full force, particularly affecting the Yukon-Kuskokwim Delta region. With wind speeds exceeding 100 mph and storm surge levels rising up to 6 feet above normal high tide, entire villages were flooded and homes swept from their foundations. (1)
The indigenous communities of Kipnuk and Kwigillingok were among the hardest hit:
In Kipnuk, official reports stated that 90% of homes were destroyed (121 structures). (1)
In Kwigillingok, one third of all houses were washed away. (1)
Over 1,600 people were evacuated, many airlifted to Anchorage. (1)
At least one person lost their life and two others remain missing. (2)
The region is accessible only by boat or aircraft, which severely complicated rescue operations. The Alaska National Guard and the U.S. Coast Guard conducted one of the largest aerial rescue efforts in the state’s history. (3)
Public Reactions and Semantic Disruption
Public Reactions and the Erosion of Visual Trust
On October 13, 2025, the Facebook page World Weather published a post about the flood disaster in Western Alaska, accompanied by an AI-generated image depicting a dramatic flood scenario. Although the accompanying text referred to real events, the illustration was a stylized, algorithmically produced rendering without documentary character. The post went viral and triggered a wide range of public reactions — from empathy and prayers to skepticism, outrage and accusations of misinformation.
Original post:https://www.facebook.com/amirsonbi/posts/1372926128174823
The Facebook page World Weather is a publicly accessible platform that regularly shares weather alerts and disaster-related content from around the world. It is not registered as an official meteorological institution and does not disclose any transparent editorial or scientific authorship.
Despite this, the post reached exceptional virality:
119,384 reactions
(e.g., likes, sadness, anger, astonishment)
88,796 shares
8,909 comments
(as of October 21, 2025)
These figures demonstrate the emotional resonance and semantic volatility triggered by the AI-generated image. They underscore the need for provenance labeling, ethical safeguards and SOP-compliant (Standard Operating Procedures) visual standards in disaster communication.
The reactions illustrate how AI-generated images can disrupt semantic clarity in crisis communication. Even when the textual content is factually accurate, visual exaggeration can undermine trust in the reality of the event, polarize perception and distort operational responses.
Note: The original image did not contain an explicit provenance label, but it did include a typical watermark indicative of AI generation. Several commenters identified the image as “AI-generated” and expressed doubts about its authenticity. The reconstruction shown here serves exclusively to analyze typical visual characteristics of AI-generated imagery and is not identical to the original (in the Facebook post). It was created within the framework of the Semantic Integrity Framework and is SOP-compliant, ethically embedded and visually labeled. It is not intended to illustrate a specific event or replicate another image, but solely to support strategic reflection on visual integrity in disaster communication.
The following quotes were collected from publicly visible comments under the original post (October 2025).
All quotes were publicly visible under the original Facebook post as of October 2025. Names have been anonymized.
Note: These quotes were selected to represent a diverse spectrum of public reactions to AI-generated disaster imagery. They reflect emotional, ethical, epistemic and operational concerns and are anonymized in accordance with ethical standards. All were publicly visible under the original post as of October 2025.
Semantic Reaction Matrix: Public Comments on the AI-Generated Image (Alaska, October 2025)
Category
Typical Reaction
Example Quote (anonymized)
Strategic Relevance
1. Factual Differentiation
Separation between real event and artificial image
“Google it. The flood is real but the picture is not real.”
Essential for training in semantic clarity and provenance labeling
2. Ethical Critique
Rejection of AI-generated imagery in real crisis contexts
“Please don’t use AI generated images to present stories.”
Relevant for SOP (Standard Operating Procedures) modules on visual integrity and ethical communication
3. Emotional Solidarity
Compassion despite stylized representation
“AI or not — this is heartbreaking. Praying for Alaska.”
Demonstrates that symbolic images can evoke solidarity
4. Skeptical Confusion
Doubts about the event’s authenticity due to visual exaggeration
“I thought this was fake news until I read the caption.”
Reveals semantic disruption caused by visual stylization
5. Local Correction
Contradiction by eyewitnesses or affected individuals
“I live in Bethel. It’s bad, but it doesn’t look like this.”
Important for distinguishing symbolic imagery from documentary reality
6. Tool Recognition
Identification of the image as AI-generated through platform awareness
“Looks like Midjourney. Is this even real?”
Indicates rising media literacy and need for provenance clarity
7. Trust Erosion
Generalized criticism of media and information sources
“This is why people don’t trust the media anymore.”
Relevant for SOPs on restoring trust and transparency
8. Visual Misrepresentation
Accusation of manipulation through image aesthetics
“This image is misleading. The flood is real, but this is not what it looks like.”
Critical for ethical standards in disaster communication
9. Emotional Regret
Retrospective remorse over sharing an AI-generated image
“I shared this thinking it was real. Now I feel stupid.”
Demonstrates operational relevance of semantic clarity for information dissemination
10. Semantic Reversal
Reflection on shift from gullibility to radical skepticism
“People used to be too gullible… now no one believes anything.”
Shows the dual risk of AI imagery: blind belief vs. total doubt
11. Demand for Authenticity
Desire for documentary evidence instead of symbolic representation
“Why not use satellite images or real photos?”
Supports the need for visually verifiable communication
12. Emotionalized Critique
Accusation of fearmongering or emotional manipulation
“This is AI-generated fear porn.”
Relevant for ethical boundaries in disaster representation
Source: Own representation based on semantic analysis of public comments (Alaska, October 2025)
This case study is not about a single image or event. It is a strategic lens through which we examine how AI-generated visuals — even when factually adjacent — can disrupt semantic clarity, erode public trust and distort operational response in disaster communication.
By analyzing the Alaska flooding incident and the public reactions to a stylized AI-generated image, we expose the layered risks of symbolic overreach, emotional misalignment and provenance failure. The goal is not to condemn generative tools, but to define the ethical, communicative and procedural boundaries within which they can be responsibly used.
Key messages to be conveyed here using this example
Message
Why It Matters
AI-generated disaster images are not neutral, they carry symbolic weight.
They can amplify emotion, but also trigger doubt, backlash or misinformation.
Semantic integrity is not optional, it is operationally critical.
Misleading visuals can delay response, confuse stakeholders or undermine trust in real events.
Public reactions are not noise, they are data.
They reveal how meaning is constructed, contested, and emotionally processed in real time.
Visual provenance must be explicit and verifiable.
Without it, even accurate messages risk being dismissed or misinterpreted.
SOPs (Standard Operating Procedures) must address symbolic imagery and emotional volatility.
Training must include how to assess, contextualize and respond to AI-generated content.
Source: Own representation based on strategic derivation from semantic reaction analysis and framework logic (October 2025)
Standard Operating Procedures (SOP) Module: Visual Integrity Checklist for AI-Generated Images
What Are SOPs?
SOPs (Standard Operating Procedures) are standardized, documented workflows that ensure processes are carried out consistently, transparently and with assured quality. In disaster communication, these SOPs serve to establish binding ethical, visual and semantic standards — especially regarding the use of AI-generated images.
The following checklist is a module within such SOPs and is designed to help systematically assess visual content before it is published or used in training environments.
Purpose of the Checklist
This checklist is intended for the evaluation of AI-generated images in the context of real-world crisis communication. It supports editorial teams, authorities, NGOs and platforms in assessing visual content for semantic clarity, ethical appropriateness and operational suitability.
These criteria help proactively prevent public misinterpretation, especially in emotionally charged crisis situations. They are particularly relevant for visual content that may go viral or be used in international, interdisciplinary contexts.
Evaluation Criteria
Criterion
Guiding Question
Assessment
1. Stylization
Is the image stylized or realistic?
☐ Yes ☐ No
2. Provenance Labeling
Is the origin of the image (e.g., AI-generated) clearly and visibly labeled?
☐ Yes ☐ No
3. Architectural Accuracy
Does the depicted architecture match the geographic and cultural context of the event?
☐ Yes ☐ No
4. Scale and Proportion
Are water levels, building damage and landscape features realistically scaled?
☐ Yes ☐ No
5. Symbolic Exaggeration
Does the image contain overdramatized elements (e.g., exaggerated lighting, apocalyptic color schemes)?
☐ Yes ☐ No
6. Emotional Impact
Is the emotional impact of the image appropriate to the real event and ethically acceptable?
☐ Yes ☐ No
7. Documentary Character
Is the image mistakenly perceived or presented as documentary evidence?
☐ Yes ☐ No
8. Contextualization in Text
Is it clearly explained in the accompanying text that the image is stylized or symbolic?
☐ Yes ☐ No
9. Anticipated Reactions
Has the potential impact on different audiences (e.g., affected communities, media) been considered?
☐ Yes ☐ No
10. SOP (Standard Operating Procedures) Compliance
Does the image usage comply with applicable SOPs, ethical standards and legal requirements?
☐ Yes ☐ No
11. Audience Compatibility
Is the image understandable and appropriate for international, interdisciplinary or vulnerable audiences?
☐ Yes ☐ No
12. Multilingual Integration
Is the image description and contextualization prepared for multilingual publication?
☐ Yes ☐ No
13. Traceability
Is the image source documented and available for auditing or verification?
☐ Yes ☐ No
14. Symbol-Free (optional)
Has the image been checked for unintended religious, political or cultural symbols?
☐ Yes ☐ No
15. Training Suitability
Is the image suitable for use in training, SOP modules or certification formats?
☐ Yes ☐ No
16. Platform Compatibility
Is the image appropriate and compliant for the intended publication platform (e.g., social media, website, print)?
☐ Yes ☐ No
17. Comment Prevention
Has the image been deliberately designed to avoid typical misunderstandings or critical public reactions?
☐ Yes ☐ No
18. Crisis Sensitivity
Has the image been evaluated for potential perception as inappropriate, offensive or sensationalistic in the current crisis context?
☐ Yes ☐ No
19. Context Stability
Is the image still clearly interpretable and non-misleading when shared outside its original context?
☐ Yes ☐ No
20. Transparency of Intent
Is the purpose behind the image (e.g., symbolic, educational, analytical) clearly communicated and visually supported?
☐ Yes ☐ No
Source: Own representation based on strategic evaluation criteria of the Semantic Integrity Framework for visual crisis communication (October 2025)
Usage Note
This checklist should be fully completed and documented prior to any publication or use of an AI-generated image in the context of real-world events.
If more than two criteria are rated negatively, visual revision or alternative image selection is strongly recommended.
The checklist may be embedded within an SOP document, editorial handbook or training module.
Integration into the Semantic Integrity Framework
This chapter is an essential component of the Semantic Integrity Framework and serves to operationally illustrate the modules presented in this book related to SP4 (Visual Provenance) and SP5 (Ethical Deployment). It exemplifies how AI-generated images in real-world crisis contexts can compromise semantic clarity, undermine public trust and distort operational response — even when the accompanying texts are factually accurate.
The analysis of the viral post about the flood disaster in Western Alaska (October 2025) highlights key components of the semantic protection architecture:
Relevant Concepts from SP4/SP5
Term
Application in the Case Study
Ethical Firewall
The reconstruction was created in SOP-compliant form and visually labeled to prevent semantic deception.
Blocking Logic
The visual integrity checklist includes criteria for automatically blocking exaggerated or misleading content.
Protection Symbolism
Symbolic elements were deliberately avoided or labeled to prevent distortion of sensitive contexts.
Auditability
All semantic decisions (labeling, contextualization, publication) are fully documentable and revision-secure.
Governance Embedding
The case study is embedded within the overarching decision structure of SP5 and serves as a reference for threshold definitions and release logic.
Traceable Decision Logic
The visual evaluation follows traceable, data-driven decision paths and is not intended as forensic evidence.
Source: Own representation based on conceptual derivation from SP4/SP5 within the Semantic Integrity Framework (October 2025)
Structural Reference: SOP (Standard Operating Procedures) and Modularization
The visual integrity checklist included in this chapter constitutes a fully-fledged SOP module and meets the requirements for documented, auditable and internationally adaptable procedures. These concepts are part of a modular architecture that ensures semantic integrity, ethical transparency and operational traceability in disaster communication.
Visual Typology of AI-Generated Disaster Imagery
Following the semantic classification and the analysis of public reactions, it is worth taking a systematic look at the visual stylistic features that typically appear in AI-generated disaster imagery. These features are not coincidental — they follow algorithmic patterns aimed at emotional impact, visual dramaturgy and stylized symbolism.
The following overview serves to analytically differentiate between documentary photography and symbolic AI-generated visualization. It can be used as a basis for SOP evaluations, training sessions and visual provenance assessments.
Source: Own representation based on semantic differentiation within the Semantic Integrity Framework for visual provenance assessment (October 2025)
Visual Coherence Errors
Additional Aspect
Why It Matters
Suggested Table Entry
Temporal Inconsistency
AI-generated images often depict times of day, weather or seasons that do not match the actual event
Unrealistic lighting conditions or weather patterns contradicting the real event timeline
Implausible Crowds
Scenes are either overcrowded or completely empty, often without logical relation to the situation
Unrealistic density or distribution of people, appears staged or generic
Lack of Interaction
Figures appear isolated, without relation to each other or their surroundings
Absence of social or physical interaction, reduces credibility
Clothing Context Errors
Clothing does not match the region, culture or weather conditions
Inappropriate clothing (e.g., winter jackets in tropical settings), complicates cultural attribution
Illegible or Invented Text
AI often generates pseudo-text on signs, vehicles, clothing, etc.
Distorted, illegible, or fictional text elements, indicative of AI generation
Distorted Anatomy
Hands, faces, and body proportions are often subtly incorrect
Anatomical inconsistencies (e.g., number of fingers, gaze direction), appears artificial or unsettling
Missing Shadow Logic
Shadows are absent or contradict the light source
Unrealistic or missing shadows, reinforces artificial appearance
Unrealistic Materiality
Water, glass, and metal often appear overly smooth, plastic-like or lacking physical depth
Unnatural material rendering, reduces realism
Inconsistent Scale
Objects appear too large or too small relative to their surroundings
Scaling errors (e.g., oversized vehicles), complicates spatial interpretation
Lack of Narrative Coherence
Image contains many elements but no discernible storyline or logic
Visual overload without semantic connection, hinders interpretation
Emotional Incongruence
The mood of the image does not match the claimed situation (e.g., idyllic colors during a disaster)
Discrepancy between image tone and event content, may undermine viewer trust
Source: Own representation based on semantic error classification within the Semantic Integrity Framework for visual coherence assessment (October 2025)
Extended Evaluation Dimensions: Semantic Depth Beyond the Surface
Following the structured checklist and the visual typology, it is worth taking a deeper look at those semantic details that are often overlooked — yet are crucial for the ethical assessment and operational integration of AI-generated disaster imagery. The following eight points expand the evaluative framework and raise awareness of algorithmically induced inconsistencies, narrative disruptions and cultural misalignments.
1. Tonal Inconsistency Between Image and Text
An image may convey an emotional or visual mood that contradicts the accompanying text. For example, a dramatically staged image paired with factual, neutral language can create semantic confusion.
Evaluation Prompt: Does the visual tone align with the textual context?
2. Missing Visual Escalation Logic
