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The Expanding Governance Challenge of Artificial Intelligence
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Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk. 

Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.

The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process. 

This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
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The Lifecycle of an AI System
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AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.

The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.

Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.

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Where AI Risks Emerge
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AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks:  bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.

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AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
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 Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence

Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms. 
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AI System Lifecycle: Key Risks at Each Stage
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Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
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Policy Tools for Mitigating AI Risks
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Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment. 

For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.

Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development. 
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Governance Overlay: Policy Interventions Across the AI Lifecycle
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Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
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The Policy Gap in AI Governance
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The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.

An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment. 

Analysis
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1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.

2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment. 

3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems. 

4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
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Recommendations
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1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.

2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.

3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.

4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
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Conclusion - The Need for Global AI Governance
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Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.

Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
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References
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• OECD AI lifecycle
• OECD AI system lifecycle description
• OECD AI governance lifecycle framework
• EU AI Act overview
• EU AI Act risk categories
• UNESCO Recommendation on the Ethics of AI
• AI governance lifecycle analysis
• OECD AI policy tools database

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Artificial Intelligence (AI) provides a varied range of services and continues to catch intrigue and experimentation. It has altered how we create and consume content. Specific prompts can now be used to create desired images enhancing experiences of storytelling and even education. However, as this content can influence public perception, its potential to cause misinformation must be noted as well. The realistic nature of the images can make it hard to discern as artificially generated by the untrained eye. As AI operates by analysing the data it was trained on previously to deliver, the lack of contextual knowledge and human biases (while framing prompts) also come into play. The stakes are higher whilst dabbling with subjects such as history, as there is a fine line between the creation of content with the intent of mere entertainment and the spread of misinformation owing to biases and lack of veracity left unchecked. AI-generated images enhance storytelling but can also spread misinformation, especially in historical contexts. For instance, an AI-generated image of London during the Black Death might include inaccurate details, misleading viewers about the past.

The Rise of AI-Generated Historical Images as Entertainment

Recently, generated images and videos of various historical instances along with the point of view of the people present have been floating all over the internet. Some of them include the streets of London during the Black Death in the 1300s in England, the eruption of Mount Vesuvius at Pompeii etc. Hogne and Dan, two creators who operate accounts named POV Lab and Time Traveller POV on TikTok state that they create such videos as they feel that seeing the past through a first-person perspective is an interesting way to bring history back to life while highlighting the cool parts, helping the audience learn something new. Mostly sensationalised for visual impact and storytelling, such content has been called out by historians for inconsistencies with respect to details particular of the time. Presently, artists admit to their creations being inaccurate, reasoning them to be more of an artistic interpretation than fact-checked documentaries. 

It is important to note that AI models may inaccurately depict objects (issues with lateral inversion), people(anatomical implausibilities), or scenes due to "present-ist" bias. As noted by Lauren Tilton, an associate professor of digital humanities at the University of Richmond, many AI models primarily rely on data from the last 15 years, making them prone to modern-day distortions especially when analysing and creating historical content. The idea is to spark interest rather than replace genuine historical facts while it is assumed that engagement with these images and videos is partly a product of the fascination with upcoming AI tools. Apart from this, there are also chatbots like Hello History and Charater.ai which enable simulations of interacting with historical figures that have piqued curiosity.

Although it makes for an interesting perspective, one cannot ignore that our inherent biases play a role in how we perceive the information presented. Dangerous consequences include feeding into conspiracy theories and the erasure of facts as information is geared particularly toward garnering attention and providing entertainment. Furthermore, exposure of such content to an impressionable audience with a lesser attention span increases the gravity of the matter. In such cases, information regarding the sources used for creation becomes an important factor.

Acknowledging the risks posed by AI-generated images and their susceptibility to create misinformation, the Government of Spain has taken a step in regulating the AI content created. It has passed a bill (for regulating AI-Generated content) that mandates the labelling of AI-generated images and failure to do so would warrant massive fines (up to $38 million or 7% of turnover on companies). The idea is to ensure that content creators label their content which would help to spot images that are artificially created from those that are not.

The Way Forward: Navigating AI and Misinformation

While AI-generated images make for exciting possibilities for storytelling and enabling intrigue, their potential to spread misinformation should not be overlooked. To address these challenges, certain measures should be encouraged.

1. Media Literacy and Awareness – In this day and age critical thinking and media literacy among consumers of content is imperative. Awareness, understanding, and access to tools that aid in detecting AI-generated content can prove to be helpful.
2. AI Transparency and Labeling – Implementing regulations similar to Spain’s bill on labelling content could be a guiding crutch for people who have yet to learn to tell apart AI-generated content from others.
3. Ethical AI Development – AI developers must prioritize ethical considerations in training using diverse and historically accurate datasets and sources which would minimise biases.

As AI continues to evolve, balancing innovation with responsibility is essential. By taking proactive measures in the early stages, we can harness AI's potential while safeguarding the integrity and trust of the sources while generating images.

References:

• https://www.npr.org/2023/06/07/1180768459/how-to-identify-ai-generated-deepfake-images
• https://www.nbcnews.com/tech/tech-news/ai-image-misinformation-surged-google-research-finds-rcna154333
• https://www.bbc.com/news/articles/cy87076pdw3o
• https://newskarnataka.com/technology/government-releases-guide-to-help-citizens-identify-ai-generated-images/21052024/
• https://www.technologyreview.com/2023/04/11/1071104/ai-helping-historians-analyze-past/
• https://www.psypost.org/ai-models-struggle-with-expert-level-global-history-knowledge/
• https://www.youtube.com/watch?v=M65IYIWlqes&t=2597s
• https://www.vice.com/en/article/people-are-creating-records-of-fake-historical-events-using-ai/?utm_source=chatgpt.com
• https://www.reuters.com/technology/artificial-intelligence/spain-impose-massive-fines-not-labelling-ai-generated-content-2025-03-11/?utm_source=chatgpt.com
• https://www.theguardian.com/film/2024/sep/13/documentary-ai-guidelines?utm_source=chatgpt.com

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When your digital identity becomes raw material for someone else’s imagination, who really owns your face?

On July 7, 2026, Meta launched Muse Image, the first standalone image-generation model developed by Meta Superintelligence Labs under Alexandr Wang. Positioned as a competitor to OpenAI’s GPT Images 2.0 and Google’s Nano Banana 2, the launch highlights Meta’s growing ambitions in the generative AI race. Yet beneath the technical advancements lies a far more consequential question than what AI can create, but about who gets to decide what it creates with?

That ambition of generative AI comes with a cost, and one of Muse Image's most controversial design choices is its integration with Instagram, which allows public profiles to become creative references for AI-generated images. Users can tag public Instagram accounts in prompts and generate AI images inspired by that person’s publicly available content. Meta presents this as a new form of personalization and creativity. However, critics argue that the feature transforms years of personal photographs into a massive library of AI-ready human identities, where non-action automatically becomes permission. The concern is not just that AI can generate realistic images. The concern is that consent itself is being manipulated in a way.

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 The opt-out system most users never asked for

Muse Image operates through Meta AI’s integration with Instagram. A user can reference a public Instagram profile in a prompt, allowing the system to generate images influenced by that person’s existing content and likeness. Under the default arrangement, the person whose profile is being referenced may not necessarily approve the generation beforehand.

Meta does provide users with controls to restrict this functionality. The setting exists under Instagram’s “Sharing and reuse” options, where users can disable permissions related to the use of their posts and reels with Meta’s AI features. But the larger issue is the direction in which responsibility flows. Instead of requiring active permission before someone’s likeness becomes available for AI-generated content, the burden is placed on individuals to discover the feature, understand the implications, locate the setting, and disable it. In a digital environment where users already navigate endless privacy menus, cookie banners, and terms of service agreements, expecting meaningful awareness from billions of people becomes unrealistic. Consent that depends on finding the exit door is very different from consent that begins with a choice.

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A guide for opting-out

One way of opting out is to have a private account. As verified, Meta does not encroach on private accounts as of now for image-generation.

But if you have a public account, here are the steps that can be followed to secure yourself from Meta’s Muse automatic consent:

On your profile, tap the menu bar which is on the top right (≡), you will enter into ‘setting and activity’, after that you will see an option of ‘sharing and refuse’ and then disable the options under it, namely- Allow people to reuse your content on Instagram and with AI features at Meta. Notably, the changes made will be only for your future content not the already existing content on your public profile.

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 Why “public” does not mean “available for anything”

The debate around Muse Image highlights a deeper misunderstanding at the center of modern digital platforms: the difference between visibility and reuse. When someone makes an Instagram account public, they are generally making a decision about the audience. They are allowing others to view their photos, discover their profile, or engage with their content. That decision was never traditionally understood as permission for their face, appearance, personal moments, or identity markers to become reusable components in AI-generated media. A photograph posted online has context. It represents a specific moment, purpose, relationship, or expression. Generative AI changes that equation because it separates identity from context. A person’s likeness can be extracted from its original context and placed into entirely new situations created by someone else. Muse Image does not just expand who can see your content. It expands what can be done with it. That distinction is what scales the problem for every public user on the app. The privacy implications become significantly larger because of Instagram’s global reach. With billions of users worldwide, even a small percentage of affected public accounts represents an enormous number of people. Many public profiles do not belong to celebrities, influencers, or creators who intentionally operate as public brands. They belong to students, professionals, small businesses, artists, photographers, and everyday users who simply chose visibility within a social network.

For years, platforms encouraged people to share more, build audiences, and maintain a public digital presence. Now, the meaning of that public presence is changing after the fact. The question becomes: should a decision made years ago to share photos socially automatically extend into permission for generative AI systems built years later?

There is also a catch while opting out. Changing these settings only protects your content from being used in future AI generations,  it does not undo anything that has already happened. Any AI images previously created using your public profile will remain unaffected. Essentially, the opt-out works as a shield for what comes next, not a reset button for what has already been created.

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 A familiar pattern in the AI era

Muse Image reflects a broader pattern emerging across the technology industry. New AI capabilities are introduced at massive scale, participation becomes the default, and individual control arrives afterward through settings that many users may never find. The same debates that once surrounded targeted advertising, data collection, and algorithmic profiling are now moving into a far more personal territory, the human identity itself. Faces are not ordinary data points. Unlike a username, password, or preference setting, a person cannot simply replace their appearance once it has been widely replicated.

Technical solutions such as AI watermarking and content labels may help identify generated material, but they address authenticity after creation. They do not answer the question of whether the generation should have happened in the first place.

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 Innovation cannot replace consent

The technological progress behind Muse Image is significant. Better image generation, improved text rendering, and more personalized creative tools represent genuine advances in artificial intelligence. But capability alone cannot determine acceptability. The future of AI will not only depend on how realistic images become, how powerful models become, or how quickly companies can deploy new features. It will also depend on whether people feel they have meaningful control over their own digital identities. Muse Image is therefore more than another AI product launch. It represents a defining question for the next phase of the internet: Will our online presence remain something we control, or will it become something others can generate?

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