-Kushagra Tiwari*
Abstract
This paper highlights how AI training data opt-out mechanisms reinforce global inequalities by consolidating technological power in Western countries. Although presented as a way to protect intellectual property, these mechanisms limit access to diverse training data, allowing Western companies to dominate AI development and embed cultural biases into AI systems. These biases marginalize non-Western perspectives and perpetuate “digital colonialism,” where AI systems reflect and enforce Western ideologies.
To counteract this, the paper proposes three key solutions: setting mandatory diversity standards for AI training data, fostering collaborative AI models that preserve local data sovereignty, and creating new intellectual property frameworks that balance local and global interests. These measures aim to create a more equitable system that ensures that AI development serves diverse global communities.
I. Introduction
Recently, a copyright infringement suit was filed by ANI Media against OpenAI in the Delhi High Court - the first such case against OpenAI outside the United States. In response, OpenAI informed the court they had already blocklisted ANI's domains from future training data - a move that aligns with standard industry practice. AI developers typically train their models on publicly available internet data, while providing opt-out mechanisms for those who wish to exclude their content - a policy first prominently highlighted in OpenAI’s response to the New York Times' copyright infringement suit.
However, the significance of AI companies' opt-out policies extends far beyond the immediate dispute over copyright. This debate is fundamentally about who shapes the future of AI systems, that will power our digital world. AI systems fundamentally learn to understand and interact with the world through their training data. When major segments of the developing world's digital content get excluded – whether through active opt-outs or passive inability to participate effectively – we risk creating AI systems that amplify existing global inequities. This piece will examine how the technical architecture of opt-out mechanisms interacts with existing power structures and market dynamics.
Part II of the article examines how opt-out mechanisms shape AI development's technical landscape and systemic inequities. Part III analyzes their impact on market dynamics and barriers to entry, while Part IV delves into how biased training data creates lasting representational imbalances. Part V explores comprehensive solutions beyond individual opt-outs, and Part VI concludes by examining the broader implications for technological equity and digital colonialism.
Please note that by arguing against the opt-out mechanism, I do not imply that publishers do not have a copyright infringement claim against AI companies.
II. The Ineffectiveness of Opt-Out Mechanisms
OpenAI's response to ANI's lawsuit reveals several critical dynamics that shape the broader impact of opt-out mechanisms in AI development. The first key insight comes from understanding the technical futility of domain-based blocking as a protective measure.
Like most AI companies, OpenAI collects training data through web crawlers – automated programs that systematically browse and index web content. These crawlers follow links across the internet, downloading text, images, and other content from millions of websites. When a website’s domain is blocklisted, the crawler is programmed to skip that specific website during its collection process.
However, due to the architecture of the modern internet, content rarely stays confined to its original domain. News articles spread across multiple platforms, get archived by various services, and appear in countless derivative works. Consider ANI's news content: a single story might simultaneously exist on their website, in news aggregators, across social media platforms, web archives, and countless other locations. This multiplication of content makes domain blocking more performative than protective.
What makes this particularly problematic is the uneven impact of opt-out requests. Large AI companies can bypass such restrictions through alternative channels like partnerships or licensing, while smaller players, especially in developing nations, often lack similar resources. The next section delves deeper into this disparity and its broader implications for the AI ecosystem.
III. Opt-Out Mechanisms Cement AI Market Domination
The structural disadvantages created by opt-out mechanisms manifest through multiple channels, compounding existing market dynamics. Early AI developers, predominantly Western companies, leveraged the "wild west" period of AI development, during which unrestricted datasets were readily available. This access allowed them to develop proprietary algorithms, cultivate dense pools of talent, and collect extensive user interaction data. These first-mover advantages have created architectural and operational moats that generate compounding returns, ensuring that even in an environment with reduced access to training data, these companies maintain a significant edge over newer competitors.
This architectural superiority drives a self-reinforcing cycle that is particularly challenging for new entrants to overcome:
Superior models extract greater value from limited training data.
Enhanced performance attracts more users and developers.
Larger user bases generate richer interaction data.
Sophisticated interaction data enables further model improvements.
Improved models continue to attract more users, perpetuating the cycle.
The establishment of opt-out mechanisms as a de facto standard adds another layer of complexity to modern AI development. Participating in such regimes necessitates substantial infrastructure development across multiple domains. Organizations must implement robust content filtering systems to identify and respect opted-out sources, while simultaneously maintaining comprehensive compliance monitoring mechanisms that can operate across diverse jurisdictions. Furthermore, technical systems must be developed for verifying content sources and managing data provenance throughout the development pipeline. To address potential data gaps, organizations also need to establish alternative data sourcing infrastructure to effectively replace opted-out data sources. These requirements collectively represent a significant operational and technical challenge for AI developers.
As Akshat Agarwal has argued, OpenAI's opt-out policy, while framed as an ethical gesture, effectively cements its dominance by imposing disproportionate burdens on emerging competitors. Newer AI companies face the dual challenge of building comparable systems with restricted access to training data while contending with market standards set by established players.
OpenAI’s approach has not only widened the gap between market leaders and new entrants but has also reshaped the trajectory of AI development itself. By normalizing opt-out mechanisms and forging partnerships for high-quality content, OpenAI has engineered a self-reinforcing system of technical, regulatory, and market advantages. Without targeted regulatory intervention to dismantle these reinforcing feedback loops, the future of AI risks being dominated by a few early movers, stifling both competition and innovation.
For AI initiatives in the developing world, these barriers are particularly burdensome. Established players can absorb compliance costs through existing infrastructure and distribute them across vast user bases, but smaller or resource-constrained initiatives bear a disproportionately higher burden. This creates what is effectively a tax on innovation, disproportionately affecting those least equipped to bear its weight. The result is regulatory capture through technical standards - the rules appear neutral but systematically advantage established players.
IV. The Hidden Costs of Biased Training
The consequences of opt-out mechanisms extend far beyond market dynamics to the fundamental architecture of AI systems, which can be described as a form of cognitive colonialism. Evidence of systematic bias is already emerging in current AI systems, manifesting through both direct performance disparities and more subtle forms of encoded cultural assumptions.
Research indicates that current large language models exhibit significant cultural bias and perform measurably worse when tasked with understanding non-Western contexts. For example, in Traditional Chinese Medicine examinations, Western-developed language models achieved only 35.9% accuracy compared to 78.4% accuracy from Chinese-developed models. Similarly, another study found that AI models portrayed Indian cultural elements from an outsider’s perspective, with traditional celebrations being depicted as more colorful than they actually are, and certain Indian subcultures receiving disproportionate representation over others.
This representational bias operates through multiple reinforcing mechanisms:
The training data predominantly consists of Western contexts, limiting understanding of non-Western perspectives.
Superior performance on Western tasks leads to higher adoption in Western markets.
Increased Western adoption generates more interaction data centered on Western contexts.
System architectures become optimized for Western use cases due to skewed data and priorities.
Deployed systems reshape local contexts to align with their operational assumptions.
The opt-out mechanism exacerbates these issues by creating a systematic skew in training data that compounds over time. As publishers from developing regions increasingly opt out—whether intentionally or due to logistical barriers—the training data grows progressively more Western-centric.
Another study found that even monolingual Arabic-specific language models, trained exclusively on Arabic data, exhibited Western bias. This bias stemmed from two main reasons: first, much of the pre-training data, although in Arabic, frequently discussed Western topics; and second, a significant portion of the data consisted of Arabic translations of content originally written in other languages, such as English. Interestingly, local news and Arabic Twitter data showed the least Western bias, highlighting the importance of local news organizations in providing culturally authentic perspectives. Additionally, the study found that multilingual models exhibited a stronger Western bias than unilingual ones, due to their reliance on diverse but predominantly Western-influenced datasets. In light of the study’s findings, we can better understand the critical role that ANI, as a local news organization, plays in shaping culturally relevant narratives, and the importance of its decision to opt out.
Addressing these biases through post-training interventions alone is challenging. If regional news organizations, such as ANI, continue to opt out of contributing their data for AI training, leading AI models risk becoming increasingly biased toward Western contexts. This would result in AI systems that depict non-Western cultures from an outsider’s perspective, further marginalizing diverse viewpoints.
As these systems mediate our interactions with digital information and shape emerging technologies, their embedded biases reinforce a form of cognitive colonialism that systematically disadvantages non-Western perspectives and needs. This colonialism manifests when Western-biased AI systems become the default technological infrastructure, forcing non-Western users to adapt to Western ways of thinking and interacting rather than the technology adapting to diverse cultural contexts. For example, when AI systems trained primarily on Western data make recommendations about healthcare, education, or social services, they inherently promote Western solutions regardless of local cultural practices or needs.
V. Beyond Individual Opt-Outs: Systemic Solutions
The challenge of creating more equitable AI development requires moving beyond the false promise of individual opt-out rights to develop systematic solutions that address underlying power asymmetries. This requires acknowledging a fundamental tension: the need to protect legitimate creator rights while ensuring AI systems develop with sufficiently diverse training data to serve global needs. The current opt-out framework attempts to resolve this tension through individual choice mechanisms, but as the above analysis has shown, this approach systematically favours established players while creating compound disadvantages for developing-world participants.
To address this systematic bias, we must implement a multi-layered solution that operates simultaneously across various dimensions. At the technical level, this begins with mandatory inclusion frameworks that establish clear metrics for dataset diversity. These frameworks would need to go beyond simple geographic quotas to ensure meaningful representation across cultural, linguistic, and socioeconomic dimensions. For instance, a technical diversity standard might require not just raw numerical representation of data from developing nations, but also balanced representation across different socioeconomic strata within those nations, measured through established demographic indicators.
However, rather than treating developing nations purely as data sources, a comprehensive solution must build local capacity for AI development and deployment. This requires moving beyond traditional technology transfer programs, which often create dependency relationships, towards collaborative development models that preserve data sovereignty while enabling knowledge sharing. For example, federated learning architectures could allow local institutions to participate in model training while maintaining control over their data, combined with open-source model architectures that enable local adaptation and enhancement.
The above cannot be meaningfully achieved via the traditional intellectual property frameworks. They are based on individual ownership rights, and are inadequate for managing collective digital resources that have both local and global implications. We need new frameworks of intellectual property that must balance local autonomy with global coordination, perhaps through tiered systems where local communities maintain sovereignty over culturally specific data while participating in broader frameworks for sharing more general knowledge.
To coordinate these various interventions effectively, we need to recognize that different approaches can work together synergistically while acknowledging inherent tensions and tradeoffs. This might manifest as a tiered system where certain baseline requirements (like minimum diversity quotas and compensation rates) are mandatory, while other elements (like specific governance structures or infrastructure development approaches) can be adapted to local contexts and needs.
VI. Conclusion: Choosing the Future of AI Governance
The solutions proposed above might appear unrealistically ambitious, particularly given the current trajectory of AI development dominated by a small number of well-resourced actors operating primarily within developed economies. It can be argued that attempting to implement such comprehensive reforms in the face of intense market pressures and established power structures represents a form of techno-social utopianism. The coordination challenges alone - aligning incentives across multiple stakeholders with divergent interests while maintaining technological competitiveness - are almost prohibitively complex.
However, acknowledging these difficulties must be weighed against the prospect that the development of artificial intelligence could fundamentally alter the trajectory of human civilization. If we accept this premise, then ensuring that AI development incorporates diverse global perspectives and serves the interests of humanity as a whole becomes not merely desirable but existentially important.
The stakes here extend beyond immediate questions of fairness or market efficiency. We are, in effect, encoding fundamental patterns of power, knowledge, and value distribution that will likely persist and amplify as AI systems become increasingly capable and influential. Early decisions and structures will become progressively more difficult to alter as systems evolve and entrench. Thus, the stakeholders must act sooner than later.
*Kushagra is a fourth-year student pursuing his B.A., LL.B. (Hons.) from the West Bengal University of Juridical Sciences (NUJS), Kolkata.
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