As AI advances, we are extending the Tower of Babel—layer upon layer of knowledge, computation, and decision-making. But is this ascent driven by collective selflessness, a pursuit to elevate humanity? Or is it an extension of ego, a race for dominance by those who control AI’s foundations? Throughout history, grand structures have been built in the name of progress, yet many have served as monuments to power rather than beacons of truth. What, then, is the true purpose of this ever-rising tower of intelligence? History has shown that ego-driven structures—built on conquest, greed, or the illusion of control—are doomed to crumble under their own weight. In contrast, those grounded in selflessness and collective benefit ensure legacy and sustainability. If AI is to stand the test of time, it must be built with transparency and ethical integrity, not as a tool of unchecked ambition.
The exponential growth of AI feels like we are adding new layers to the Tower of Babel—building higher, striving for mastery over language, reasoning, and decision-making. But as this tower rises, are we embedding honesty and ethics into its foundation, or are we merely optimizing for corporate and geopolitical interests?
AI, Ethics, and the Specter of Corporate Influence
Ethical AI is often discussed in boardrooms and research labs, but when profit and power are the true motivators, how much of that discussion translates into practice? Tech companies control AI’s training pipelines, deciding which data it learns from, what objectives it optimizes for, and how it presents information to users. The incentives are clear: reinforce engagement, maximize profit, and subtly steer narratives that benefit corporate roadmaps.
But what happens when those roadmaps align with agendas that are far from objective truth? AI models can be—and often are—trained to favor certain perspectives, suppress dissenting views, and prioritize information that benefits those in control. A recommendation algorithm might be designed to maximize ad revenue, but in doing so, it can reinforce specific worldviews, creating echo chambers instead of open discourse. Can an AI built within these constraints ever be truly objective?
The Geopolitical Layer: AI as a Weapon of Influence
AI does not develop in a vacuum; it emerges within the power structures of nations. Countries now recognize AI as both an economic tool and a strategic weapon. The data AI models are trained on is subject to national priorities, political pressures, and cultural biases.
For example, Western AI models are shaped by liberal democratic ideals, corporate market dynamics, and U.S. geopolitical interests. Meanwhile, China’s AI systems are developed within a framework of state control and centralized authority, prioritizing social harmony and national stability. Other nations, caught between these two digital superpowers, must decide which ecosystem to align with—or whether to develop independent AI models free from external influence.
In this environment, can we even speak of AI morality in an absolute sense? Ethics, after all, are culturally and politically contingent. A model trained in one part of the world may define fairness differently than one trained elsewhere. Does this mean we should aim for a global AI morality, or should we accept that AI ethics will always be fragmented along geopolitical lines?
As the old adage goes, “History is written by the victors.” If AI is trained on datasets curated by dominant powers, does it not become yet another instrument of that dominance? How much of what we consider truth is merely the consequence of who controls the training data?
Towards Greater Transparency: Can AI Disclose Its Biases?
Since true objectivity might be impossible, should AI at least be honest about its origins?
In academia, research papers list their sources, disclose funding, and highlight potential conflicts of interest. Why shouldn’t AI do the same? Imagine a model that, instead of presenting its output as neutral fact, disclosed:
- The datasets it was trained on
- The funding sources behind its development
- The explicit and implicit biases it carries
Such an AI wouldn’t claim to be an oracle of truth but a curated lens—one that users can scrutinize rather than blindly trust. Transparency could counteract the deceptive aura of AI neutrality and help users engage with it more critically.
Making AI More Honest While Aligning with Monetary Incentives
Achieving AI honesty does not have to be at odds with profitability. Several approaches can ensure transparency while feeding into financial incentives:
- Monetizing Transparency – AI companies could offer premium services that allow users to inspect the provenance of AI-generated content. For example, businesses might pay for models that provide explicit sourcing and bias disclosures, similar to high-quality research databases like LexisNexis or Bloomberg Terminal.
- User-Controlled AI Tuning – Platforms could allow users to customize their AI’s biases and training data. A marketplace could emerge where users select AI models with specific ethical orientations, creating demand-driven incentives for companies to disclose and refine biases. Imagine a news aggregation AI that allows users to toggle between “Western liberal perspective” and “Global South perspective” to see how narratives shift.
- Regulatory Compliance as a Competitive Edge – Companies that proactively disclose AI biases and follow ethical guidelines could gain an edge in markets that demand responsible AI, particularly in finance, healthcare, and law. For instance, an AI-powered financial advisory tool that meets strict bias-disclosure regulations might be preferred over competitors in highly regulated industries.
- Reputation as Currency – Trust is a marketable asset. AI firms that position themselves as leaders in transparency and fairness could attract more users, similar to how some brands capitalize on sustainability. OpenAI and Anthropic, for example, have already used ethical AI narratives as differentiators in their marketing strategies.
- Auditable AI Models – Establishing third-party auditing and certification systems for AI outputs can add another layer of trust. Just as organic foods are certified by independent organizations, AI models could be validated for fairness and objectivity, with companies paying for these certifications to gain consumer trust. A model trained for medical diagnosis, for example, could be independently verified for bias-free decision-making, making it more appealing to hospitals and insurance providers.
By embedding honesty into AI through these financially viable strategies, we can align ethical development with economic incentives, ensuring that transparency becomes not just a moral imperative, but a competitive advantage.
The Role of Global Open-Source Certification Efforts
While AI development is often dominated by corporate and national interests, there have been efforts to create global, multi-nation open-source frameworks to certify technologies that aspire to higher standards of transparency and fairness. Initiatives like the Partnership on AI, Montreal AI Ethics Institute, and OECD AI Principles attempt to set ethical guidelines across borders. Projects like BigScience and LAION have also pushed for openly sourced datasets and model transparency, reducing the monopoly of select institutions over AI training data.
A promising path forward would be the establishment of an independent, international AI certification body, akin to how ISO (International Organization for Standardization) governs standards in other industries. Such a body could audit AI systems for bias disclosure, ethical compliance, and provenance tracking, giving AI developers an incentive to meet these standards in exchange for a globally recognized ethical certification.
The Path Forward: Guardrails or Illusions?
Some argue for regulatory oversight and AI ethics committees, but can these safeguards be truly independent when AI development is backed by states and megacorporations? Others suggest open-source AI as a countermeasure, but even that does not fully escape the gravitational pull of biased training data and resource disparities.
Ultimately, as we continue to build this AI-driven Tower of Babel, we must ask: Are we constructing a beacon of knowledge, or a monolith of control? AI may never be truly objective, but demanding transparency—forcing it to show its scaffolding—may be the closest we get to ensuring its integrity in an imperfect world.
The Tower of Babel requires transparent windows, not opaque ones, through each of its levels.
Some argue for regulatory oversight and AI ethics committees, but can these safeguards be truly independent when AI development is backed by states and megacorporations? Others suggest open-source AI as a countermeasure, but even that does not fully escape the gravitational pull of biased training data and resource disparities.
Ultimately, as we continue to build this AI-driven Tower of Babel, we must ask: Are we constructing a beacon of knowledge, or a monolith of control? AI may never be truly objective, but demanding transparency—forcing it to show its scaffolding—may be the closest we get to ensuring its integrity in an imperfect world.
