As artificial intelligence becomes increasingly embedded in organizational decision-making, public administration and knowledge generation processes, the ethical risks of AI are frequently characterized by known issues like bias, transparency, fairness, privacy and explainability. Even though these issues are significant, they fall short of capturing the underlying political economics of AI systems, particularly how value is extracted, concentrated and dispersed across AI ecosystems.
The main ethical dilemma is not only to determine whether AI systems are robust, accurate or reliable but also who benefits from AI, who gets represented, who bears the cost and how advantages and disadvantages are distributed across data, infrastructure, labor, and governance layers. Value asymmetry, therefore, becomes a defining characteristic of digital colonialism that must remain at the centre of all AI ethics and governance issues.
At this stage where technology is moving faster than the regulations, AI audits, assurance and compliance frameworks can serve as practical instruments to identify and constrain value asymmetry provided these tools are interpreted more broadly than just a few box-ticking exercises or procedural checklists. AI audits currently are often limited to internal reporting, technical tests and post- hoc risk management. These approaches may improve the visibility into model behavior, but they frequently fail to highlight the structural conditions under which the AI systems are designed, procured, deployed and monetized. Therefore, the broader questions that determine whether AI contributes to shared value frameworks or to a concentrated group of representatives mostly remain unanswered.
AI audits are a subset of a larger assurance ecosystem that connects technical documentation, expert oversight, redress mechanisms and institutional accountability. It is worthy to consider that audit frameworks should not only assess whether a system is safe or compliant, but also whether it is equitable in the way it allocates value across stakeholders.
This requires examination of the entire AI lifecycle including data sourcing, model training, platform dependence, deployment contexts and post-deployment monitoring. Hidden asymmetries can appear at every stage: data may be extracted without meaningful consent; models may be developed using resources sourced from one context while profits accrue elsewhere; organizations may acquire AI systems that lead to vendor lock-ins; impacted communities may have limited recourse when systems cause harm. AI audits can make these asymmetries visible, early in the process, but only if they are designed to ask the right governance questions.
In many settings, compliance is treated as the minimum threshold for legal or organizational acceptability. Yet in the context of digital colonialism, compliance alone is insufficient because it does not address power imbalance, dependence on proprietary infrastructure or the uneven distribution of expertise and control. Assurance mechanisms, therefore, need to extend beyond documentation and disclosure to include meaningful accountability structures, including independent review, continuous monitoring, grievance mechanisms, contractual safeguards, and, where appropriate, redistributive remedies.
Such mechanisms can create concrete pathways for contesting value extraction and asymmetry, especially in environments where public institutions, smaller firms, or Global South actors have limited leverage over dominant AI vendors and infrastructure providers.
A key contribution of the well-designed governance ecosystem is to connect the language of auditing and compliance with the ethical and political goals of redistribution of value. In mainstream governance debates, audits are often considered as ways to mitigate risk, enhance trust, and demonstrate responsible practice. It is imperative to retain those goals but extend the evaluations to check on their ability to redistribute visibility, voice, responsibility and benefits. It starts with asking whether the governance process enables affected groups to participate in defining what counts as harm, whether it exposes the terms under which value is captured, and whether it creates enforceable obligations for actors who benefit most from AI systems.
In conclusion, it is now essential that AI ethics must switch from a narrow concern with system-level compliance toward a more ambitious goal of redistributive governance. Audits and assurance frameworks are useful not as standalone vehicles but as means to create structured visibility into the ways AI systems concentrate power and value. If designed thoughtfully, they can shift the AI governance landscape from passive monitoring to active correction of inequality. And, in doing so, they enable one of the most practical routes for resisting value asymmetry in the era of artificial intelligence.
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