Alliance for Ai & Humanity (AAIH)

Explore the AAIH Insight Editorial Board in Singapore, featuring distinguished thought leaders and experts dedicated to advancing responsible AI, ethics, governance, and innovation. Gain valuable perspectives from global contributors shaping the future of trustworthy and human-centered artificial intelligence.

Is AI ethics just a performance? This bold op-ed by AAIH Fellow Sudhir Tiku exposes how ethical frameworks serve power rather than challenge it — and what real accountability must look like.

Explore how artificial intelligence is reshaping the boundaries of humanity in “AI, Posthumanism, and the Future,” a deep dive into transhumanist and posthumanist thinking. AAIH examines how emerging technologies challenge what it means to be human in an AI-driven world.

Is AI ethics just a performance? This bold op-ed by AAIH Fellow Sudhir Tiku exposes how ethical frameworks serve power rather than challenge it — and what real accountability must look like.

Learn how to use AI in a safe, fair, and responsible way with simple and practical training. AAIH helps you understand the basics of responsible AI so you can build better and trustworthy solutions. Responsible AI Training From Alliance for AI & Humanity equips professionals with skills in AI ethics, governance, and risk management. Learn to build transparent, fair, and compliant AI systems aligned with global standards and business needs.

Explore whether AI will shape or control the future and what it means for jobs, society, and technology. AAIH breaks down key insights into how artificial intelligence is influencing tomorrow’s world in simple terms.

Advance your career with a Responsible AI Certification Course from AAIH Singapore. Learn how to build ethical, transparent, and compliant AI systems. Join now and become a leader in responsible AI practices.

Find membership opportunities in Singapore with AAIH to be part of Discover Responsible AI. Network with experts, contribute to responsible AI development, and keep up with the latest information on responsible innovation, governance frameworks, and artificial intelligence standards.

Explore the growing divide in artificial intelligence between Western nations and the rest of the world. This insightful piece highlights key challenges such as unequal access to technology, regulatory differences, data governance, and innovation gaps. Learn how global collaboration, ethical frameworks, and inclusive policies can help bridge the AI divide and ensure equitable growth and responsible AI adoption across regions.

Looking to build AI solutions responsibly? A Responsible AI course helps you understand ethical AI practices, data privacy, bias mitigation, and global compliance standards. Learn how to design transparent, fair, and accountable AI systems while staying aligned with evolving regulations. Perfect for professionals and organizations aiming to lead in ethical AI innovation.

Explore AAIH Insights at AAIH Singapore, featuring thought leadership and expert perspectives from from Global Thought Leaders. Stay informed on the latest developments in artificial intelligence (AI), including ethics, governance, policy, and industry trends. Gain valuable knowledge from leading experts shaping the future of responsible AI and innovation.

Probability Can Never Be Permission — The Structural Flaw of Open Agent AI and the Conditions for the Next Standard examines the inherent risks in relying on probabilistic models to guide AI behavior. It argues that current open-agent AI systems can make unsafe decisions if probability is treated as permission. The Op-Ed outlines key principles and conditions needed to establish a safer, more reliable AI standard for the future.

Will AI control the future? Learn how artificial intelligence is changing businesses, industries, and the way we live and work. Understand the benefits and challenges of AI and stay ready for a future shaped by smart technology.

Advance your career with a Responsible AI course that teaches ethical frameworks, real-world governance strategies, and best practices for building trustworthy AI systems. Learn from industry experts and gain the skills to design, deploy, and manage AI responsibly in today’s evolving tech landscape.

1. Introduction: The Era of “Free Mistakes” Is Over

For the past three years, the mistakes of generative AI were little more than curiosities. Chatbots claiming that King Sejong threw a MacBook, or image generators producing six-fingered hands, were technical amusements—errors that cost no one any money. Within a chat window, mistakes could be erased at zero cost with a single refresh.

That era has ended.

In 2026, as AI systems connect directly to corporate workflows, financial systems, and physical control networks—what we now call agentic workflows—the nature of error has fundamentally changed. Hallucinations are no longer textual oddities. They return as misrouted ten-million-dollar transfers, incorrectly ordered inventory that must be scrapped, or hate speech that escalates into lawsuits. Errors now arrive as bills.

There is no longer room to excuse AI failures with phrases like “the data was insufficient” or “the model is still learning.” In business, it does not matter whether a mistake is technical or probabilistic. There is only one question that matters:

Who will write off the cost of this accident?

We have entered an era in which mistakes are no longer free—and errors are no longer abstract.

2. The Economic Definition of Hallucination

Engineers define hallucination as a probabilistic model producing plausible falsehoods. Chief Financial Officers and lawyers define it very differently. To them, hallucination is an uncontrolled risk that materializes as a contingent liability on the balance sheet.

Until now, AI performance has been measured almost exclusively by benchmark scores and parameter counts. This is akin to evaluating a car solely by its top speed while ignoring repair costs and insurance premiums after a crash. A sports car that crashes every 100 kilometers is not a means of transportation—it is a liability.

True AI cost-efficiency is not the price per token. It is the total cost of ownership (TCO)—including post-incident remediation costs, legal expenses, and reputational recovery. A technology that is correct ninety-nine times but capable of bankrupting a company on the hundredth mistake carries a mathematically negative economic value.

3. Privatized Profits, Socialized Risks

The current LLM supply structure exhibits a peculiar asymmetry. Model vendors promote performance and collect API fees, while quietly embedding disclaimers in their terms of service stating that responsibility for outputs lies entirely with the user.

This is a classic case of privatized profit and socialized risk. Would the market tolerate a pharmaceutical company selling a drug with known side effects while claiming that patients bear full responsibility for the outcome?

Yet in the AI market, this contradiction has become the norm. When discriminatory outputs damage a company’s reputation, or faulty code crashes production servers, the cost is not borne by the model provider. It is paid by the application company that adopted the model. Unless this unfair risk transfer is addressed, AI adoption remains a game of corporate roulette.

4. “Human in the Loop” Is the Most Expensive Cost Model

To mitigate these risks, the industry’s default response has been Human in the Loop—AI drafts, humans review. On the surface, this appears safe. Economically, it is contradictory.

The fundamental purpose of adopting AI is to reduce labor costs and maximize ROI. If organizations must rehire highly paid experts to continuously monitor AI outputs, the economic justification for AI evaporates.

This approach reduces human labor to a low-cost inspection mechanism to compensate for unstable systems. It is not innovation; it is the outsourcing of risk management to human time. True automation must be safe without constant human supervision. That requires structural systems—not people—to filter risk.

5. Liability Shapes Architecture: Lessons from the Elevator

History shows that safety advances were driven not by moral awakening, but by insurance and lawsuits. Elevator safety brakes became mandatory in the 19th century not because people died, but because building owners went bankrupt paying damages. Aviation safety regulations, too, were written in blood—and enforced by insurers.

The AI industry is approaching the same inflection point. When “the AI did it” no longer survives in court, companies will stop searching for smarter models and start demanding safer architectures.

Liability pressure forces architectural evolution. No CEO will stake a company’s future on probabilistic uncertainty inside a model. They will demand external control mechanisms that make responsibility deterministic. This is why a new responsibility structure operating outside the model is becoming essential.

6. The Invisible Liability: Loss of Trust Capital

Financial compensation is not the most dangerous cost. Loss of trust is.

When a human employee makes a mistake, the public blames the individual. When AI fails, the public concludes that the company’s system itself is rotten.

If an AI agent insults customers, leaks personal data, or recommends competitors’ products, the loss is not the day’s revenue. It is the erosion of trust capital accumulated over decades. This intangible cost does not immediately appear on quarterly financial statements, but it rapidly corrodes enterprise valuation.

Deploying AI is not merely installing software. It is delegating corporate reputation to a machine. Delegating reputation to an uncontrollable system without safeguards is not bold innovation—it is managerial malpractice.

7. The Missing Ledger: Where Are AI Actions Recorded?

Financial transactions are recorded in double-entry ledgers and audited by third parties. Where are the millions of judgments and actions taken by AI agents recorded today? Most vanish into ephemeral logs or opaque black boxes beyond the reach of auditors.

A system that cannot answer who decided what, when, and on what grounds after an incident is catastrophic. AI requires a digital audit ledger.

Not merely output logs, but immutable records of intent, decision criteria, and execution authorization—a ledger of responsibility. Without such a ledger, AI systems resemble vaults with disabled security cameras, where embezzlement and error are inevitable.

8. When Actuaries Design AI

In the near future, AI performance will be evaluated not by developers reading accuracy charts, but by actuaries analyzing risk tables. Insurance premiums will be calculated based on expected litigation costs.

Uncontrollable black-box models will face prohibitively high premiums and gradual exclusion from enterprise markets. Systems equipped with structural safeguards that verify and constrain inputs and outputs will earn lower risk scores and premiums.

Markets will reorganize around safety cost, not raw performance. Engineering goals will shift from “99% accuracy” to “zero catastrophic failure.” The winners will be those who offer the lowest liability cost.

9. Structure Is Legal Immunity: Due Diligence

The only viable defense against massive AI litigation is proving due diligence. In the AI era, this proof is not a claim of “good training data.”

The only effective evidence is demonstrating systemic structures that prevent harm regardless of model behavior. External supervisors that intercept high-risk outputs before execution—dual architectures separating generation from permission—form the strongest legal shield.

Structure is not merely engineering. It is a legal safety net.

10. The Price of Autonomy

We want autonomous AI because it is convenient. Autonomous agents are efficient and seductive. But autonomy carries a heavy price tag. As control is relinquished, liability increases.

When autonomous vehicles take the wheel, responsibility shifts from the driver to the manufacturer. When agentic AI executes business tasks, responsibility shifts to system designers.

Are you prepared to pay that price? If your system cannot guarantee safety, do not grant autonomy. Or build a controlled sandbox before activating agents. Autonomy without control is not freedom—it is negligence.

11. Conclusion: Show Me Your Balance Sheet

Do not be seduced by polished AI demos. Demos show success; reality invoices failure. The gap between demo and product is liability.

True AI capability is not what systems can do, but what they can prevent themselves from doing.

It is time to change the question. Boards should stop asking, “How intelligent is this AI model?” and instead ask, “When this AI fails, who pays the bill?”

Do not assign responsibility to models. The bill will always land on the desk that approved action without control.

Responsibility is not something to be hoped for. It is something that must be engineered.

Postmodernism emerged as a deep suspicion toward grand narratives that claimed universal truth, moral certainty and historical inevitability. Thinkers such as Jean-François Lyotard argued that modern societies legitimated knowledge through sweeping stories about progress, reason, or emancipation and that these stories masked structures of power beneath their promise of objectivity. Artificial intelligence now enters this philosophical terrain not merely as a technical innovation but as a new site where knowledge is produced, validated and distributed at scale. The ethical question is therefore not limited to whether AI systems are biased or inaccurate, but whether they quietly reinstate a new grand narrative under the banner of data driven neutrality.

Large scale AI systems are trained on enormous datasets that aggregate fragments of human expression across languages, cultures and histories. The resulting models generate responses that appear comprehensive and balanced, yet the appearance of balance often conceals the dominance of certain linguistic and cultural patterns. A postmodern lens reminds us that no dataset is innocent and no aggregation is neutral because inclusion and exclusion are always political decisions shaped by economic and institutional power. When AI systems speak in a tone that feels universal, they may in fact be reproducing the epistemic priorities of those who control computational infrastructure and data pipelines. AI ethics informed by postmodernism therefore begins with incredulity toward algorithmic authority and treats model outputs as situated narratives rather than final verdicts.

The philosopher Michel Foucault argued that knowledge and power are intertwined in such a way that regimes of truth emerge through institutional practices rather than through pure rational discovery. Artificial intelligence embodies this insight in a technical form because the training process operationalizes certain patterns as statistically legitimate while marginalizing others as noise. What becomes predictive becomes authoritative, and what becomes authoritative gradually shapes institutional decisions in hiring, credit allocation, policing, and education. The ethical challenge is not only to remove bias but to interrogate the conditions under which particular forms of knowledge become encoded as algorithmic norms. When AI systems rank candidates or summarize political debates, they do not simply mirror reality but participate in constructing the categories through which reality is interpreted.

Postmodern theory also destabilized the idea that meaning is fixed and transparent. The work of Jacques Derrida emphasized that texts contain internal tensions and that interpretation depends on context, perspective, and difference. AI models operate by compressing linguistic variability into probabilistic structures that predict the most likely continuation of a sentence or argument. This process can produce remarkable fluency, yet it also risks smoothing over ambiguity and reducing the space for interpretive plurality. When millions of users rely on generative systems to draft essays, speeches and policy proposals, language itself may converge toward optimized patterns that privilege clarity and consensus over productive disagreement. An ethics shaped by postmodern sensitivity would resist this convergence and preserve room for heterogeneity, irony and dissent.

Another postmodern insight concerns the fragmentation of identity and the recognition that the self is not a stable essence but a dynamic construction shaped by social discourse. AI systems increasingly participate in the formation of identity by curating content, suggesting responses and recommending cultural artifacts. Recommendation engines influence aesthetic preferences, while language models influence how individuals articulate their thoughts in professional and personal contexts. The ethical issue is not simply that AI might manipulate users, but that it may normalize particular identity templates through subtle nudges that reward conformity to statistically dominant styles. Postmodern ethics calls for vigilance against normalization disguised as personalization and demands that individuals retain interpretive agency rather than becoming passive recipients of algorithmically filtered meaning.

Postmodernism also challenges the distinction between fact and narrative by highlighting the ways in which supposedly objective descriptions are embedded in cultural frames. AI intensifies this tension because predictive outputs often carry prescriptive implications. When a model identifies a neighborhood as high risk or a candidate as low suitability, the descriptive claim can quickly transform into a normative judgment that influences real world outcomes. The compression of what is statistically likely into what ought to be done exemplifies the ethical danger of conflating probability with value. A postmodern informed AI ethics would reintroduce a critical distance between prediction and prescription and ensure that algorithmic outputs remain advisory rather than determinative.

Finally, postmodernism encourages skepticism toward totalizing systems that claim comprehensive explanatory power. AI systems that integrate text, images, code and behavioral data may appear to approximate such totality, especially when marketed as general intelligence. Ethical reflection must resist the temptation to treat these systems as neutral arbiters of truth and instead recognize them as contingent constructions shaped by training data, optimization goals, and institutional incentives. In this sense, postmodernism does not reject technology but equips society with conceptual tools to question its authority and to prevent the reemergence of unexamined grand narratives in digital form.

Pluralism, Power and Ethical Design in an Algorithmic Age

If postmodernism invites skepticism toward universal narratives, it also opens space for pluralism and contextual ethics. Artificial intelligence systems operate across diverse cultural environments, yet many alignment frameworks implicitly assume a relatively homogeneous moral landscape rooted in liberal individualism. Postmodern thought reminds us that values are historically situated and socially negotiated rather than universally self-evident. An AI ethics attentive to pluralism must therefore acknowledge that concepts such as fairness, privacy, and autonomy carry different meanings across societies. The task is not to abandon shared principles but to design systems that accommodate contextual variation while preserving fundamental safeguards against harm.

Value pluralism becomes especially salient when AI systems are deployed globally in education, healthcare, and governance. A language model trained predominantly on English language data may reproduce assumptions about social roles and norms that do not align with local traditions elsewhere. Ethical design in this context requires participatory processes that involve diverse stakeholders in shaping model behavior and evaluation metrics. Postmodernism supports such participatory governance because it resists the imposition of a single authoritative perspective and instead favors dialogical engagement among multiple voices. In practice this may involve localized fine tuning, community oversight boards, and culturally sensitive auditing protocols that treat ethics as an ongoing negotiation rather than a static checklist.

Power asymmetry remains central to this discussion because the infrastructure necessary to train advanced models is concentrated in a small number of corporations and states. Control over compute resources, proprietary datasets, and cloud platforms translates into influence over which epistemic frameworks become globally dominant. Postmodern analysis of power can illuminate how these concentrations shape not only economic outcomes but also symbolic authority. When AI systems become embedded in search engines, productivity software, and public services, they mediate access to knowledge and opportunity. Ethical responses must therefore address structural inequalities by promoting transparency in data sourcing, equitable access to computational resources, and regulatory frameworks that prevent monopolistic consolidation of algorithmic influence.

Postmodernism also foregrounds the instability of meaning and the importance of interpretation. In the context of AI, this insight suggests that explainability should not be reduced to technical transparency alone. Providing access to model weights or architectural diagrams does little to empower ordinary users if they lack the expertise to interpret them. Ethical explainability requires communicative clarity that translates complex processes into narratives accessible to affected communities. Such translation acknowledges that meaning arises in dialogue and that trust depends on mutual understanding rather than on the mere disclosure of technical details.

Another crucial dimension involves cognitive sovereignty and the preservation of human agency in environments saturated with algorithmic mediation. AI systems that optimize engagement and predict preferences can gradually shape attention patterns and belief formation. Postmodern thought highlights how discourse structures perception and identity, which implies that algorithmic curation is never neutral. Ethical design must therefore incorporate mechanisms that allow users to adjust recommendation parameters, access alternative perspectives, and understand how their data informs content selection. Protecting cognitive sovereignty means ensuring that individuals remain capable of critical reflection rather than being subtly steered toward preconfigured informational pathways.

Environmental considerations further complicate the ethical landscape because the computational demands of large-scale AI systems carry material consequences. Data centers consume significant energy and water resources, and hardware supply chains involve extraction of rare minerals with social and ecological implications. A postmodern sensibility that questions narratives of technological inevitability can help resist complacency about these externalities. Instead of assuming that ever larger models represent linear progress, ethical discourse can interrogate the tradeoffs between performance gains and environmental costs. Sustainable AI development requires integrating ecological metrics into evaluation frameworks and incentivizing efficiency alongside capability.

Ultimately, integrating postmodern insights into AI ethics does not entail relativism or cynicism but rather a disciplined attentiveness to context, power, and plurality. Artificial intelligence systems are not autonomous moral agents but socio technical constructs embedded in networks of institutions, incentives, and cultural narratives. Recognizing this embeddedness prevents the reification of AI as an independent authority and reinforces the responsibility of designers, policymakers, and users to shape its trajectory.

The future of AI ethics will likely depend on balancing skepticism with constructive design. Postmodernism teaches that no system is beyond critique and that claims to universality warrant careful examination. At the same time, societies must develop shared frameworks that enable coordination and accountability in a world where algorithmic systems influence everyday life. The challenge lies in crafting governance structures that are flexible enough to accommodate diversity yet robust enough to prevent harm. By embracing incredulity toward meta narratives while committing to participatory and context sensitive design, AI ethics can evolve into a mature discipline capable of guiding technological development without surrendering to either naive optimism or paralyzing doubt.

Investigate the authoritative publications on Responsible AI by AAIH Singapore. Find research, guidelines, frameworks, and industry insights on ethical AI, governance standards, and responsible implementation of artificial intelligence.