Insight: Beyond Algorethics: Addressing the Ethical and Anthropologic

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Winners Consulting Services Co. Ltd. (積穗科研股份有限公司), Taiwan's expert in AI Governance, alerts business leaders to a critical finding from 2025 academic research: AI recommender systems do not merely reflect user preferences—they actively construct human behavior and decision-making, creating governance risks that go far beyond what current algorithmic ethics ("algorethics") can address. For Taiwanese enterprises deploying recommendation algorithms in e-commerce, HR platforms, or content services, this research directly shapes how ISO 42001 certification, EU AI Act compliance, and Taiwan's AI Basic Law obligations must be approached. The question is no longer whether to govern your AI recommender systems, but whether your current framework is substantive enough to protect human autonomy at the organizational level.

About the Author and This Research

Octavian M. Machidon is a cross-disciplinary researcher specializing in AI ethics, human-computer interaction, and the anthropological dimensions of algorithmic systems. With an h-index of 12 and 684 cumulative citations, his work occupies an influential position in the rapidly evolving field of AI governance and ethics. These metrics signal that his research is not peripheral speculation but peer-validated scholarship that shapes how the academic and policy communities think about the relationship between AI systems and human well-being.

Published in 2025 in the journal AI Governance & Ethics, this paper arrives at a pivotal moment: regulators worldwide are codifying AI oversight requirements, enterprises are scrambling to understand their obligations, and the gap between technical compliance and genuine ethical governance is becoming increasingly visible. Machidon's contribution is to articulate precisely why that gap exists and what a more complete framework would look like.

His methodology bridges ethical philosophy, anthropology, and computer science—a combination that allows him to ask questions that pure technical analysis cannot reach. Rather than asking "How can we make recommendation algorithms fairer?" he asks the more fundamental question: "What does it mean for an AI system to respect the full complexity of human existence?" This framing has direct practical implications for any enterprise building an AI management system under ISO 42001.

Core Research Findings: The Systematic Reduction of Human Complexity

The central insight of Machidon's paper is both philosophically rigorous and practically urgent: AI recommender systems do not simply optimize for user satisfaction—they enact a systematic reduction of human beings to quantifiable behavioral profiles. This is not a bug that better engineering can fix; it is an inherent tendency of how recommendation architectures are designed to operate. Understanding this distinction is essential for Taiwanese enterprises seeking to build AI governance frameworks that satisfy both the letter and the spirit of ISO 42001 and the EU AI Act.

Finding 1: Algorethics Is Necessary but Insufficient

"Algorethics"—the effort to embed ethical principles such as fairness, transparency, and privacy protection directly into algorithmic design—has been the dominant response to concerns about AI recommender systems. Machidon acknowledges its value but argues compellingly that it addresses symptoms rather than causes. The fundamental problem is structural: recommender systems are built to convert the rich, dynamic, contradictory nature of human beings into static preference vectors that can be optimized. No amount of fairness-weighting or transparency disclosure changes this underlying architecture. For enterprise AI governance teams, this means that an ethical review conducted solely by data scientists or engineers—even with the best intentions—cannot fulfill the holistic requirements of ISO 42001's human-centered AI management mandate. Governance must extend beyond the algorithm itself to the organizational decisions, power structures, and cultural assumptions that shape how AI systems are designed and deployed.

Finding 2: A Three-Dimensional Framework for Human-Centered Recommender Systems

Machidon's constructive contribution is a three-dimensional framework that moves beyond algorethics toward a genuinely human-centered approach. The first dimension is policy and regulation: external normative boundaries that establish minimum standards for how recommender systems may operate, enforced by legal and regulatory mechanisms. The second dimension is interdisciplinary research: ongoing empirical investigation into the actual effects of recommender systems on human autonomy, mental well-being, and social cohesion, providing the evidence base that good policy requires. The third dimension is education and digital literacy: equipping end users with the critical capacity to engage with AI-curated environments as informed agents rather than passive subjects. Critically, these three dimensions are mutually reinforcing rather than independent: research informs policy, policy establishes standards and enforcement mechanisms, and education ensures that users are not merely governed but are active participants in the governance ecosystem. For Taiwanese enterprises, this framework maps directly onto the multi-stakeholder governance structure that ISO 42001 requires—and signals that effective compliance cannot be achieved through technical measures alone.

Finding 3: Recommender Systems Exploit Vulnerabilities and Prioritize Engagement Over Well-Being

A particularly important observation in Machidon's analysis is that recommender systems are structurally incentivized to exploit user vulnerabilities—not because developers intend harm, but because engagement optimization and genuine well-being are often misaligned objectives. Platforms that maximize time-on-site or click-through rates may simultaneously be reducing users' capacity for autonomous decision-making, deepening cognitive biases, and undermining mental health. This finding has direct implications for how enterprises conduct AI impact assessments under ISO 42001 and how they define "harm" in their risk classification frameworks aligned with the EU AI Act.

Implications for Taiwan AI Governance Practice

Machidon's research arrives at a moment when Taiwanese enterprises face a convergence of international and domestic regulatory pressures that make proactive AI governance not just ethically desirable but commercially necessary.

EU AI Act High-Risk Classification: The EU AI Act, which entered into force in 2024, classifies AI systems that influence significant individual decisions—including employment screening, credit assessment, and educational access—as high-risk systems subject to stringent transparency, accountability, and human oversight requirements under Article 6 and Annex III. Taiwanese enterprises serving European markets or operating within European supply chains must assess whether their recommender systems fall within this classification. The consequences of non-compliance include fines of up to 3% of global annual turnover for violations of EU AI Act obligations.

ISO 42001 Certification Requirements: ISO 42001, published in 2023 as the world's first international standard for AI management systems, aligns closely with the human-centered framework Machidon proposes. Certification requires enterprises to demonstrate not only technical risk mitigation but systematic organizational governance: documented AI impact assessments, clear accountability structures, human oversight mechanisms that are genuinely operative (not merely nominal), and continuous monitoring of AI system effects on stakeholders. Enterprises deploying recommendation algorithms must be able to show auditors that their governance framework addresses the anthropological risks Machidon identifies—not just the technical ones.

Taiwan AI Basic Law: Taiwan's AI Basic Law (人工智慧基本法), currently advancing through the Legislative Yuan, establishes human dignity and fundamental rights as non-negotiable boundaries for AI development and deployment. Machidon's finding that recommender systems systematically erode user autonomy is precisely the category of risk that Taiwan's legislative framework aims to prevent. Enterprises that build robust governance mechanisms now will be better positioned to demonstrate compliance when the law's implementing regulations are finalized.

How Winners Consulting Services Co. Ltd. Supports Taiwanese Enterprises

Winners Consulting Services Co. Ltd. (積穗科研股份有限公司) provides Taiwanese enterprises with the expertise needed to translate research insights like Machidon's into concrete, auditable governance mechanisms that satisfy ISO 42001, EU AI Act, and Taiwan AI Basic Law requirements. Our approach is grounded in the same three-dimensional logic the paper advocates: we combine regulatory knowledge (policy), evidence-based risk assessment (research), and organizational capability building (education) to create governance frameworks that are genuinely effective rather than superficially compliant.

1. AI Recommender System Risk Classification Audit: We conduct structured assessments of your existing AI recommender systems against the EU AI Act risk taxonomy and ISO 42001 requirements. This includes identifying which systems may qualify as high-risk under Article 6, mapping data flows and user impact pathways, and generating a prioritized remediation roadmap. This audit is the essential first step before any ISO 42001 certification process and provides the evidentiary foundation for all subsequent governance decisions.
2. Cross-Functional AI Governance Structure Design: Reflecting Machidon's three-dimensional framework, we help enterprises move AI governance from a single-department responsibility to an organization-wide accountability structure. This includes designing AI Ethics Review Committees with appropriate cross-functional membership (legal, compliance, HR, business, IT), defining RACI matrices for AI accountability roles, and establishing escalation protocols for high-risk AI decisions. This structure directly addresses the ISO 42001 requirement for demonstrable organizational accountability.
3. Layered Digital Literacy and AI Ethics Training Program: We design and deliver training curricula tailored to different organizational levels—strategic awareness for senior executives, risk identification skills for middle management, and responsible AI use practices for frontline employees. This capability-building component is essential for sustaining governance mechanisms over time and for satisfying ISO 42001's human competence requirements. It also directly enacts the education dimension of Machidon's framework within your organization.

Frequently Asked Questions

What is the most common AI governance blind spot for Taiwanese enterprises using recommender systems?

The most common blind spot is treating recommender system governance as a purely technical problem. Most Taiwanese enterprises delegate AI ethics oversight entirely to IT or data science teams, without legal, ethical, or cross-functional review. Machidon's research makes clear that the core risks of recommender systems—erosion of user autonomy, exploitation of cognitive vulnerabilities, prioritization of engagement over well-being—are not technical defects that engineers can fix. They are structural features that require organizational governance responses. ISO 42001 explicitly requires enterprises to demonstrate multi-level accountability for AI systems, not just technical compliance. Enterprises that confine AI governance to the IT department will find it difficult to satisfy certification auditors or regulatory examiners.

Does the EU AI Act apply to Taiwanese companies using AI recommender systems?

Yes, potentially and significantly. The EU AI Act has extraterritorial effect: if your AI system's outputs affect individuals within the European Union—whether as customers, employees, or users—your enterprise may fall within its jurisdiction regardless of where your headquarters are located. For Taiwanese e-commerce platforms, HR technology providers, or content platforms serving European markets, this is not a hypothetical concern. AI recommender systems that influence employment decisions, credit access, or educational opportunities may be classified as high-risk systems under Article 6 and Annex III, triggering requirements for transparency documentation, human oversight mechanisms, and bias testing. Penalties for non-compliance can reach 3% of global annual turnover. Winners Consulting Services Co. Ltd. recommends initiating an EU AI Act applicability assessment immediately.

What specific requirements does ISO 42001 impose on enterprises using AI recommender systems?

ISO 42001, published in 2023, requires enterprises to establish a comprehensive AI management system that goes well beyond technical controls. For recommender systems specifically, this includes: documented AI risk assessments that address impacts on individual autonomy and dignity (not just data security); clear organizational accountability structures with named roles responsible for AI governance decisions; human oversight mechanisms that are operationally effective—meaning algorithms can be overridden by human judgment when necessary; continuous monitoring of how AI systems actually affect stakeholders over time; and training programs that build genuine AI literacy across the organization. Machidon's three-dimensional framework maps closely onto these requirements: the policy dimension corresponds to regulatory compliance, the research dimension to ongoing impact monitoring, and the education dimension to the competence-building ISO 42001 mandates. Winners Consulting Services Co. Ltd. provides full ISO 42001 implementation support.

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