From Functional Unemployment to the Ontological Worker: Why AI Uproots Value, Not Just Jobs

Introduction: The Tyranny of Statistics and Socio-Economic Noise

The global debate on Artificial Intelligence (AI) has become hostage to statistics. Data from giants like PwC, which forecasts the displacement of 20% of existing jobs by 2037 (see PwC 2018 analysis), or the World Economic Forum (WEF), which anticipates a “disruption of 44% of core skills by 2027” (according to the WEF Future of Jobs Report), create intense functional noise. This noise traps us in a myopic view: that of functional risk – structural unemployment, income inequalities.

This approach constitutes a strategic omission. Most of the debate ignores the critical aspect: the non-conscious ontology of AI. AI does not “want” to take jobs; AI is a probability-based process. Yet, in the absence of consciousness, AI objectively amplifies systemic biases within its training data, transferring structural human errors into the productive core.

Our thesis is simple: the challenge is not economic, but ontological. We are not threatened by mass unemployment, but by the uprooting of the value of labor from beneath the umbrella of human judgment.

Non-Conscious AI and the Amputation of Responsibility

The problem starts with the design premise: AI is a prediction machine devoid of mens rea (culpable intent). When a recruitment algorithm excludes a certain socio-professional category, society searches either for a human culprit or for a vicious “intent” within the code.

This search is futile. As Justice News247 argues in “Why AI Should Be Devoid of Responsibility When Nobody and Nothing Is Perfect?” LINK, attributing responsibility to AI is a logical trap.

• The Structural Answer: If AI is non-conscious, it cannot be responsible for bias. Instead, it becomes a faithful mirror of the ethical insufficiency in human datasets. Thus, the challenge is not the elimination of jobs, but the elimination of the human judgment necessary to identify and correct the amplified bias, especially in critical fields (see the analysis “Robert Williams: The Algorithmic Scales of Justice” concerning bias in law enforcement).

De Lege Ferenda Proposals – Anchoring Philosophy in Workforce Transition

To shift the debate from statistical lament to structural solution, we propose the immediate integration of ontological ethics into the global legislative architecture:

1. Creation of the “AI Workforce Transition Framework” (AITF)

This framework must be mandatory at the EU and global levels, aiming to recognize and mitigate the ontological risk, not just the functional one:

• Extension of the Ethical Design Audit (EDA): The EDA must be mandatorily extended to systematic evaluation of training data. It is not enough to test the outcome; we must certify the ethical design of the inputs to eliminate systemic biases before they are amplified by non-conscious AI.
• Mandatory Non-Anthropomorphized Reskilling Funds: Reskilling funds must be mandatory for AI-adopting companies, focusing on developing unique human competencies that involve judgment and philosophy, not just formal technical skills.

1. Integration of the “Systemic Functional Error (SFE)” into the AI Act

We must define SFE as a distinct legal category within the AI Act. SFE would sanction functional errors resulting from the conflict between AI optimization goals and ethical values, without implying mens rea. This ensures that damages are treated as a structural quality problem and not a moral failure of the machine.

Conclusion and Call to Action

Ignoring the non-conscious ontology of AI costs us more than jobs; it costs us ethical coherence. Our response must be structural and philosophical.

Justice News247 demands that this vision be proposed and debated at major global forums:

• UNESCO Global Forum AI Ethics 2025 (Bangkok, June 24-27)
• WEF Future of Jobs
• Call to Action on LinkedIn: Launching an extensive debate on the necessity of reskilling based on rational judgment, not just technical competencies.

Concluding Note: This article contains observations and analyses based on philosophical and structural judgment, intended to enhance public debate. It is in no way intended to interfere with the activity or policies of the cited entities (e.g., PwC, WEF) and does not represent an offense to them or any other involved parties.

By

Robert Williams

Editor in Chief