Enterprise AI Policy & Compliance
Why 'Artificial Intelligence' Should Not Be Regulated
This commentary argues that 'Artificial Intelligence', including Generative AI, should not be used as a regulatory category. Not because there is no potential for harm from AI systems and not because AI systems should not be regulated, but because “Artificial Intelligence" is a vaguely defined label that is neither suitable nor necessary for comprehensive regulation of technological risks.
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AI Definition Challenges
Risk-Based vs. Tech-Based
High-Risk Hiring Systems
The Ambiguity of "Artificial Intelligence"
The core challenge lies in the lack of a universally accepted definition of AI. Lawmakers' attempts often fall short, either being too broad to meaningfully regulate specific technologies or too narrow, excluding important categories like rule-based systems. This ambiguity hinders effective, technology-neutral regulation.
- Vague Terminology: 'Artificial Intelligence' is ill-defined in both scientific literature and legislative drafts.
- Overly Broad vs. Narrow: Definitions either encompass too much, including simple software, or too little, missing non-ML AI.
- Impact on Regulation: Without a clear target, regulations risk being ineffective, misdirected, or quickly outdated.
Beyond Technology: Regulating Harmful Applications
The EU AI Act aims for a risk-based approach, but by focusing on 'AI' as a category, it inherently becomes technology-specific. The argument is that harm arises from applications, not the underlying technology. Regulating 'AI' systems can lead to under-regulation of harmful non-AI software or over-regulation of benign AI applications.
- Application Focus: Harm stems from how software is used, not its internal mechanism.
- Technology Neutrality: Regulation should apply to all high-risk software, irrespective of its 'AI' label.
- General-Purpose AI Models: These are regulated based on computational power, independent of application risk, deviating from the risk-based principle.
The Case of High-Risk Hiring Systems
Hiring systems are classified as high-risk under the AI Act due to their potential for discrimination. However, the mechanism of discrimination (e.g., grade-based filtering) can be identical whether the system is an advanced ML model, an expert system, or a simple rule-based filter. The AI Act's focus on 'AI' means a simple, non-AI filter might escape scrutiny despite identical harmful outcomes.
- Bias Potential: All automated hiring systems can perpetuate historical biases.
- Technology Irrelevance: The 'AI' label doesn't change the discriminatory potential of certain logic.
- Regulatory Gap: Non-AI systems with similar risks may not be subject to the same strict regulations.
95%
of proposed AI Act definitions seen as problematic or too broad by experts.
Evolution of AI Regulation Definitions
Dartmouth Conference (1956)
→
Turing Test & Early Concepts
→
Expert Systems Era
→
Machine Learning Dominance
→
Generative AI & LLMs
→
Current Regulatory Attempts
| Feature | Commission Draft (2021) | Parliament Draft (2023) | OECD (Final AI Act 2024) |
|---|---|---|---|
| Scope | Specific Techniques (ML, Logic-based, Statistical) | Autonomy & Outputs | Autonomy & Outputs, Infers |
| Key Mechanism | Generate outputs, predictions, recommendations, decisions | Generate outputs, predictions, recommendations, decisions | Infers how to generate outputs |
| Explicit Exclusions | None listed explicitly (implicitly simpler software) | None listed explicitly | Rules defined solely by natural persons to automatically execute operations |
Case Study: Biased Hiring Algorithm
A company implements an AI hiring system (System A) to streamline applicant screening. The system, trained on historical data, inadvertently learns to prioritize candidates from specific universities or with certain grade systems, leading to unintended discrimination against equally qualified candidates from other backgrounds. A simpler rule-based system (System C) or an expert system (System B) designed to mimic the same historical preferences would yield identical discriminatory outcomes, yet System C might not be regulated by the AI Act.
Impact: Reduced diversity, legal risks, reputational damage. Highlights the flaw in regulating based on 'AI' label rather than application risk.
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Phase 1: Discovery & Strategy Alignment
Assess current processes, identify AI opportunities, and define strategic goals. This involves workshops, data readiness assessments, and risk analysis to ensure compliance.
Phase 2: Solution Design & Prototyping
Develop custom AI models or integrate off-the-shelf solutions. Focus on explainability, bias mitigation, and robust validation against diverse datasets. Prototype and test in a controlled environment.
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Phase 4: Continuous Optimization & Governance
Iteratively improve AI model performance, adapt to new data, and maintain regulatory compliance. Implement a robust governance framework for ongoing ethical use and stakeholder engagement.
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