AI-Allowed Condition: Criteria & Boundaries

• AI-allowed condition is a framework defining when AI systems are permitted to act, based on regulatory thresholds, risk analysis, and ethical guidelines.
• It incorporates multi-dimensional risk assessments that evaluate reliability, safety, security, and transparency through both quantitative and qualitative measures.
• The conditions span legal, societal, and technical domains, ensuring that AI systems function responsibly while supporting human oversight and flourishing.

An AI-allowed condition defines the set of circumstances, regulatory thresholds, technological stages, or societal contexts under which artificial intelligence systems are permitted to operate, make decisions, or assume roles typically governed by human professionals or institutional norms. Across domains such as law, medicine, defense, scientific research, and digital governance, the identification and operationalization of AI-allowed conditions are pivotal to ensuring responsible deployment, safeguarding human interests, and aligning technological advancement with core ethical and legal values.

1. Autonomous Levels of AI and Legal Authorization

A central framework for conceptualizing AI-allowed conditions in the legal domain is the Autonomous Levels of AI Legal Reasoning (AILR), an instrumental grid that maps AI capabilities to the requirements underpinning the Authorized Practice of Law (APL) and Unauthorized Practice of Law (UPL) (Eliot, 2020). The AILR specifies discrete levels:

• Level 0: No automation; all decisions are human.
• Level 1: Simple assistance (e.g., tools like word processing), triggering no legal advice implications.
• Level 2: Advanced assistance (e.g., query-based NLP or simple ML systems), possibly bordering on providing legal advice and raising minor liability questions.
• Level 3: Semi-autonomous automation (e.g., knowledge-based or intermediate ML/DL systems), generally delivers legal advice and likely incurs legal liability and minimal qualification requirements.
• Level 4: Domain autonomous operation, fulfilling many APL criteria partially or likely, such as duty of care or confidentiality.
• Level 5: Fully autonomous AI systems, assumed to be equivalent to human lawyers in regulatory responsibilities.
• Level 6: Superhuman autonomous systems, exceeding human capability (speculative).

The designation for whether a system meets key practice-of-law factors at a level $L$ is formalized as:

$$APL/UPL(K, L) = \begin{cases} \text{n/a}, & L = 0 \ \text{No}, & L = 1 \ \text{Maybe or No}, & L = 2 \ \text{Yes/Minimal/Likely}, & L = 3 \ \text{Yes/Partial/Likely}, & L = 4 \ \text{Yes}, & L = 5 \ \text{Yes Plus}, & L = 6 \end{cases}$$

Only at higher levels (principally 5 and 6) do AI systems satisfy the conditions for authorized legal practice across all dimensions, encompassing duty of care, confidentiality, enforceable codes of conduct, and liability.

2. Risk-Based and Trustworthiness Criteria

Across critical application areas, an AI system is "allowed" only if it satisfies multi-dimensional, risk-based trustworthiness criteria (Poretschkin et al., 2023). Key requirements include:

• Comprehensive Risk Analysis: For every core quality dimension—reliability, safety, security, data protection, transparency, autonomy, and control—a protection requirement is set, categorizing the risk as "high" or "medium" depending on potential harm.
• Measurable Quality Criteria: Each risk area is evaluated using both quantitative (statistical, accuracy, robustness, uncertainty) and qualitative criteria.
• Mitigating Measures: Documented actions such as adversarial training, data anonymization, and fail-safe mechanisms must be in place.
• Ongoing Accountability: Full documentation of risk analyses, mitigations, and reviews across the AI lifecycle is mandatory.
• Cross-dimensional Assessment: Residual risks must be negligible or justified via transparent, multi-stakeholder discussion and acceptance. Only then is an AI application deemed trustworthy and thus "allowed."

This multidimensional approach is necessary to ensure that the system satisfies operational, legal, and ethical requirements, and that trade-offs (e.g., between interpretability and performance) are justified and agreed upon.

3. Societal, Regulatory, and Ethical Boundaries

AI-allowed conditions are shaped by the societal, regulatory, and ethical contours of each field:

• In Law: A system is not authorized to practice law until it fulfills strict professional, relationship, and confidentiality criteria (Eliot, 2020).
• In Medicine: Regulatory approval (e.g., by NMPA in China) requires adherence to classification, risk documentation, clinical effectiveness, and transparent naming conventions (Han et al., 11 Nov 2024).
• In Science and Policy: The ethical legitimacy of interoperable AI—those able to connect disparate data "spheres"—depends on respecting the justice and autonomy of each social sphere and avoiding unfair dominance or bias (Demichelis, 2022).
• In Defense/Intelligence: "AI-allowed" status is contingent on the development of robust adversarial defenses, secure pipelines, and transparent resilience to attack (Shipp et al., 2020).

AI is allowed only when mechanisms are in place to guard against bias, misclassification, data leakage, system manipulation, and breaches of privacy or justice.

4. Design, Transparency, and Human Interaction

User interfaces and human–AI interaction design constitute a practical layer of AI-allowed conditions:

• AI-Resilient Interfaces: Systems must make their outputs and reasoning visible, contextually rich, and easy to scrutinize, allowing users to notice, judge, and—if required—recover from errors or inappropriate outputs (Glassman et al., 14 May 2024).
  • Conditions for allowance include cognitive transparency, support for user modifications, and noticeable error visibility.
• Cognitive Engagement and Learning: In educational and decision-support contexts, AI is more effective (and learnings more durable) when explanation-only assistance is provided, rather than simple recommendations, as this fosters deeper cognitive engagement (Gajos et al., 2022).

Allowing AI under such participatory and transparent conditions empowers users and supports better decision-making, critical for high-stakes deployments.

5. Data, Validation, and Contextualization

The allowance of AI in scientific and medical research often depends on data richness, validation, and the contextual integration of outputs:

• AI Evaluation in Aging/Longevity Research: An AI system is allowed to advise on interventions only if its outputs are correct, comprehensive, explainable, causally validated, interdisciplinary, standardized, and anchored in longitudinal, mechanistic data (Fuellen et al., 11 Aug 2024). Use of knowledge graphs (KGs) and retrieval-augmented generation (RAG) mechanisms support these high standards of validation.
• AI in Medical Devices: Allowance is strictly connected to documented regulatory pathways, robust technical files, standardized risk classification, and demonstrable clinical advantage (Han et al., 11 Nov 2024).

This approach ensures responsible, evidence-based deployment and reduces the risk of harmful or spurious recommendations.

6. Automation, Human Flourishing, and the Telos of Law

An emerging philosophical and legal approach considers AI-allowed conditions in terms of societal telos, or greatest human flourishing:

• Virtue Jurisprudence and Eudaimonia: Under a neo-Aristotelian framework, AI-driven automation is "allowed"—indeed, normatively desirable—when the legal order is reoriented to facilitate leisure and personal development as necessary preconditions for eudaimonia (human flourishing). Policies and regulations are justified not to preserve work for its own sake, but to foster conditions in which AI liberates individuals for intellectual, creative, or civic pursuits (Siapka, 31 Oct 2024).

Such conditions challenge conventional regulatory wisdom, focusing instead on outcomes aligned with virtue and quality of life.

7. Future Challenges and Directions

Current and future AI-allowed conditions are dynamic and context-sensitive. Key challenges include:

• Adapting Regulatory Frameworks: Increasing system autonomy requires ongoing updating of professional, ethical, and legal frameworks to address new forms of liability, explanatory demand, and cross-domain spillover (Eliot, 2020, Poretschkin et al., 2023).
• Data and Interoperability Risks: Greater interoperability and data fusion heighten risks of injustice, bias, and privacy erosion, necessitating precise boundaries and transparency mechanisms (Demichelis, 2022).
• Technical and Human Oversight: Future AI-allowed conditions will likely require more robust benchmarks, validation regimes, and participatory oversight to align system autonomy with societal benefit (Fuellen et al., 11 Aug 2024, Glassman et al., 14 May 2024).

Summary Table: Key Dimensions of AI-Allowed Condition

Dimension	Central Criterion	Representative Paper
Autonomy / Regulatory Status	AILR Level 5+ or explicit risk-based approval	(Eliot, 2020, Poretschkin et al., 2023)
Trustworthiness / Safety	Multidimensional, documented assessments	(Poretschkin et al., 2023, Shipp et al., 2020)
Societal/Ethical Appropriateness	Respect for domain autonomy and justice	(Demichelis, 2022, Siapka, 31 Oct 2024)
Transparency / User Engagement	Contextual outputs, ability to recover/contest	(Glassman et al., 14 May 2024, Gajos et al., 2022)
Data/Validation Requirements	Comprehensive, explainable, reproducible	(Fuellen et al., 11 Aug 2024, Han et al., 11 Nov 2024)

AI-allowed conditions are, therefore, a structured yet evolving set of procedural, technological, and normative requirements that ensure AI outputs are safe, justified, explainable, and contextually appropriate. The continual reassessment and refinement of these conditions is fundamental to the responsible and beneficial integration of AI into critical realms of society.