Technical Standards Shaping the Future of AI

By Vijeth Shivappa

AI’s rapid advancement has raised significant ethical and technical concerns, necessitating standards to balance innovation with responsibility. These standards provide a structured framework for organizations to develop and deploy AI systems that are trustworthy, compliant, and beneficial to society. They are developed by international organizations, governments, and industry bodies, reflecting a global effort to mitigate risks and maximize rewards. Research suggests several critical areas must be addressed in AI-specific technical standards, as outlined below:

Model Transparency and Explainability

AI models must be documented with clear descriptions of architecture, training processes, and hyperparameters, using standardized formats like ONNX or TensorFlow. Techniques like SHAP (SHapley Additive exPlanations) and LIME are recommended for interpretability, ensuring explanations are tailored to technical and non-technical audiences. Transparency is balanced with privacy, safety, and security, as noted in UNESCO’s Recommendation on the Ethics of AI.

Data Quality and Privacy

Standards must ensure data used for training, validation, and testing is accurate, representative, and free from biases that could lead to unfair outcomes.
Compliance with privacy regulations like GDPR, CCPA, and HIPAA is essential, with requirements for anonymization, pseudonymization, and secure storage (e.g., AES-256 encryption). Metadata standards for documenting data provenance, collection methods, and preprocessing steps are crucial for auditability.

Safety and Security

AI systems must resist adversarial attacks (e.g., FGSM, PGD) and have fail-safe mechanisms, such as human-in-the-loop approaches for critical applications.
Security protocols include encrypted APIs (e.g., TLS 1.3), protection against model theft, and certified robustness techniques like randomized smoothing, as detailed in IEEE standards like 2846 for automated driving systems.

Bias Mitigation and Fairness

Standards must include bias testing using fairness metrics (e.g., demographic parity, equal opportunity) and tools like Fairlearn or AI Fairness 360.
Regular audits are required to ensure AI systems do not perpetuate bias, promoting social justice and accessibility for all, as emphasized in the EU Ethics Guidelines for Trustworthy AI.

Interoperability

Standards should support standardized model formats (e.g., ONNX, PMML) and data formats (e.g., CSV, JSON, Parquet) for cross-platform compatibility.
APIs must follow REST or gRPC standards, and models should be optimized for common hardware (e.g., CPUs, GPUs, TPUs) and edge devices (e.g., TensorFlow Lite).

Governance and Compliance

Governance structures must include risk management, auditability (e.g., logging decisions for at least 12 months), and compliance with regulations like the EU AI Act, effective in 2025.
Risk assessments for high-risk applications (e.g., healthcare, finance) are required, using frameworks like NIST AI RMF, with regular audits by certified third parties.

Performance and Evaluation

Standardized metrics (e.g., precision, recall, F1-score for classification; BLEU, ROUGE for NLP) and domain-specific benchmarks (e.g., GLUE, COCO) are essential.
Robustness testing against adversarial inputs and stress testing for edge cases ensure reliability, with cross-validation techniques like k-fold for generalizability.

Ethics and Accountability

AI systems must adhere to ethical principles like fairness, transparency, and accountability, as outlined in frameworks like UNESCO’s Recommendation and OECD AI Principles.
Mechanisms for redress and responsibility, such as auditability and impact assessments, ensure accountability, addressing ethical trade-offs like accuracy vs. fairness.

Sustainability

Standards should promote energy-efficient AI, such as model quantization and pruning, and report carbon footprints for training and deployment.
Longevity and backward compatibility are considered to ensure sustainable AI development, aligning with UN Sustainable Development Goals.

Global Harmonization

Striving for international alignment is crucial, but challenges exist due to fragmented regulatory landscapes, as seen in the EU, U.S., and UK approaches.
Organizations like ISO, IEC, and NIST aim for consensus standards, with initiatives like NIST’s Global Engagement Plan (final version July 26, 2024) promoting cooperation.

Notable Frameworks and Standards are Pivotal in Operationalizing These Considerations

NIST AI Risk Management Framework (AI RMF 1.0)

Released August 3, 2021, updated March 25, 2025, it provides a structured approach to managing AI risks, encouraging alignment with international standards like ISO/IEC 5338 and ISO/IEC 42001.

ISO/IEC 42001:2023

A standard for artificial intelligence management systems (AIMS), offering a framework for AI governance, compliance, and risk mitigation, as highlighted in recent articles from KPMG (May 22, 2025).

EU AI Act

Effective in 2025, it enforces risk-based governance for high-risk AI systems, with standards detailing state-of-the-art methods for safety engineering, as noted in the European Commission’s standardization request C(2023)3215.

IEEE Ethically Aligned Design

Focuses on human well-being, with standards like IEEE 2846 for safety-related models in automated driving systems, emphasizing transparency and accountability.

OECD AI Principles

Guide AI development for inclusive growth, with standards covering terminology, risk management, and ethical considerations, supporting global trade and collaboration.

Recent Developments in Shaping AI Technical Standards

Global Cooperation

NIST’s Global Engagement Plan (NIST AI 100-5, July 26, 2024) aims for consensus standards, while UNESCO’s 3rd Global Forum in Bangkok (2025) highlights ethical AI standards.

Industry Initiatives

Companies like Microsoft and IBM advocate for responsible AI, with tools like Fairlearn and SHAP integrated into standards, addressing bias and transparency.

Regulatory Focus

The EU AI Act is a landmark, with standards development behind schedule (expected completion late 2025/early 2026), potentially leading to delays, as reported on May 26, 2025. The U.S. sees a surge in state-level AI laws, with NIST coordinating federal efforts through the Interagency Committee on Standards Policy (ICSP).

Conclusion

AI-specific technical standards address critical considerations like data quality, transparency, safety, and global harmonization, with frameworks like NIST AI RMF, ISO/IEC 42001, and the EU AI Act leading the way. They ensure responsible AI development. As of today, these standards are vital for compliance, trust, and societal benefit, with ongoing efforts to refine and harmonize them globally.