Ethical AI discussions have accompanied the field's development since early machine learning algorithms emerged. As AI systems demonstrate capabilities that surpass human performance in traditionally exclusive domains, ethical considerations have become very timely.

The proposed "Ethics and Social Implications Overview" standard aims to enhance AI predictability and safety through systematic approaches to preventing algorithmic misuse. Primary risks center on information collection and usage practices, including unauthorized access and training on biased, inaccurate or unrepresentative data. The "black box" phenomenon—characterized by opaque AI decision-making mechanisms—is compounded by insufficient transparency in tracking data, processes and algorithmic outcomes. Incomplete understanding of AI's long-term social impact adds to the list of ethical challenges.

The developing standard avoids promoting any particular system of values, encompassing diverse criteria from accountability and non-discrimination to personal data protection.

AI controllability

The "Automated AI System Controllability" standard proves particularly vital for medical, legal and transport applications. Neural networks frequently make inexplicable decisions, raising questions about human-AI control transfer mechanisms in critical situations and algorithm reliability verification.

The standard establishes AI observability requirements, including real-time status monitoring capabilities. Control transfer protocols define conditions and procedures for human intervention in automated processes. Uncertainty response algorithms receive particular attention, forming essential safety elements for unforeseen circumstances.

AI security and privacy

The "AI Security and Privacy" standard addresses the defining challenges of the digital age: personal data protection and preventing the misuse of AI’s capabilities. The standard offers systematic approaches to preventing inadvertent personal data disclosure or recovery through indirect evidence.

Identifying AI architectural vulnerabilities potentially exploitable for illicit purposes forms a key component. Risk assessment methodologies and protective mechanism implementation create comprehensive cybersecurity systems for AI technologies.

Environmental applications

Belarus's AI standardization approach distinctively emphasizes natural resource management and environmental protection applications. AI proves highly effective in identifying forest areas conducive to disease-carrying insect proliferation, which is critical for preventing zoonotic disease epidemics.

For example, UNDP supported the AI-based Zzapp Malaria programme, achieving a 52% reduction malaria incidence. The solution proved twice as effective as conventional mosquito nets and remains adaptable for other geographic regions with similar climates.

AI also significantly improves natural disaster prevention and early warning systems. UNDP's SDG AI Lab has developed prototype systems that alert rural Madagascar populations to impending droughts and calculate safe earthquake evacuation routes in Istanbul, Turkey. 

Future trajectory

UNDP efforts have fostered expert communities establishing technical committees responsible for AI standard review and approval. This foundation will support future intergovernmental technical committees harmonizing standards across jurisdictions. Plans include expanded private sector and NGO cooperation alongside digital education programme development, leveraging the expertise of the UNDP Regional Centre in Istanbul.

Coordinated efforts between state institutions, scientific communities and international partners create robust institutional frameworks for future technologies as SDG accelerators.

Belarus's AI standardization experience demonstrates the possibility of harmoniously combining technological innovation with sustainable and ethical development principles — potentially serving as a benchmark for countries building responsible digital economies of the future.