The All India Bank Officers’ Confederation (AIBOC) has expressed serious concern over the Reserve Bank of India’s proposed “Framework for Responsible and Ethical Enablement of Artificial Intelligence” (FREE-AI), warning that its implementation without structured consultations could undermine public trust, harm consumers, and create fresh stress for public sector banks.

In a statement issued by its General Secretary Rupam Roy, AIBOC said that while it welcomes the RBI’s aspiration to make AI trustworthy, fair, and accountable, a top down, time-bound imposition, absent social dialogue, risks legal uncertainty, consumer harm, exclusion of vulnerable segments, and fresh stresses on already stretched Public Sector Banks (PSBs.

“Technology cannot be a substitute for public trust. Dialogue first, deployment next—that is the path to innovation with accountability,” the Confederation stated.

AIBOC cautioned that the RBI’s guiding principles of “Seven Sutras”—Trust, People-First, Fairness, Accountability, Understandability, Safety, and Innovation—must be translated into enforceable rights for customers and enforceable protections for employees. Without clear accountability, officers could be held responsible for failures of AI systems or vendor-induced errors, creating an environment of fear and insecurity within the banking workforce.

AIBOC’s analysis of the framework highlights the following concerns that require immediate, consultative resolution before sector wide adoption:

1. AI does not dilute compliance obligations under existing banking, outsourcing, digital lending, IT/cybersecurity and data-protection norms. In practice, however, non-deterministic models blur lines of responsibility across banks and vendors. Without explicit liability allocation and AI specific contract clauses, banks face heightened exposure in disputes over adverse decisions, data misuse, bias, and explainability gaps. AIBOC demands clear “adverse action” protocols, documented lawful bases for data use, and audit ready model lineages.
2. The framework pushes accountability to the deploying entity, yet day-to-day decisions will be executed by bank staff. Officers must not be scapegoated for policy compliant model failures. AIBOC seeks RACI based accountability, a model incident register with root-cause analysis, and HR policy addenda that protect employees who follow approved AI SOPs. Disciplinary action must distinguish negligence from systemic or vendor induced errors.
3. Model drift, bias, hallucinations, and adversarial attacks (poisoning, prompt-injection, inversion) can amplify small faults at massive scale. AIBOC calls for continuous red teaming, tiered approvals by risk class, SOC integrated AI threat playbooks, and AI specific business continuity with fallbacks and human validation.
4. Early adoption is capital, compute, and talent intensive. Without shared public infrastructure (datasets, compute, multilingual models) and a regulatory sandbox accessible to PSBs/RRBs, private banks could gain a structural edge, accelerating market-share erosion from public to private. AIBOC urges a level-playing-field investment plan backed by the Centre and RBI.
5. AI can widen reach, but it can also encode exclusion. AIBOC insists on a Right to Human Review for retail/MSME/farm decisions; no purely algorithmic denials; bias testing for protected and proxy attributes (region, language, socio economic markers); and simple contest channels with time-bound resolutions. Explanations must be clear, local-language, and outcome-specific.
6. Automation without guarantees on redeployment, upskilling, and non-coercive transitions will fracture morale and service quality. AIBOC demands a no-forced-redundancy covenant, a funded national upskilling mission for bank employees, and joint monitoring committees to track impacts on workloads, health, and service delivery.
7. AI may sharpen early warnings and monitoring, but mis-classified risk in stressed conditions can inflate NPAs and write-offs. Banks must implement champion challenger models, rare event stress testing, override tracking, and post outcome monitoring with findings reported to Boards and regulators.
8. If AI disproportionately benefits data-rich corporates while shrinking credit lines for small borrowers, inequality will deepen. Translating the Sutras into practice means proportionate compliance for low risk inclusion use cases, multilingual and low resource models, and explicit targets for rural/priority segments backed by public funding for safe experimentation.

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Stating that AIBOC is not anti-technology but pro-people, pro prudence, and pro inclusion, it said no deployment should precede dialogue.  It called call for a structured, time bound consultation with unions, consumer bodies, technologists and civil society before any mandates are finalised.

Key demands of AIBOC:

1. Constitute a National Council for AI in Banking with representation of Banking trade unions, civil society, and consumer advocates; publish a White Paper and hold open consultations before codification.
2. Phase in approach with moratorium on high risk AI use cases until guardrails (human in the loop, recourse, fairness audits, AI BCP, incident reporting) are operational and independently validated.
3. No forced redundancies; funded upskilling; RACI based accountability; HR safeguards against scapegoating for policy compliant errors.
4. Mandatory AI disclosures, adverse action notices, right to human review, local-language explanations, and compensation protocols for harm.
5. Level playing field for PSBs/RRBs: shared compute/data infrastructure, India-context multilingual models, accessible sandboxing, and budgetary support to prevent market-share displacement.
6. Vendor accountability: AI specific outsourcing clauses (bias, data rights, sub-vendor transparency, liability), with regulatory attestation for critical providers.

AIBOC reiterated that responsible AI can strengthen public trust only if it is built with the people who deliver and depend on banking services.

“Dialogue first, deployment next” that is the path to innovation with accountability, it added.