The study, authored by Nirmal Acharya of Central Queensland University and Padmaja Kar of the Australian International Institute of Higher Education, argues that conventional transport-AI governance was built for a different species of machine. Older tools such as adaptive signal controllers, origin-destination estimators, and automatic vehicle location pipelines had bounded decision envelopes, numerical outputs, and failure modes that could be written into procurement documents. Generative systems break every one of those assumptions simultaneously. Their outputs are open-ended natural language, their failure modes include hallucination and prompt injection, and their decision envelopes shift whenever a vendor silently updates the base model, something a municipal contract cannot pin down.

The authors first test whether the four canonical cross-sector AI instruments can fill the void: the NIST AI Risk Management Framework, the EU AI Act, the OECD AI Principles, and ISO/IEC 42001. Each, they conclude, is serious but insufficient. The NIST framework’s domain-agnostic functions do not specify safety-critical, spatially distributed failure modes. The EU AI Act’s high-risk obligations assume the product-lifecycle conformity familiar from medical devices, not foundation models that retrain continuously. The OECD principles are non-binding and offer no maturity scale, and ISO/IEC 42001’s annual audit cadence is incompatible with systems that drift on weekly timescales. These instruments answer what values an agency should uphold, but not how mature a given system’s governance is today or what the next operational increment should be.

To make the gap tractable, the paper organizes the missing controls into six governance voids, split into two classes. Operability voids break day-to-day assurance: output-level traceability, where a single prompt’s output cannot be traced to an audit artifact because prompt-response logs and versioned model snapshots are not contractually retained; audit-cadence mismatch, where a system passes a scheduled audit and then changes materially the following week through a vendor-side update; and meaningful human control, where operators flooded with high-frequency co-pilot recommendations learn to rubber-stamp approvals, leaving logs that are formally complete but cognitively empty. Institutional voids break the contracts that make operability enforceable: liability diffusion across the vendor, integrator, and operator chain; equity measurement that ignores the conversational surface where accents and dialects determine whether service is delivered at all; and cross-jurisdictional portability, where conformity in a strict regulatory regime does not transfer to a lighter one.

The remedy is a five-pillar lifecycle governance framework: transparency and explainability, accountability and liability, equity and non-discrimination, auditability and oversight, and human-in/on-the-loop control. Each pillar carries a five-level maturity scale from 0 to 4, anchored in concrete transport failure scenarios. On transparency, level 2 requires public model cards, disclosed geographic and linguistic training boundaries, and prompt-response logs retained for at least 90 days, while level 4 offers citizens a public API to query and appeal decisions. On accountability, level 2 demands a formally mapped decision authority chain, frozen third-party updates before validation, and insurance covering generative-AI failures. On human control, level 2 requires a real-time override usable within five seconds and logged in structured form.

The framework’s dependency lattice is where the argument becomes sharpest: transparency feeds auditability, which feeds accountability, which feeds human control, with equity feeding back into transparency as disparate-impact findings reveal which lineages must be disclosed. The ordering is causal, not decorative. A city that writes strong accountability contracts while publishing no model or data-lineage cards cannot enforce those contracts after an incident, because the audit trail needed to allocate responsibility does not exist. Similarly, operators pressing override controls cannot reconstruct outcomes if auditability is missing. The authors frame this as a ceiling effect rather than a temporal rule: upstream maturity is necessary but not sufficient, and out-of-order investment yields diminishing returns rather than certain failure.

What distinguishes the paper from most governance literature is its commitment to falsifiability. The authors propose twelve testable propositions linking pillar maturity to observable outcomes, including incident severity, mean-time-to-resolution, disparate-impact detection latency, override-exercise rate, deployment survival, and public trust. Candidate designs range from partial least squares structural equation modeling on transit-authority surveys, to difference-in-differences comparisons of matched incidents, to Cox proportional-hazards survival analysis on metropolitan pilots, to scenario-based rider surveys testing whether transparency builds trust indirectly through perceived contestability. All sample sizes are explicitly labeled as illustrative pending pilot power analysis, and the authors stress that no proposition counts as tested until its design is pre-registered.

For practice, the framework translates into a procurement-grade baseline of level 2 on all five pillars, written into generative-AI tenders as enforceable evaluation criteria. Agencies are advised to negotiate clauses requiring at least 24 hours notice of vendor-side model updates and prompt-log retention of at least 90 days. Vendors get design-phase boundaries, and regulators get a lifecycle conformity-assessment scaffold calibrated to generative drift timescales rather than annual cycles, with jurisdictions able to set minimum pillar levels vendors must demonstrate across all operating territories to prevent governance race-to-the-bottom. The authors caution that the framework is theoretically derived and not yet empirically validated, and that data-access friction, particularly undisclosed vendor prompt logs, will require partnerships with municipal authorities.

The validation path proceeds in three phases: pilot testing of the maturity matrix on three to five metropolitan planning organizations with inter-rater reliability targets, early empirical tests of four propositions using publicly available data, and comparative case studies of the cross-pillar predictions over six-to-eighteen-month operating windows. Whether the framework survives contact with real dispatch rooms, chatbot deployments, and signal-control centers will determine if generative AI in urban mobility gets the lifecycle scaffolding that, the authors argue, currently exists nowhere in the transport governance toolkit.

Subject of Research: Governance of generative AI in urban mobility operations

Article Title: Governing generative AI in urban mobility: a five-pillar framework and twelve falsifiable propositions for operations, accountability, and public trust

Article References: Acharya, N., & Kar, P. (2026). Governing generative AI in urban mobility: a five-pillar framework and twelve falsifiable propositions for operations, accountability, and public trust. Frontiers in Future Transportation, 7, Article 1951756. https://doi.org/10.3389/ffutr.2026.1951756

Image Credits: AI Generated

DOI: 10.3389/ffutr.2026.1951756

Keywords: generative AI, urban mobility, governance framework, transport policy, AI accountability, algorithmic auditing, public transit, foundation models, AI ethics, maturity model, human-in-the-loop, transport governance

The study’s starting point is a theoretical one. Disaster and emergency management, the authors contend, behaves as a complex adaptive system: a network of interdependent actors—central and local government agencies, emergency services, Indigenous partners, civil society organizations, and private sector entities—whose roles and relationships shift as hazard conditions evolve. Three properties of such systems matter most for DEM. Self-organization allows actors to reconfigure coordination autonomously, as observed during the 2011 Tōhoku disaster in Japan, where local actors improvised networks after formal command structures collapsed. Nonlinearity means disruptions can propagate in ways that produce consequences disproportionate to their initial cause, a dynamic famously analyzed by Charles Perrow in his work on normal accidents. Emergent behavior, meanwhile, means system performance arises from interactions among actors rather than from prescribed plans, and therefore cannot be fully anticipated or controlled in advance.

These characteristics expose a structural mismatch. Leadership scholarship has evolved from trait-based theories toward relational, situational, and transformational models, yet it has remained anchored within the organizational unit rather than the interorganizational system. Even systems-oriented approaches such as meta-leadership and complexity leadership theory retain a largely leader-centric orientation, privileging the individual actor as the primary locus of control. The limitations of that orientation are visible in disaster history: post-event analyses of Hurricane Katrina identified failures of individual leadership alongside structural fragmentation, unclear cross-agency mandates, and inadequate mechanisms for aligning distributed actors toward shared objectives. The 2010 Haiti earthquake similarly showed how leadership capacity interacted with pre-existing institutional weakness and fragmented international coordination.

To fill this gap, the researchers turned to stewardship theory, which emerged as a counterpoint to agency theory’s assumption that actors are self-interested and require monitoring and incentives to perform. Stewardship instead conceptualizes actors as fundamentally motivated to pursue collective goals when organizational identification and value alignment are strong. Extended to the system level, stewardship becomes a form of meta-governance—concerned with shaping a system’s goals, rules, feedback, and response—rather than with directing operational activity. The concept has been applied in natural resource governance, public health system management, and complexity-informed public policy, but had not been systematically examined within DEM, despite the field’s combination of distributed authority, multi-agency interdependence, and nonlinear hazard environments.

The empirical work employed constructivist networked grounded theory, a methodology combining Charmaz’s constructivist grounded theory with social network analysis to examine both the relational structure of the DEM system and practitioners’ experiences within it. Data were collected between August 2024 and June 2025 from participants recruited across national forums, professional networks, emergency services, territorial authorities, civil society organizations, and private sector actors. Interviews ranged from 43 to 96 minutes, with a mean of approximately 60 minutes, and analysis proceeded through iterative coding, constant comparison, and progressive category refinement until theoretical saturation was reached. Notably, stewardship was not a predetermined analytical construct; it emerged inductively as the integrative category explaining how participants understood coherence, legitimacy, learning, and adaptive coordination to be sustained across organizational boundaries.

Four interdependent themes emerged from the analysis. The first concerned governing a distributed system. Participants described uncertainty about who holds overarching responsibility for the system as a whole, with national oversight perceived as bounded to particular hazards rather than extending across the full breadth of interagency coordination. Recurrent misalignment between nationally designed arrangements and local operational realities was a persistent friction, sharpened by the fact that national guidance, absent legislative force, cannot be required to be followed. Participants also described ambiguity about which actors were recognized as legitimate contributors to the system, with the operational network extending well beyond formally prescribed structures.

The second theme centered on trust and inclusive participation as the relational infrastructure of coordination. Participants emphasized that the quality of pre-existing relationships was a more reliable predictor of coordination effectiveness than formal arrangements or statutory roles—one practitioner quipped that you need to build a relationship over eight cups of coffee before you can start asking for something. Civil society organizations were described as providing relational networks and community intelligence that are substantial but underrecognized within formal system definitions. Indigenous partnership emerged as a distinct dimension: under Te Tiriti o Waitangi, signed in 1840, Māori hold an enduring basis for partnership in governance, and participants described how the quality of that engagement directly shaped both the legitimacy and operational reach of the system, though Treaty-based partnership quality varied significantly across regions.

The third theme addressed information sharing and learning. The aspiration of a common operating picture was widely referenced but characterized as aspirational rather than operational—one participant observed that the only common thing about it is the word common. Incompatible information systems, platform fragmentation, and institutional protectiveness over data combined to produce what the authors call a feedback deficit: a system theoretically capable of shared sensemaking but operationally fragmented along institutional boundaries. Compounding this, insights gained through events and exercises were described as fading as urgency declined and personnel changed, leaving institutional memory residing in individuals rather than organizational systems and rendering learning sporadic rather than cumulative.

The fourth theme concerned sustaining capability and legitimacy over the long term. Resourcing was described as unevenly distributed, with reliance on locally generated ratepayer revenue producing what participants called a postcode lottery of capability, where organizational capacity reflects local financial circumstances rather than hazard exposure or statutory obligation. Workforce sustainability emerged as a significant concern, with 31 of 40 participants describing burnout, illness, recruitment delays, inconsistent professional standards, and the absence of any centralized repository for qualifications or competency requirements. Accountability, meanwhile, was described as diffuse, operating primarily through post-event inquiry rather than ongoing assurance of preparedness and coordination quality.

From these findings, the authors distill a three-pillar framework of system stewardship. Relational stewardship encompasses the trust, boundary-spanning networks, and inclusive participation through which coordination becomes possible, including sustained investment in Treaty-based partnerships with iwi and the formalization of relationships into organizational arrangements rather than individual positions. Informational stewardship covers shared situational awareness, data transparency, and feedback-informed learning, requiring both technical investment in compatible information management and cultural change that treats learning as a shared system function. Institutional stewardship concerns equitable resourcing, workforce capability, and accountability mechanisms that sustain capability and legitimacy over time. Crucially, the three pillars are interdependent: weakness in one degrades the others, while alignment across all three creates the conditions for sustained adaptive performance. Trust facilitates information sharing; shared understanding strengthens institutional confidence; equitable resourcing enables the sustained engagement through which relationships are built.

The practical implications are considerable. For national agencies, stewardship implies a reorientation of the governance function—from setting requirements to cultivating conditions, from assuring compliance to strengthening capability. For policymakers, the framework makes visible the structural interdependencies between funding, professional standards, and accountability mechanisms, demonstrating that reforms to any single dimension in isolation are unlikely to produce durable improvement. The authors caution that the conditions underpinning system stewardship should not be assumed to persist indefinitely; trust, shared learning, institutional legitimacy, and collaborative relationships require continual investment and renewal, and may be eroded by organizational change, resource constraints, and competing priorities. The study’s limitations are acknowledged: findings derive from a single national context, reflect participant interpretations rather than direct observation, and capture a single point in time, though the framework forms part of a cumulative research program. Ultimately, the study argues that strengthening disaster and emergency management requires moving beyond episodic leadership during events toward the deliberate, continuous stewardship of the relational, informational, and institutional conditions through which adaptive coordination, collective learning, and long-term system coherence are cultivated and sustained.

Subject of Research: System stewardship as a complex adaptive system governance framework for disaster and emergency management in Aotearoa New Zealand

Article Title: Stewardship for Disaster and Emergency Management: A Complex Adaptative System Governance Framework

Article References: Miller, T., Le De, L., & Hore, K. (2026). Stewardship for Disaster and Emergency Management: A Complex Adaptative System Governance Framework. International Journal of Disaster Risk Science, 17(4), 725-738. https://doi.org/10.1007/s13753-026-00762-6

Image Credits: AI Generated

DOI: 10.1007/s13753-026-00762-6

Keywords: disaster and emergency management, system stewardship, complex adaptive systems, meta-governance, governance framework, adaptive coordination, Aotearoa New Zealand, trust and collaboration, institutional memory, emergency management workforce, Māori partnership, disaster risk science