Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of artificial intelligence with geographic information science, combined with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by integrating heterogeneous data sources such as satellite imagery, GPS trajectories, transit records, volunteered geographic information, and social sensing data into scalable, high-resolution urban mobility analytics. This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks, including accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies, highlights the growing use of deep learning architectures, and examines emerging techniques such as knowledge graphs, federated learning, and explainable AI that support equity-relevant insights across diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance, which currently constrain the inclusiveness and robustness of GeoAI applications in urban mobility research. To address these challenges, the paper proposes a structured research roadmap linking these gaps to concrete methodological and governance directions including equity-aware loss functions, adaptive multimodal fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems.

Studies show the usefulness of public companies' annual reports has been consistently declining. In the past, financial metrics disclosed in regulated financial statements, such as a company’s book value or operating income (“GAAP metrics”) provided a meaningful explanation of stock prices and thus allowed for an efficient market-based allocation of capital among publicly-traded firms, as well as the economy at-large. Disturbingly, GAAP metrics no longer provide a strong correlation with stock prices, nor with the economy at-large: while enjoying a booming economy, nearly half of all U.S. public companies reported losses in their pre-Covid financial statements. Meanwhile, publicly traded companies have been increasing disclosures of alternative metrics, i.e., newly-created adjustments to the GAAP metrics (“non-GAAPs”) appearing in press releases and in formats other than audited statements thereby exacerbating the potential for opportunistic and misleading reporting by managers. The reporting and content of non-GAAPs are carried out at the discretion of management alone, do not follow any consensually binding practices and are not a result of negotiations with stakeholders. Not surprisingly, findings show managers opportunistically disclose non-GAAP earnings to conceal reported losses and to meet or beat analysts’ expectations. When faced with deficiencies in the regulated financial disclosure, private parties are able to negotiate alternative novel metrics that substitute the use of GAAP metrics in contractual arrangements. Executives of publicly traded firms act alike, and use tailor-made financial indicators in compensation schemes. Investors in publicly traded companies, however, do not enjoy similar privileges, cannot negotiate financial metrics disclosed, and remain bound to a flawed generic financial disclosure regime. Juxtaposing private parties’ negotiation over the financial metrics used in voluntary contracts with the limitation investors in public companies face, this article proposes a novel approach for financial disclosure regulation—a Voting on Reporting regime under which companies, after gaining the consent of their shareholders, are allowed to report alternative but audited financial metrics that replace GAAP metrics and better fit their shareholders’ information needs. At present, financial metrics disclosed by publicly traded companies are either dictated by the regulator (GAAP) or opportunistically used and governed by managers alone (non-GAAPs). By allowing shareholders to participate in devising and regulating a company’s financial reporting, the new regime could kill two birds with one stone: (i) it would allow financial statements to better cater to investors’ interests and information needs; and (ii) it would curtail managers' opportunistic reporting of non-GAAPs.

Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality expose mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of AI with geographic information science, together with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by combining heterogeneous sources such as satellite imagery, GPS traces, transit records, volunteered geographic information, and social sensing into scalable, high-resolution urban mobility analytics. The main aim of this paper is presenting a systematical review of recent GeoAI studies that fuse multiple geospatial modalities for key tasks such as accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion, highlights the growing use of deep learning architectures, and examines emerging techniques including knowledge graphs, federated learning, and explainable AI shape equity-relevant insights in diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance that constrain the inclusiveness and robustness of current GeoAI practices, and proposes a structured research roadmap linking these gaps to actual methodological and governance directions—such as equity-aware loss functions, adaptive fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts—to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems.

While intended to make up for more than a decade of underfunding, the Inflation Reduction Act raises serious concerns regarding the use of nearly $80 billion in fresh funding it provides to the Internal Revenue Service (Service). In reacting to core concerns raised regarding tax administration responsibilities, this article discusses the contemporary United States tax collection system and provides analysis and policy recommendations regarding the desired distribution of tax administration responsibilities between the government and the private sector.

A fundamental element in corporate governance research is the agency problem: in dispersed ownership markets, conflicts arise between shareholders and managers; in concentrated ownership markets, it more commonly arises between controlling and minority shareholders. Although institutional investors can monitor management and potentially protect minority shareholders, their size, market centrality, and role as agents managing others’ capital generate their own agency concerns. In response, scholars and regulators have increasingly envisioned retail investors as an additional counterweight governance mechanism vis-à-vis institutional investors. While majority-of-the-minority rules were not enacted specifically for their benefit, they have enhanced the formal weight of minority votes and thus indirectly strengthened retail investors’ potential role. Participation costs have likewise been reduced, including through the adoption of online voting, to incentivize and facilitate retail investor participation and enhance its impact on corporate governance. This Article challenges the expectations that regulators and scholars have placed on retail investors. First, it identifies the conditions necessary for meaningful retail monitoring and demonstrates that they are not realized in practice: the participation rate of retail investors is low; it remains largely homogeneous (and low) across different subjects, contrary to expectations that it would increase for more important matters or in cases where retail voting becomes pivotal; and it appears to be driven more by the number of items on the agenda than by their substantive importance. Second, when examined in relation to institutional investors, a concerning picture emerges. Rather than constituting a counterweight governance mechanism vis-à-vis institutional investors, retail shareholders reduce their participation as institutional investors increase their holdings and influence. Thus, instead of constituting a counter mechanism to the institutional agency problem, retail investors effectively “clear the stage” for the growing dominance of the institutional voice. All in all, this Article suggests that reforms aimed at empowering minority shareholders do not necessarily yield a more prominent retail voice; rather, they may predominantly benefit institutional investors.