We use the iWildCam version 2.0 released in 2021 as a Examples of train set images can be seen in Figure 14. Random examples from the out-of-distribution test set. Figure 15 shows examples of train set images. Figure 15: Random examples from the ImageNet ILSVRC 2012 challenge train set [37, 11]. The full training set is notably not class balanced, exhibiting a long-tailed distribution (see Figure 16). Figure 17: Random examples from the iNaturalist 2017 challenge train set [46].

Often, observational data alone is not enough to uniquely identify a system's causal structure.

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While credit is often portrayed as the fuel of development, access to credi t is unevenly distributed -- not merely as a function of income or collateral, but increasingly as a function of data visibility. In this context, the core hypothesis of this paper is that data, when governed ethically and reused efficiently, operates as a re distributive economic asset. The idea that being poor is more expensive is not new; it has been conceptualized as the "poverty premium" -- where low - income individuals pay higher effective prices for credit, insurance, and other services (Carrière - Swallow & Haksar, 2019). Y et what has ch anged is the infrastructure of decision - making: creditworthiness is increasingly determined by algorithmic systems whose inputs are not equitably distributed. Individuals with limited credit histories or fragmented digital footprints remain invisible, not due to financial incapacity, but due to informational exclusion. This asymmetry is not merely a market failure -- it is a structural inequality encoded in data regimes. W e argue that positive credit data -- payment histories, utilization patterns, and account stability -- constitutes a nonrival input that, once generated, can be reused across institutions at near - zero marginal cost without diminishing its value (Jones & Tonetti, 2020; Acemoglu et al., 2023). However, the ability to extract value from such data remains highly uneven. In traditional credit markets, the absence of negative signals penalizes borrowers more than the presence of positive behavior benefits them.

We use the iWildCam version 2.0 released in 2021 as a Examples of train set images can be seen in Figure 14. Random examples from the out-of-distribution test set. Figure 15 shows examples of train set images. Figure 15: Random examples from the ImageNet ILSVRC 2012 challenge train set [37, 11]. The full training set is notably not class balanced, exhibiting a long-tailed distribution (see Figure 16). Figure 17: Random examples from the iNaturalist 2017 challenge train set [46].

Often, observational data alone is not enough to uniquely identify a system's causal structure.