Proxy (statistics)

About: Proxy (statistics) is a research topic. Over the lifetime, 5257 publications have been published within this topic receiving 94504 citations. The topic is also known as: proxy variable & proxy measurement.

Are family members suitable proxies for transitional care unit residents when collecting satisfaction information

Abstract: Objective. To examine the agreement and association of elders’ responses with family member proxy responses using the same, previously validated satisfaction instrument on both groups of respondents. Methods. Satisfaction data came from transitional care unit residents and family members (N = 462 paired responses) from one facility and were collected between 1999 and 2000. The satisfaction questionnaire consisted of 17 items evaluating the art of care, technical quality, efficacy, amenities of the care environment, and global satisfaction. Bias indexes and intraclass correlation coefficients were used to examine the satisfaction scores. Results. In general, proxy satisfaction ratings were higher than ratings of residents. The results also show that proxy ratings varied less from resident ratings for the amenity items, which were considered the most concrete items. Proxy ratings were much higher for the art of care and efficacy domain items, which were considered the least concrete items. Conclusion. The results of this investigation show that proxy ratings do not necessarily substitute for resident ratings.

Efficient proxy signatures based on trapdoor hash functions

Abstract: Proxy signatures have found extensive use in authenticating agents acting on behalf of users in applications such as grid computing, communications systems, personal digital assistants, information management and e-commerce. Importance of proxy signatures has been repeatedly highlighted by applied cryptographers through different variations, namely threshold proxy signatures, blind proxy signatures and so forth. Unfortunately, most recent constructions of proxy signatures only improve on minor weaknesses of previously built schemes, and most often do not deliver formal security guarantees. In this study, the authors propose a technique to construct provably secure proxy signature schemes using trapdoor hash functions that can be used to authenticate and authorise agents acting on behalf of users in agent-based computing systems. They demonstrate the effectiveness of their approach for creating practical instances by constructing a discrete log-based instantiation of the proposed generic technique that achieves superior performance in terms of verification overhead and signature size compared with existing proxy signature schemes. Formal definitions, security specifications and a detailed theoretical analysis, including correctness, security and performance, of the proposed proxy signature scheme have been provided.

Proxy Discrimination in the Age of Artificial Intelligence and Big Data

Abstract: Big data and Artificial Intelligence (“AI”) are revolutionizing the ways in which firms, governments, and employers classify individuals. Surprisingly, however, one of the most important threats to antidiscrimination regimes posed by this revolution is largely unexplored or misunderstood in the extant literature. This is the risk that modern algorithms will result in “proxy discrimination.” Proxy discrimination is a particularly pernicious subset of disparate impact. Like all forms of disparate impact, it involves a facially-neutral practice that disproportionately harms members of a protected class. But a practice producing a disparate impact only amounts to proxy discrimination when the usefulness to the discriminator of the facially-neutral practice derives, at least in part, from the very fact that it produces a disparate impact. Historically, this occurred when a firm intentionally sought to discriminate against members of a protected class by relying on a proxy for class membership, such as zip code. However, proxy discrimination need not be intentional when membership in a protected class is predictive of a discriminator’s facially-neutral goal, making discrimination “rational.” In these cases, firms may unwittingly proxy discriminate, knowing only that a facially-neutral practice produces desirable outcomes. This Article argues that AI and big data are game changers when it comes to this risk of unintentional, but “rational,” proxy discrimination. AIs armed with big data are inherently structured to engage in proxy discrimination whenever they are deprived of information about membership in a legally-suspect class whose predictive power cannot be measured more directly by non-suspect data available to the AI. Simply denying AIs access to the most intuitive proxies for such predictive but suspect characteristics does little to thwart this process; instead it simply causes AIs to locate less intuitive proxies. For these reasons, as AIs become even smarter and big data becomes even bigger, proxy discrimination will represent an increasingly fundamental challenge to anti-discrimination regimes that seek to limit discrimination based on protected traits that often happen to be directly predictive characteristics. Numerous anti-discrimination regimes do just that, limiting discrimination based on potentially predictive factors like preexisting conditions, genetics, disability, sex, and even race. This Article offers a menu of potential strategies for combatting this risk of proxy discrimination by AI, including prohibiting the use of non-approved types of discrimination, mandating the collection and disclosure of data about impacted individuals’ membership in legally protected classes, and requiring firms to employ statistical models that isolate only the predictive power of non-suspect variables.