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Corruption is sustained by powerful male networks, reinforcing women’s exclusion from politics. Yet, contrary to this conventional wisdom, we theorize that corruption can sometimes increase women’s access to power. Since women are often perceived as “cleaner” than men, where institutions allow heads of government to be held accountable on economic issues, chief executives may use women’s inclusion in high-profile posts to signal that they are curbing the abuse of public office for private gain. Examining upward of 150 countries over 16 years, we investigate whether and where corruption is linked to the presence of women finance ministers—a high-profile post capable of quelling economic malfeasance. We show that increases in corruption bolster women’s presence, particularly in countries with free and fair elections and presidential systems. That our results hold only in contexts of high accountability suggests this relationship is not endogenous but reflects chief executives’ efforts to preempt punishment.
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Campaign Contributions, Independent Expenditures, and the Appearance of Corruption: Public Opinion vs. the Supreme Court's Assumptions
We use survey experiments to test the validity of judicial assumptions underlying campaign finance regulation. Our evidence supports the key assumption that ‘‘appearance of corruption’’ is directly related to the monetary value of campaign contributions. Contrary to the Court’s reasoning in Buckley v. Valeo and Citizens United v. FEC, independent expenditures are more likely to elicit the appearance of corruption than direct contributions, and direct contributions well below the legal limit also create the appearance of corruption. Our findings therefore call into question key legal tenets underlying campaign finance regulation and suggest that the amounts raised by virtually every federal election campaign exceed the thresh-old required to elicit widespread public perceptions of corruption.
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- PAR ID:
- 10257278
- Date Published:
- Journal Name:
- Election Law Journal: Rules, Politics, and Policy
- ISSN:
- 1533-1296
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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In this position paper, we introduce a new epistemic lens for analyzing algorithmic harm. We argue that the epistemic lens we propose herein has two key contributions to help reframe and address some of the assumptions underlying inquiries into algorithmic fairness. First, we argue that using the framework of epistemic injustice helps to identify the root causes of harms currently framed as instances of representational harm. We suggest that the epistemic lens offers a theoretical foundation for expanding approaches to algorithmic fairness in order to address a wider range of harms not recognized by existing technical or legal definitions. Second, we argue that the epistemic lens helps to identify the epistemic goals of inquiries into algorithmic fairness. There are two distinct contexts within which we examine algorithmic harm: at times, we seek to understand and describe the world as it is, and, at other times, we seek to build a more just future. The epistemic lens can serve to direct our attention to the epistemic frameworks that shape our interpretations of the world as it is and the ways we envision possible futures. Clarity with respect to which epistemic context is relevant in a given inquiry can further help inform choices among the different ways of measuring and addressing algorithmic harms. We introduce this framework with the goal of initiating new research directions bridging philosophical, legal, and technical approaches to understanding and mitigating algorithmic harms.more » « less
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Abstract PurposeThe COVID-19 pandemic and associated public health control measures resulted in both higher rates of intimate partner abuse and violence (IPA/V) and more severe victimization. Domestic violence advocacy programs struggled to maintain organizational capacity to provide survivor-centered services in the face of both increased demand and rapid changes necessary to mitigate disease spread. The current study explores ways that legal advocates and the legal systems responded to the needs of IPA/V survivors. MethodsLeaders of 25 state and territory Coalitions across the U.S. participated in the study. Semi-structured interview questions were based on rapidly emerging areas of concern and drew on possible strengths and weaknesses in direct service provision during the height of the COVID-19 pandemic, including questions about public health control measures, service provision, gaps in services, and lessons learned. ResultsInterviews with Coalition leaders revealed gaps in legal system responses during the pandemic, but also suggested new directions for service delivery. Four main themes emerged: lack of access to the legal system, limitations of in-person legal system responses, limitations of virtual legal system responses, and changes needed moving forward. ConclusionBacklogs in case processing communicate to survivors and the larger community that responding to IPA/V is not urgent. Advocates faced difficulty supporting survivors in person while virtual hearings sometimes presented other challenges for advocacy. However, some changes, including innovative online services and broad resolve to center BIPOC survivor voices, have the potential to enhance safety for survivors and push the movement forward.more » « less
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Rank aggregation from pairwise preferences has widespread applications in recommendation systems and information retrieval. Given the enormous economic and societal impact of these applications, and the consequent incentives for malicious players to manipulate ranking outcomes in their favor, an important challenge is to make rank aggregation algorithms robust to adversarial manipulations in data. In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons. We consider a setting where pairwise comparisons are initially generated according to a BTL model, but a fraction of these comparisons are corrupted by an adversary prior to being reported to us. We consider a strong contamination model, where an adversary having complete knowledge of the initial truthful data and the underlying true BTL parameters, can subsequently corrupt the truthful data by inserting, deleting, or changing data points. The goal is to estimate the true score/weight of each item under the BTL model, even in the presence of these corruptions. We characterize the extent of adversarial corruption under which the true BTL parameters are uniquely identifiable. We also provide a novel pruning algorithm that provably cleans the data of adversarial corruption under reasonable conditions on data generation and corruption. We corroborate our theory with experiments on both synthetic as well as real data showing that previous algorithms are vulnerable to even small amounts of corruption, whereas our algorithm can clean a reasonably high amount of corruption.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1089/elj.2019.0610
