Search results

This study aims to measure the impact of the Non-Cooperative Countries and Territories, Organization for Economic Cooperation and Development and US PATRIOT Act Section 311 blacklists on external deposits from blacklisted jurisdictions into BIS reporting countries in 1996–2008, a period when anti-money laundering-related actions were consistently less stringent than post-2010, to see whether they had an effect even absent the threat of sizable financial fines.

The study uses descriptive statistics and bivariate and multivariate regressions to analyze the probable impact from blacklists on non-bank external deposits. The country sample is divided into offshore financial centers (OFCs) and non-OFCs and includes 158 non-listed countries. The impact of the blacklists is tested both jointly and individually for the respective blacklists.

The authors find mixed impact from jurisdictions being blacklisted on the growth rate of stocks of deposits into BIS reporting countries. Effects are often zero, negative in several cases and positive in some cases. This is consistent with the “stigma effect” and the “stigma paradox” in the literature. An overall impact from blacklisting is difficult to discern. Different blacklists had different effects, and the same blacklist impacted countries differently, illustrating the importance of disaggregating the analysis by individual countries.

Interpretation of these data is limited by the absence of comparable data on non-resident deposits in blacklisted jurisdictions.

The impact of a blacklist depends in part on the structure of the listed jurisdictions’ economies, implying that country-specific sanctions may be more effective than blacklists.

This is one of the very few papers to date to rigorously test the impact of blacklists on external deposits.

The presence of Latin in heavy metal music ranges from full texts, intros, song and album titles to band names, pseudonyms, and literary quotations. This chapter sheds light on heavy metal's fascination with the history and ‘arcane’ sound of Latin, and investigates its patterns of use in lyrics with the help of Natural Language Processing tools and digitally-available linguistic resources. First, the authors collected a corpus of lyrics containing differing amounts of Latin and enhanced it with descriptive metadata. Next, the authors calculated the richness of the vocabulary and the distribution of content words. The authors processed the corpus with a morphological analyser and performed both a manual and a computational search for intertextuality, including allusions, paraphrase and verbatim quotations of literary sources. The authors show that, despite it being a dead language, Latin is very frequently used in metal. Its historical status appears to fascinate bands and lends itself well to those religious, epic and mysterious themes so characteristic of the heavy metal world. The widespread use of Latin in metal lyrics, however, sees many bands simply reusing Latin texts – mostly from the Bible – or even misspelling literary quotations.

The purpose of this paper is to explore the challenges of implementing accountable artificial intelligence (AI) systems in India, focusing on the need for algorithms to justify their decisions, especially in subjective and complex scenarios. By analyzing various government projects, documented biases and conducting empirical case studies and experiments, the study highlights the limitations of AI in recognizing the nuances of India’s unique social landscape. It aims to underscore the importance of integrating political philosophy to ensure that AI systems are held accountable within India’s sociopolitical context, urging policymakers to develop frameworks for responsible AI decision-making.

The research adopts a mixed-methods approach to address the five research questions. It begins with an extensive literature review, focusing on AI’s transformative potential, algorithmic bias and accountability in the Indian context. Data is collected from 15 AI use cases in health care, education and public safety, 13 government automated decision tools and five bias cases, including facial recognition and caste-based discrimination. Additionally, ten case studies and three experiments on ChatGPT are analyzed. Content analysis is used to interpret and categorize the data, identifying patterns and themes. Specific case studies and experiments on autocompletion in search engines further support the findings.

The study revealed significant limitations in current AI systems when applied to India’s complex socio-cultural landscape. Analyzing 15 AI applications and 13 government projects, the research identified multiple instances of algorithmic bias. Experiments with Google’s autocomplete and ChatGPT showed that these systems often reinforce social stereotypes and struggle with nuanced, subjective situations. The findings emphasize the accountability gap in AI-driven decisions, highlighting the need for rigorous oversight, particularly in welfare projects where errors could lead to severe consequences. The study recommends developing regulatory frameworks, improving AI design and raising public awareness to address these challenges.

In the context of complex societies like India, a pressing concern arises: who should assume responsibility for the repercussions stemming from algorithmic failures to comprehend subjective complexities? To this end, there exist no serious scholarly works toward which present paper tries to shed new insights. It draws upon insights from the corpus of political philosophy literature, encompassing both classical and contemporary notions of responsibility, and seeks to establish connections between these concepts and the unique sociopolitical structure of India. The work is unique in the focus of the paper and is original in the direction projected.