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Accounting as a technology of neoliberalism: The accountability role of IPSAS in Nigeria
حسابداری به عنوان فناوری نئولیبرالیسم: نقش پاسخگویی IPSAS در نیجریه-2021 This paper critically examines the implications for Nigeria’s indebtedness of neoliberalism
as a neo-colonial dependency concept and International Public Sector Accounting
Standards (IPSAS) as a technology of a new form of economic imperialism. As Nigeria’s
huge oil and gas revenues continue to be lost to corruption, the country relies on loans
from Paris Club countries and International Financial Institutions (IFIs), notably the
World Bank. In 1999, when the country changed from military to democratic
governance, Nigeria’s debt to the Paris Club and the World Bank was $30bn. With
pressure from the Paris Club and the World Bank to repay its debts, the new democratic
Nigerian government sought debt forgiveness and rescheduling. Although the World
Bank, representing the creditors in debt negotiation, does not go into specific accounting
standards to be adopted by debtor nations, the Bank does require Nigeria to embrace
neoliberal economic reforms (including public sector reporting framework that produces
consistently relevant and reliable financial information – which denotes IPSAS). Despite
the partial debt forgiveness, repayment of the balance of the debt and adoption of IPSAS,
Nigeria remains endemically corrupt, relies on loans from powerful nations and IFIs, and
has again become debt-laden. Contrary to neoliberal assumptions therefore, we provide
the evidence that better accounting may not necessarily be a panacea for economic
development.
keywords: نئولیبرالیسم | موسسات مالی بین المللی (IFIS) | حسابداری بین المللی بخش دولتی | استانداردها (خود) | فساد | شفافیت و پاسخگویی | Neoliberalism | International Financial Institutions (IFIs) | International Public Sector Accounting | Standards (IPSAS) | Corruption | Transparency and Accountability |
مقاله انگلیسی |
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Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information
شفافیت و پاسخگویی در پشتیبانی تصمیم گیری هوش مصنوعی : توضیح و تجسم شبکه های عصبی کانولوشن برای اطلاعات متن-2020 Proliferating applications of deep learning, along with the prevalence of large-scale text datasets, have revolutionized
the natural language processing (NLP) field, thereby driving the recent explosive growth.
Nevertheless, it is argued that state-of-the-art studies focus excessively on producing quantitative performances
superior to existing models, by playing “the Kaggle game.” Hence, the field requires more effort in solving new
problems and proposing novel approaches and architectures. We claim that one of the promising and constructive
efforts would be to design transparent and accountable artificial intelligence (AI) systems for text
analytics. By doing so, we can enhance the applicability and problem-solving capacity of the system for realworld
decision support. It is widely accepted that deep learning models demonstrate remarkable performances
compared to existing algorithms. However, they are often criticized for being less interpretable, i.e., the “black
box.” In such cases, users tend to hesitate to utilize them for decision-making, especially in crucial tasks. Such
complexity obstructs transparency and accountability of the overall system, potentially debilitating the deployment
of decision support systems powered by AI. Furthermore, recent regulations are emphasizing fairness
and transparency in algorithms to a greater extent, turning explanations more compulsory than voluntary. Thus,
to enhance the transparency and accountability of the decision support system and preserve the capacity to
model complex text data at the same time, we propose the Explaining and Visualizing Convolutional neural networks
for Text information (EVCT) framework. By adopting and ameliorating cutting-edge methods in NLP and
image processing, the EVCT framework provides a human-interpretable solution to the problem of text classification
while minimizing information loss. Experimental results with large-scale, real-world datasets show that
EVCT performs comparably to benchmark models, including widely used deep learning models. In addition, we
provide instances of human-interpretable and relevant visualized explanations obtained from applying EVCT to
the dataset and possible applications for real-world decision support. Keywords: Convolutional neural network | Machine learning interpretability | Class activation mapping | Explainable artificial intelligence |
مقاله انگلیسی |