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A Data Mining Approach to Detecting Bias and favoritism in Public Procurement

Intelligent Automation & Soft Computing 2023年第6期36卷 3501-3516页

作者： Yeferson Torres-Berru Vivian F.Lopez-Batista Lorena Conde Zhingre University of Salamanca Salamanca37008Spain Instituto Tecnológico Superior Sudamericano Loja1101608Ecuador Universidad Internacional del Ecuador Loja1101608Ecuador

In a public procurement process,corruption can occur at each stage,favoring a participant with a previous agreement,which can result in over-pricing and purchases of substandard products,as well as gender *** paper’s aim is to detect biased purchases using a Spanish Language corpus,ana-lyzing text from the questions and answers registry platform by applicants in a public procurement process in ***,gender bias is detected,pro-moting both men and women to participate under the same *** order to detect gender bias and favoritism towards certain providers by contracting enti-ties,the study proposes a unique hybrid model that combines Artificial Intelli-gence algorithms and Natural Language Processing(NLP).In the experimental work,303,076 public procurement processes have been analyzed over 10 years(since 2010)with 1,009,739 questions and answers to suppliers and public insti-tutions in each *** bias and favoritism were analyzed using a Word2-vec model with word embedding,as well as sentiment analysis of the questions and answers using the VADER *** 32%of cases(96,984 answers),there was favoritism or gender bias as evidenced by responses from contracting *** proposed model provides accuracy rates of 88% for detecting favor-itism,and 90%for detecting gender *** one-third of the procure-ment processes carried out by the state have indications of corruption and *** Latin America,government corruption is one of the most significant challenges,making the resulting classifier useful for detecting bias and favoritism in public procurement processes.

关键词： favoritism bias natural language processing Word2vec sentiment analysis word embeddings

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Procurement Auctions with Ex-ante Auctioneer-Bidder Collusion and Endogenous Bribery

Procurement Auctions with Ex-ante Auctioneer-Bidder Collusio...

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中国信息经济学会2012学术年会暨第四届博士生论坛

作者： Huang He,Li Zhipeng

We study three-tier government procurement auctions,in which the auctioneer may collude with one ex-ante favored bidder and the government regulates the auctioneer with a Performance-Based Transfer（PBT）.The favored bidder is permitted by the auctioneer to submit two bids simultaneously in exchange for a bribe that is proportional to the difference between the two *** bribe share is the auctioneer’s decision *** the bribe share is given,the favored bidder adopts a piecewise strategy: he bids honestly if his cost is too high（higher than a threshold） to afford the bribe;he bribes and submits two bids if his cost is too low（lower than another threshold）,but in this case he is so efficient that his optimal strategy is too aggressive to appear natural,so he has to adopt a sub-optimal strategy to conceal the corruption;only when his cost is neither too high nor too low（between the two thresholds）,he can make the most of the privilege of submitting two *** two thresholds are decreasing in the bribe share and are well distinguished from similar thresholds in related *** the corruption,the allocation is inefficient with a positive probability,which implies a reduced expected social *** the corruption probability and the inefficiency probability are decreasing in the bribe *** the bribe share is one,our model simply turns into the standard *** the corruption results in a higher expected price,the government can regulate the auctioneer by imposing a ratio of the exceeding price on the auctioneer as a *** reward scheme is referred to as the PBT *** the PBT scheme,the optimal bribe share for the auctioneer is an increasing function of the PBT *** implies that if the PBT ratio exceeds some threshold,the optimal bribe share will be one,which implies no corruption and full efficiency.

关键词： Auctions Ex ante Corruption favoritism Bribe Share Anti-Corruption

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