• Korean Journal of Weed Science
• /
• v.12 no.2
• /
• pp.124-131
• /
• 1992

Not only reducing the carry-over effects of quinclorac [3, 7-dichloro-8-quinoline carboxylic acid] used in paddy field to some following vegetable crops but also rationalizing agro-ecology conservation and farm economy, the reducing feasibility of application rates by various cropping patterns and application timing after rice seeding and transplanting. Four cropping patterns namely dry direct seeding(DDS), flooded direct seed(FDS), transplanting of 8 days old early seedlings(EST) and 25 days old machinery seedling(MST) were experimented with 7 application timings as 0, 5, 10, 15, 20, 25, 30 days after seeding/transplanting and 9 levels of application rates as 0, 75, 150, 225, 300, 375, 450, 525, and 600g ai/ha of the chemical, respectively. Within the maximum permitted limit of rice phytotoxicity, the minimum application rate of quinclorac to complete control of Echinochloa crus-galli as influenced by various cropping patterns with application timing could be evaluated as follows : A. Dry direct seeding : The minimized application rate at application timing upto 10 days after seeding (DAS) was counted 150g ai/ha, and delaying upto 15-30 DAS, the rates were increased upto 225-525g ai/ha. B. Flooded direct seeding and transplanting : The application rates were minimized 75g ai/ha at application timing upto 10 days after seeding/transplanting(DAS/T), 150g ai/haupto 15 DAS/T, and 225g ai/ha at later than 20 DAS/T, respectively.

• Journal of Intelligence and Information Systems
• /
• v.28 no.3
• /
• pp.259-278
• /
• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.