با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
هوش مصنوعی - Artificial intelligence
سال انتشار:
2020
عنوان انگلیسی مقاله:
Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information
ترجمه فارسی عنوان مقاله:
شفافیت و پاسخگویی در پشتیبانی تصمیم گیری هوش مصنوعی : توضیح و تجسم شبکه های عصبی کانولوشن برای اطلاعات متن
منبع:
Sciencedirect - Elsevier - Decision Support Systems, 134 (2020) 113302. doi:10.1016/j.dss.2020.113302
نویسنده:
Buomsoo Kima, Jinsoo Parkb, Jihae Suhc,⁎
چکیده انگلیسی:
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
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0