The benefits of AI and machine learning do not present themselves without risks. From the trustworthy use of AI and machine learning to the ability to explain the workings of the algorithms to the amplified risk of propagating bias in decision making, internal and external stakeholders are rightly concerned. However,
It is often said that cooperation is key to addressing big, intractable problems. The European Union recently highlighted this with amendments to the Administrative Tax Cooperation Directive. These amendments are designed to improve cooperation between tax authorities on administrative tax and reduce tax evasion and tax fraud.[1] Increasing information exchange
To find exact duplicates, matching all string pairs is the simplest approach, but it is not a very efficient or sufficient technique. Using the MD5 or SHA-1 hash algorithms can get us a correct outcome with a faster speed, yet near-duplicates would still not be on the radar. Text similarity is useful for finding files that look alike. There are various approaches to this and each of them has its own way to define documents that are considered duplicates. Furthermore, the definition of duplicate documents has implications for the type of processing and the results produced. Below are some of the options. Using SAS Visual Text Analytics, you can customize and accomplish this task during your corpus analysis journey either with Python SWAT package or with PROC SQL in SAS.
