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The proliferation of software tools and automated techniques in digital forensics has brought about some controversies regarding bias and fairness. Different biases exist and have been proven in some civil and criminal cases. In our research, we analyze and discuss these biases present in software tools and automation systems used by law enforcement organizations and in court proceedings. Furthermore, we present real-life cases and scenarios where some of these biases have determined or influenced these cases. We were also able to provide recommendations for reducing bias in software tools, which we hope will be the foundation for a framework that reduces or eliminates bias from software tools used in digital forensics. In conclusion, we anticipate that this research can help increase validation in digital forensics software tools and ensure users' trust in the tools and automation techniques.

 

Money laundering using cryptocurrencies has become increasingly prevalent, and global and national regulatory authorities have announced plans to implement stringent anti-money laundering regulations. In this paper, we examine current anti-money laundering (AML) mechanisms in cryptocurrencies and payment networks from a technical and policy perspective, and point out practical challenges in implementing and enforcing them. We first discuss blacklisting, a recently proposed technique to combat money laundering, which seems appealing, but leaves several unanswered questions and challenges with regard to its enforcement. We then discuss payment networks and find that there are unique problems in the payment network domain that might require custom-designed AML solutions, as opposed to general cryptocurrency AML techniques. Finally, we examine the regulatory guidelines and recommendations as laid out by the global Financial Action Task Force (FATF), and the U.S. based Financial Crimes Enforcement Network (FinCEN), and find that there are several ambiguities in their interpretation and implementation. To quantify the effects of money laundering, we conduct experiments on real-world transaction datasets. Our goal in this paper is to survey the landscape of existing AML mechanisms, and focus the attention of the research community on this issue. Our findings indicate the community must endeavor to treat AML regulations and technical methods as an integral part of the systems they build and must strive to design solutions from the ground up that respect AML regulatory frameworks. We hope that this paper will serve as a point of reference for researchers that wish to build systems with AML mechanisms, and will help them understand the challenges that lie ahead.