The role of evaluations in reaching decisions using automated systems supporting forensic analysis

Bollé, Timothy; Casey, Eoghan; Jacquet, Maëlig

doi:10.1016/j.fsidi.2020.301016

Cited by 7 publications

(21 citation statements)

References 14 publications

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“…More details about the characteristics of the included studies are shown in Appendix 2. The included studies utilized AI for fraud detection (n=17) [1,2,6,8,14,17,18,19,21,22,23,24,26,27,28,30,31], identifying and classifying detected fraud (n=8) [3,4,11,12,13,15,20,25], and investigating and analyzing fraudulent data (n=6) [5,7,9,10,16,29]. The most common algorithm used in the included studies was Convolutional Neural Network (CNN) (n=13), followed by Artificial Neural Network (ANN) (n=10).…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

Iqbal

Abd‐Alrazaq

Househ

2022

Studies in Health Technology and Informatics

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Over the past decade, Artificial Intelligence (AI) technologies have quickly become implemented in protecting data, including detecting fraud in healthcare organizations. This scoping review aims to explore AI solutions utilized in fraud detection occurring in treatment settings. To find relevant literature, PubMed and Google Scholar were searched. Out of 183 retrieved studies, 31 met all inclusion criteria. This review found that AI has been used to detect different types of fraud such as identify theft and kickbacks in healthcare. Additionally, this review discusses how AI techniques used in network mapping fraud can detect and visualize the hacker’s network. A proper system must be implemented in healthcare settings for successful fraud detection, which may overall improve the healthcare system.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

Iqbal

Abd‐Alrazaq

Househ

2022

Studies in Health Technology and Informatics

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“…Numerous studies in DF have revealed that forensic practitioners frequently issue inconsistent or biased results [13,14]. In addition, the majority of AI-based approaches lack the necessary clarity and replicability to allow investigators to assess the accuracy of their output [15]. Thus, a forensically sound process 2 , is one that integrates automated investigative analysis-evaluated through scientific (accuracy and precision) metricswith human assessments of the outcome.…”

Section: Methods For Evaluating Dfai Techniquesmentioning

confidence: 99%

“…Depending on the design, a robust AIbased system can uncover various heretofore unrecognized clues. If these new revelations (even though relevant) are not properly analyzed and evaluated, they may lead investigators to believing that the outputs dependably fulfil their needs [15]. As a result, an extensive review of the output of DFAI will be required (supposedly provided by human experts) to arrive at a factually correct conclusion.…”

Section: Methods For Evaluating Dfai Techniquesmentioning

confidence: 99%

“…As with the output of any other forensic tool capable of extracting and analyzing evidence from digital artifacts, which frequently requires additional review and interpretation that are compatible with the working hypothesis, the results of forensic examinations conducted using DFAI should be viewed as "recommendations" that must be interpreted in the context of the overall forensic observation and investigation [15]. In addition, the evaluation apparatus must be verifiable, appropriate for the task it seeks to solve, and compatible with the other contextual analysis of the investigative model.…”

Section: Methods For Evaluating Dfai Techniquesmentioning

confidence: 99%

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Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

Solanke

Biasiotti²

2022

Künstl Intell

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The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process.

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“…Another study published design requirements for automated forensic tools. The work also provides a hierarchy for evaluating an automated forensics system [ 41 ].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Arshad

Abdullah

Alawida

et al. 2022

Sensors

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Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

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The role of evaluations in reaching decisions using automated systems supporting forensic analysis

Cited by 7 publications

References 14 publications

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

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