A scholarly article that Investigations and Transparent use in Explainable AI.

Automation is crucial for dealing with the increasing amount of digital evidence. However, without a clear foundation that covers a definition, classification, and common terminology, there are different views of what automation entails. This creates a chaotic situation: some consider keyword searches or file carving as automation while others do not. We, therefore, reviewed automation literature in digital forensics and other fields, interviewed three practitioners, and discussed the topic with experts from academia. The researchers propose a definition and then present several aspects of automation for digital forensics. Researchers conclude by saying that these basic discussions are necessary to advance and improve discipline through a shared understanding (Michelet, 2023).

The academic paper chose for the case study examination presented in Unit 2 Individual Project is titled, “Automation for digital forensics: Towards a definition for the community” (Michelet,2023).  The problem description of this paper is profound in the improvement of research tools in digital forensics, there has been limited discussion on the use of automated approaches. The intriguing aspects include the use of Artificial Intelligence (AI) used in a very difficult cybersecurity domain of digital forensics, and more often computer forensics. Key concepts are understood in the community, however without well-defined approaches the results lead to different judgments between practitioners. 

Whild a few tasks of automation are simplistic; others are more complex technologies the rely on AI. Computer forensics and digital forensics is not a new discipline with researchers recovering delete files and search for logon activities. The forensic processes have been progressing slowly as researchers are required to create evidence acceptable in a court of law (Computer, 2023). If an investigator cannot explain how an AI tools operates then there is a breach of transparency.

Analyze the case study or article, noting at least three characteristics that illustrate the principle.

The first characteristics of the Unit 2 IP focus highlights that researchers impact on real world applications.  AI is used to focus on crucial digital forensic data and the proliferation of digital devices with the ever-increasing data amounts generated. The use of AI does help to somewhat level the playing field for investigation. However, the inclusion of AI as a marketing term neuters the automation effects. If the digital forensic solution does not focus with Machine Learning (ML) complexity the AI is very light weight.  A related characteristics of the use of AI forensic investigation revolves around the Chain of Custody (COC) (Wilson-Kovacs, 2021)). With AI Explainable AI the may entre doubt in the COC is not maintained, and the digital evidence may not be acceptable in a court of law. These experimental designs that lead to accuracy in a lab may not translate sufficiently for a Court of Law.

A second characteristic that illustrates the principle is Explainable AI (XAI) approaches used to help understand the ML based models.  Revealing that models predict based on related features instead of novel characteristics of malicious behaviors. In addition, the model’s prediction may trigger based on genuine benign behaviors. The use of XAI approaches aim to make the machine learning models more transparent.  The Explainable component of AI is to allow research to explain the algorithm which is much different that use of a “black box”.  The understanding of XAI should help to provides tools that methods that are interpretable, support Trust and Reliability.  XAI helps to mitigate bias in AIM models and support Regulatory compliance. “On June 16, 2022, FTC issued a report to Congress concerning recommendations on “reasonable policies, practices, and procedures” for such AI uses and on legislation to “advance the adoption and use of AI for these purposes.” (Piper, 2022).”

A third characteristic used of illustrate the principle of XAI approaches is the Temporal Inconsistencies in the sample.  The use of Artificial Intelligence is to be training the dataset may lead to over-optimistic classification performance.  This might be revealed with models classify malware that should be based on malicious behaviors and not temporal differences.  Temporal sample inconsistency is relative to the machine learning models show over-optimistic performance because of timing aspect of data samples (Tursunalieva, 2024)

Find additional references to support or provide counter arguments for your inquiry. These may include videos, conference proceedings, discussions by scholars, and podcasts.

One of the professional conference videos addresses the future of automation in digital forensics.  The conference is the Techno Security & Digital Forensics Conference of 2018.  It is a little dated but does provide coverage of the basic adoption challenges for AI implementation in digital forensics. This presentation reports on the issues of processing speed and how automation can be applied today and future projection of digital forensic platforms. (Future of Automation in Digital Forensics – Techno Security & Digital Forensics Conference 2018.)

Another scholarly written article focuses on a comprehensive overview of AI in use in digital investigations. This study used a comprehensive approach that combined qualitative, descriptive, and analytical methods, based mainly on various legal documents and academic literature. The study showed how AI can play a crucial role in law enforcement, by looking at areas such as arrest methods, release choices, sentencing steps, recidivism estimation, crime identification and assessment, and suspect tracking through sophisticated audio analysis techniques. The study shows how machine learning methods can help to enhance the examination and management of case data. The study provides several suggestions to make the best use of AI in digital criminal investigations. These suggestions propose the focus on high-risk cases by using various data sources to support informed decision-making. Furthermore, the study proposes the use of AI in crime forecasting, suspect recognition, and the improvement of security measures (Faqir, 2023).

In the interest of justice or clarity, many legal agencies use Artificial Intelligence (AI techniques to help them fight cybercrimes. AI tools such as computational intelligence, neural networks, artificial immune systems, machine learning, data mining, pattern recognition, fuzzy logic, and heuristics are very useful in cybercrime prevention and detection. However, these tools exists as individual utilities and are not often associated with Artificial Intelligence.  

Assess the impact of explainable AI on the industry in your example. What are the problems associated with explainable AI?

To better asse the impact of explainable AI in the industry of digital forensics this paper breaks down the challenges for both explainable AI (XAI) in table 1 and Digital Forensics challenges in table 2.

Table 1

Explainable AI (XAI) Challenges

Issue	Description
Hidden Processes	AI systems, black box, especially deep learning models, often have hidden processes that are not clear, even to the creators.
Prone to Manipulation	These models can be manipulated by malicious attacks and can show prejudices based on race, gender, and other aspects of identity.
Conflicting Views	There are different views on what explainability should accomplish, resulting in different emphasis on various stakeholder goals.
Lack of Clarity	The absence of clarity in AI decision-making processes can cause issues, especially in critical areas such as healthcare, lending, and criminal justice.

Table 2

Digital Forensics challenges

Issue	Description
Data Volume	Every investigation has more and more data, which makes it harder to finish them quickly and efficiently.
Cloud and Encryption Forensics	Investigators face difficulties with cloud and encryption forensics in particular.
Multiple Devices	Cases where there is evidence on more than one device are more complicated to investigate.
Training and Resources Shortage	There is a worry about not having enough training and resources for digital forensics.

A range of software and methods that AI technology can help to find crucial digital evidence, such as emails, chat logs, and metadata. This field of digital investigation with AI support depends on diversity, using sophisticated tools to ensure accuracy and completeness in dealing with different types of digital evidence that are credible and admissible in court. However, these advancements also bring ethical and legal responsibilities. Chain of Custody (COC is maintaining the quality of evidence, respecting privacy, and ensuring fairness are vital.

The digital applications that use AI assistance require processing huge amounts of data from different sources, such as electronic devices, network records, mobile data, and social media content. They need to use different technologies, such as artificial intelligence, text recognition, and statistical analysis, to get and show digital evidence for legal purposes (Faqir, 2023).

Summary of the problems of Explainable AI (XAI) as it relates to Digital Forensics

Digital forensic experts identified the limitations of IT skills in AI and automation. The areas to skill-up include locating and extracting data, recognizing devices, analyzing network traffic, forensics, coded data and using specific tools for programming. Artificial Intelligence is relevant to many of the tasks for digital forensic examiners. The applications used for evidence recovery vary for the different types of criminal acts. Explainable AI (XAI) can assist digital forensic examiners to classify images, maintain their source and reduce the exposure to disturbing images in these frames. Another option is to view the similarity between the image and the score without displaying the image or using the default image description generated by the XAI and ML algorithm (Abdul, 2023).

XAI may have limited usefulness for law enforcement in preliminary investigations. Traditional criminal forensics are becoming more AI technology based.  Police officers that are digital forensic investigators claim that they were neutral and unbiased in principle, but most of digital forensics’ analysis indicated that the suspect had been arrested and convicted. In the supporting papers the participants suggested the forensic analysis method to identify who was communicating for suspicious communication, and to produce and assess the evidence later.

In this case, the investigator expects the digital forensics expert to extract the data from the relevant devices and submit it to the court in a reportable format, so XAI can perform this operation automatically. It is a real challenge to integrate XAI into digital forensic space, but XAI offers the potential of tackling old problems that are increasingly difficult to solve. The growing amount of data required for analysis, the complexity of IT crimes and the diversity of evidence delay the delivery of valuable digital forensic information, relies on data processing, and consume many resources (Abdul, 2023).

References

Abdul, R. J., Ahmed, W., Pandya, S., Praveen Kumar, R. M., Alazab, M., & Thippa, R. G. (2023). A Survey of Explainable Artificial Intelligence for Smart Cities. Electronics, 12(4), 1020. https://doi.org/10.3390/electronics12041020

Computer, E. (2023). Digital Forensics and Fraud Investigations: Techniques and Best Practices. Express Computer, https://coloradotech.idm.oclc.org/login?url=https://www.proquest.com/trade-journals/digital-forensics-fraud-investigations-techniques/docview/2805260715/se-2

Faqir, R. S. A. (2023). Digital Criminal Investigations in the Era of Artificial Intelligence: A Comprehensive Overview. International Journal of Cyber Criminology, 17(2), 77-94. https://doi.org/10.5281/zenodo.4766706

Future of Automation in Digital Forensics – Techno Security & Digital Forensics Conference 2018, https://www.youtube.com/watch?v=6LFWOj3EpsQ

Michelet, G., Breitinger, F., & Horsman, G. (2023). Automation for digital forensics: Towards a definition for the community. Forensic Science International (Online), 349https://doi.org/10.1016/j.forsciint.2023.111769

Piper, D. (2022). FTC REPORT ON AI: COMBATING ONLINE HARMS THROUGH INNOVATION. Journal of Internet Law, 26(1), 1-16. https://coloradotech.idm.oclc.org/login?url=https://www.proquest.com/trade-journals/ftc-report-on-ai-combating-online-harms-through/docview/2705455707/se-2

Tursunalieva, A., Alexander, D. L. J., Dunne, R., Li, J., Riera, L., & Zhao, Y. (2024). Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications. Applied Sciences, 14(2), 496. https://doi.org/10.3390/app14020496

Wilson-Kovacs, D. (2021). Digital media investigators: challenges and opportunities in the use of digital forensics in police investigations in England and Wales. [Use of digital forensics] P