AI and Law Enforcement
AI and Law Enforcement
Introduction
Artificial Intelligence is currently being used in the criminal justice system through its advanced tools that help in forensic analysis, surveillance, and decision-making. It has become an essential ingredient in criminal justice system and is increasingly being relied upon by American local law enforcement. Despite the potential for AI to enhance efficiency and objectivity in criminal investigations, major ethical and reliability concerns do arise. This report examines the application of AI in forensic technology, addresses debates over how AI might be used either to help solve or contribute to racism in the law, reconsiders public fears of AI, and then analyzes ethical implications and reliability. The report further discusses a case in which AI was used to solve a criminal incident, considering how the case was solved and the impacts on the use of the technology in the field.
The use of forensic technology within the context of artificial intelligence
Forensic technology employing AI has revitalized the means of collection, analysis, and interpretation of evidence. Facial recognition, DNA analysis, prediction of crime patterns, and digital forensics are all now being carried out using different AI systems. These systems can sift through big data sets much quicker and identify those patterns which otherwise would go unnoticed by human investigators. For instance, AI-powered tools such as CompStat help police departments analyze crime trends and effectively deploy resources. Similarly, AI-powered software such as Clearview AI has been used in identifying suspects through facial recognition technology.
Nonetheless, AI’s reliance on big data inputs also brings challenges, the prominent one being biases inherent in the training data. Forensic technologies depend on clean, unbiased datasets to function correctly. According to Gipson Rankin (2021), inaccuracies or manipulated data have the potential to lead to wrongful convictions or overlooked suspects, where the very justice it is designed to support is prejudiced by AI. Still, the report by the RAND Corporation stresses that unregulated use of AI in the police increases the likelihood for ethical breaches and unintended outcomes (Yeung et al., 2021)
The potentialities of AI in forensic sciences are limitless. For example, AI can speed up processes that are quite tiresome for humans to undertake, such as fingerprint analysis or comparisons in handwriting. These automated tools do not only save time but also reduce human error. However, their accuracy would depend on well-trained algorithms. That is why apprehensions about bias and inaccuracies persist. AI learns from the data it is fed, and if that data contains human errors or prejudices, then the system reproduces these flaws at scale.
Arguments for “AI is as likely to contribute to racism in the law as it is a means to end it.”
The two competing arguments in this report are that AI can be used to perpetuate racism or as a tool to combat it. The first argument is that if the AI systems are fed biased historical data, then they are bound to make recommendations with racial biases and may even exaggerate them. Examples of such instances include predictive policing tools, which have been used to stereotype and create injustices within minority communities such as African Americans. As reported by the RAND Corporation, without robust oversight, these systems risk embedding systemic biases into operational decisions, further marginalizing communities of color (Yeung et al., 2021). Conversely, some proponents say AI can reduce human bias by applying the same set of algorithms across decision-making. Conceivably, these algorithms can examine cases based on the facts of a case rather than prejudices, as a result, arriving at a fairer conclusion. For example, AI systems that anonymize inputs prior to analysis cannot engage in racial profiling during investigations.
In this argument, I hold the cautious perspective. Although AI can help fight against racism, it increases the bias in its current applications because of poor data and inadequate oversight. There needs to be continuous monitoring and transparency at the algorithmic design so that AI contributes to bridging rather than widening up the racial gap (Gipson Rankin, 2021). Furthermore, the fact that AI plays a reinforcing role in structural inequalities has to be recognized. The criminal justice system has always had biases against minority groups, especially Black Americans. AI tools also tend to be prejudicial against these groups since they were trained on biased datasets, further increasing the disparity that already exists. Fairness in AI involves much more than just a few technical fixes; fairness involves cooperation by engineers, lawyers, and community leaders in finding and rooting out biases at every level of design and implementation.
Public Fears and the Reality of Concerns Related to AI.
Public Fears and the Reality of Concerns related to AI is majorly anchored in its lack of transparency and accountability. Many fear that “technology automated” decisions could lead to unjust outcomes because machines do not have the moral judgment necessary particularly in intricate situations. The New Orleans case is illustrative of this fear, where citizens are worried if law enforcement can actually govern the use of AI responsibly. I basically agree with these being valid concerns. Indeed, Gipson Rankin (2021) describes how the opacity of AI often denies those affected any legal recourse in instances where errors have been committed due to data being hacked or because of flawed algorithms. Besides this, cybersecurity risks also raise a number of public mistrust issues, with evidence of breaches concerning sensitive forensic data. Public fears provide support and firm basis for the need for rigorous oversight mechanisms that ensure the ethical and reliable use of AI tools (Yeung et al., 2021). Public fears also reflect broader concerns about the potential misuse of AI in surveillance. Citizens have described AI-powered surveillance as “surveillance on steroids,” emphasizing its invasive nature and the lack of safeguards to prevent abuse. This is reflected in studies showing that over 70% of Americans worry about AI replacing human judgment in critical tasks, such as law enforcement decision-making (Yeung et al., 2021).
Example of AI in Criminal Investigation
One prominent case is that of the “Golden State Killer,” Joseph James DeAngelo. The police used an AI genealogy website, GEDmatch, to test the DNA evidence from the crime scenes (Wickenheiser, 2019). Comparing the DNA profile of the suspect to the publicly available genetic material pointed them to his distant relatives and finally to himself. The most important role of AI here was to narrow the list of suspects from millions down to just one. However, genetic data used in such techniques involve voluntary submission by persons unaware of possible uses in law enforcement applications. This example shows that though AI could solve complex cases. However these solutions raise ethical dilemmas as well.
Methods I would use to solve the case
If I were to be tasked with solving this case, I would use AI tools in strict conformity with the ethical guidelines. First, data anonymization techniques will be employed to protect the identity of individuals while analyzing the data. Second, the process should be transparent; this means providing documentation of the decision-making process of the AI system for external audits. I would also work with human investigators in validating the findings from AI so that they meet the standards set by the law. Additionally, I would add steps of community input during AI system development to increase public trust. Further, I would seek the involvement of diverse stakeholders in the design process for an AI system, considering fairness and inclusivity with a view of reducing the probability of biased outcomes. Rigorous testing of these AI tools in controlled environments before they go live would also be essential (Gipson Rankin, 2021). Such tests would mimic a wide range of scenarios so that the developers could find the weak points and further refine the algorithms to make them more reliable and fair. This means that AI systems have to be constantly updated and trained to keep them abreast of new challenges, such as changes in criminal behavior or advancements in forensic science.
Ethical Implications of Using AI
The use of AI raises ethical concerns and challenges. AI systems make decisions that can affect people’s lives, sometimes without them even realizing it. This means AI-generated decisions can potentially lead to harm. Reliance on AI, facial recognition technologies, for instance, has led to the increased error rate in recognizing individuals with darker skin, more so affecting minority communities than others (Gipson Rankin, 2021). These are issues that demand the implementation of ethical frameworks, such as impact assessments prior to the deployment of AI tools and diverse stakeholders in their development. Additionally, regulations must hold AI developers and users accountable for ensuring fairness and accuracy in their applications. Besides that, ethical issues also go to data security. According to RAND Corporation, vulnerabilities in the AI system can result in a data breach. This can result in sensitive information being compromised. Ethical use of AI should ensure cybersecurity mechanisms are maintained in order to protect privacy and avoid misusing individuals’ personal information. (Yeung et al., 2021).
Reliability of using AI technology to solve the case.
Reliability of AI in criminal investigations would therefore be based on data quality, design of the algorithm, and oversight of the system. While AI is particularly appropriate to process large datasets for finding patterns, it makes decisions as good as the quality of data that trains the algorithm. Poor quality or biased data can result in grave mistakes. For example wrongful cases of arrest due to faulty face recognition systems. Furthermore, the particulars of a criminal investigation generally entail subjective elements that AI alone can’t grasp (Gipson Rankin, 2021). For example, motive or intent is only contextualized by investigators through understanding and, therefore, exceeds mere raw data. The implication, therefore, is that while AI is helpful, it needs to supplement rather than supplant human judgment. Other issues of reliability arise out of increased cybersecurity risks. In this regard, hackers can compromise AI systems, distort outputs, and, in the process, frustrate justice
Conclusion
In the criminal justice system, AI has introduced advanced methods of forensic analysis and decision-making. However, it is worth noting that its applications do not go without ethical concerns and issues related to their reliability. However, looking at AI being utilized in the investigation of critical cases, like the renowned case “Golden State Killer,” it would be concluded that AI possesses huge potential toward being effective and efficient in complementing performance, notwithstanding a number of challenges.
References
Gipson Rankin, S. M. (2021). Technological tethereds: potential impact of untrustworthy artificial intelligence in criminal justice risk assessment instruments. Wash. & Lee L. Rev., 78, 647.
Wickenheiser, R. A. (2019). Forensic genealogy, bioethics and the Golden State Killer case. Forensic science international: Synergy, 1, 114-125.
Yeung, D., Khan, I., Kalra, N., & Osoba, O. (2021). Identifying systemic bias in the acquisition of machine learning decision aids for law enforcement applications. RAND.
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AI and Law Enforcement
There are ethical and reliability concerns about artificial intelligence (AI) making law enforcement, homeland security, private security, and corrections operational decisions. This is explained in a January 2021 research publication by the Rand Corporation, created by Douglas Yeung, Inez Khan, Nidhi Kalra, and Osonde A. Osoba.
AI is increasingly relied upon by American local law enforcement, as documented in a Wall Street Journal presentation on July 3, 2019. This documentation focuses on the New Orleans, LA, police department, which uses many cameras set up on street corners and places where humans are found. Because of this, AI analyzes the tremendous amount of information and makes decisions (not humans) regarding what needs to be addressed by police.
In this New Orleans presentation, private citizens call this AI surveillance as “surveillance on steroids!” Private citizens also ask in this presentation who makes sure that AI performs as intended. Also, can the police be trusted to police themselves regarding what is quickly becoming “technology automated” (machines making the decisions) and not human law enforcement authorities?
You are part of a task force to research the use of forensic data and technology that relies upon artificial intelligence (AI). Your manager has asked for an analysis report on potential current uses of AI in criminal investigations.
Read the article from the University Library.
Write a 1,400- to 1,750-word analysis report responding to the following:
- Analyze the use of forensic technology within the context of artificial intelligence.
- Provide a stance on the two arguments presented in the article about “AI is as likely to contribute to racism in the law as it is a means to end it.”
- Explain why you would or would not agree about “Public Fears and the Reality of Concerns Related to AI.”
- Provide an example of the use of AI in a criminal investigation case and evaluate its use to solve it.
- Explain the methods you would use to solve the case.
- Assess the ethical implications of using technology such as AI in criminal investigations.
- Assess the reliability of using AI technology to solve the case.
Please cite two literature references (found outside the classroom) using correct APA formatting in the body of this report that reinforces what you are saying, and list these two literature references with correct APA formatting on a literature reference page at the end of this report…eight points deducted for each missing literature reference.

