The AI assistant agrees with the suggested answer D: Information about how the individual was identified by the AI system.

The reason for choosing this answer is that while the immediate moment of arrest may not legally require a detailed explanation of the AI system's role in identification, principles of transparency, due process, and the potential for legal challenges strongly suggest that this information should be disclosed. When an individual is identified and subsequently arrested based on an AI system that considers factors like "previous criminal history, location, time, race and gender," there are significant implications for civil liberties and potential biases. Explaining how the individual was identified by the AI system, even if not at the exact moment of arrest but at a subsequent point (e.g., during booking or initial questioning), is crucial for several reasons:

Transparency and Accountability: The use of AI in policing raises concerns about fairness, bias, and due process. Providing information about how the AI identified the individual helps ensure transparency in law enforcement practices.
Right to Challenge: If an individual understands that an AI system was instrumental in their identification and subsequent arrest, they (and their legal counsel) can better assess whether there are grounds to challenge the AI's methodology, data inputs, or potential biases. Explainable AI, which allows investigators to understand how the AI reaches its conclusions, is becoming increasingly important in this context [ukri.org].
Mitigating Bias: The case study explicitly mentions that the AI system considers "race and gender" as factors in assigning a likelihood score. This raises serious concerns about algorithmic bias and discrimination. Individuals have a right to know if such factors played a role in their identification.
Legal Precedent and Policy: While the Fourth Amendment focuses on probable cause, not the immediate explanation of the reason for arrest, the increasing use of AI in law enforcement is leading to new legal and policy discussions. Reports highlight concerns about AI systems used by state governments, often without meaningful public input or oversight, impacting decisions about policing and targeting [epic.org]. Some agencies are using automated decision systems in policing, including facial recognition databases filled with mugshots, raising questions about data retention and due process [aclu-wa.org]. As AI systems become more sophisticated and integral to law enforcement, especially in identifying potential wrongdoing, there will likely be increased scrutiny and demands for transparency regarding their operation and impact [skadden.com]. The ARIEN project, for example, focuses on explainable AI to help law enforcement act on information with greater confidence [ukri.org].
Building Trust: In an era of increasing public scrutiny of police practices, providing clear information about the tools used, especially advanced AI, can help build or maintain public trust.

The reasons for not choosing the other answers are:

A. Information about the scoring protocol of the AI system: While important for overall system transparency, explaining the complex "scoring protocol" (the intricate algorithms and statistical models) at the moment of arrest is likely too technical and overwhelming. The focus should be on how the individual was *identified*, not necessarily the granular details of the scoring mechanism itself, which might be better addressed in a legal context.
B. Information about the public sites surveilled by the AI system: While this information is relevant to understanding the scope of the AI's data collection, it is a broader detail about the system's operation rather than directly explaining *why this specific individual* was targeted. The focus at the time of arrest should be on the individual's connection to the AI's output.
C. Information about use of the AI system in prosecuting the case: This information is premature at the time of arrest. Decisions about prosecuting the case, and how the AI system's output will be used as evidence, are legal strategies that develop after the arrest and typically involve legal counsel. The immediate concern at arrest is the basis for the arrest itself.

The discussion content mentions that "the suspect must generally be informed of the charges promptly after the arrest, typically during booking or shortly thereafter." While true, this doesn't contradict the need to explain *how* the individual was identified, especially when an AI system played a direct role in that identification. The charges themselves might be general, but the method of identification is a distinct and increasingly important piece of information given the rise of AI in law enforcement [youtube.com].

Citations:

How Automated Decision Systems are used in Policing, https://www.aclu-wa.org/story/how-automated-decision-systems-are-used-policing
New EPIC Report Delves into State AI Procurement, https://epic.org/new-epic-report-delves-into-state-ai-procurement/
The US Government Is Using AI To Detect Potential Wrongdoing ..., https://www.skadden.com/insights/publications/2024/03/insights-special-edition/the-us-government-is-using-ai
How AI is revolutionising the fight against drug trafficking - UKRI, https://www.ukri.org/who-we-are/how-we-are-doing/research-outcomes-and-impact/how-ai-is-revolutionising-the-fight-against-drug-trafficking/
How AI is revolutionising the fight against drug trafficking - YouTube, https://www.youtube.com/watch?v=agQZ4S70d1A