machine-learning
-
The preceding chapters have moved through the criminal justice and immigration contexts applying two parallel regulatory frameworks — the EU AI Act and the US constitutional and statutory structure — to each system encountered. This chapter steps back to map the architecture of those two frameworks directly against each other. The comparison is not academic:…
-
This chapter is a synthesis. Chapters 25 through 31 have analyzed each algorithmic system in depth — the legal frameworks that govern it, the constitutional arguments it generates, and the specific deficiencies that make its outputs contestable. This chapter assembles those tools into a practical sequence for the lawyer who walks into court knowing that…
-
The chapters in this module have documented how algorithmic systems enter criminal proceedings: COMPAS at sentencing, the PSA at bail, Clearview AI at identification, predictive policing in patrol, mass surveillance in the investigative data stream, and algorithmic outputs at the evidentiary threshold. Each of those systems has one feature in common: they all generate outputs…
-
Every system examined in the previous four chapters — COMPAS at sentencing, the PSA at bail, Clearview AI at identification, predictive policing in patrol — eventually converges on the same procedural threshold. Before algorithmic output can influence what a jury decides, it must survive the courtroom. That moment is the procedural bottleneck of algorithmic criminal…
-
The previous chapter examined how the PSA can displace individualized judicial judgment at the bail stage — a tool that is transparent by design but dangerous in practice because of automation bias. Clearview AI presents a structurally different threat. The PSA operates within the formal legal proceeding. Clearview operates before it — in the investigative…
-
The previous chapter examined COMPAS as the defining case of proprietary opacity in criminal justice AI: a tool whose internal methodology was protected as a trade secret, leaving defendants to challenge a score they could not examine. The Public Safety Assessment presents a different legal problem — one that is in some ways more revealing…
-
COMPAS has appeared in this book before — introduced in Chapter 2 as the case that made the blackbox problem impossible for courts to ignore, and examined in Chapter 14 as the central exhibit in the algorithmic discrimination debate. This chapter does not repeat that foundation. It builds on it, moving from the general to…
-
The immigration modules showed how automated systems can shape a case before a judge fully sees it. In criminal justice, the architecture of harm is the same but the stakes escalate. A visa denial can be reconsidered. A detention recommendation can be revisited at a bond hearing. But when an algorithm enters bail, sentencing, parole,…
-
The previous chapters examined the systems that shape immigration outcomes before a judge ever sees the case: data-fusion platforms, detention classifiers, supervision scores, text-analytics tools, and biometric matching systems. Different architectures, same structural vulnerability. In automated immigration systems, the database often becomes the first version of the factual record. If the data is wrong, every…
-
The previous chapter examined how text analytics can shape an asylum file before the adjudicator reaches the merits. Facial recognition raises a structurally similar problem in a different form: instead of screening a narrative, it screens the body. At the border, identity is law. If the system confirms a match, the person moves. If it…