Matches in SemOpenAlex for { <https://semopenalex.org/work/W3163497472> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W3163497472 abstract "Most applications of machine learning in criminal law focus on making predictions about people and using those predictions to guide decisions. For example, judges use risk assessment tools to predict the likelihood of future violence when making decisions about whom to detain pre-trial. Whereas this predictive technology analyzes people about whom decisions are made, we propose a new direction for machine learning that scrutinizes decision-making itself. Our aim is not to predict behavior, but to provide the public with data-driven opportunities to improve the fairness and consistency of human discretionary judgment. We call our approach the Recon Approach because it encompasses two functions: reconnaissance and reconsideration. Reconnaissance harnesses natural language processing to cull through thousands of hearing transcripts and illuminate factors that appear to have influenced decisions at those hearings. Reconsideration uses modeling techniques to identify cases that appear anomalous in a way that warrants a closer review of those decisions. Reconnaissance reveals patterns that may show systemic problems across a set of decisions; reconsideration flags potential errors or injustices in individual cases. As a team of computer scientists and legal scholars, we describe our early work to apply the Recon Approach to parole-release decisions in California. Drawing on that work, we discuss challenges to the Recon Approach, as well as its potential to apply to sentencing and other discretionary decision-making contexts within and beyond criminal law." @default.
- W3163497472 created "2021-05-24" @default.
- W3163497472 creator A5018761586 @default.
- W3163497472 creator A5033827647 @default.
- W3163497472 creator A5035085176 @default.
- W3163497472 creator A5054737143 @default.
- W3163497472 date "2021-04-26" @default.
- W3163497472 modified "2023-09-23" @default.
- W3163497472 title "The Recon Approach: A New Direction for Machine Learning in Criminal Law" @default.
- W3163497472 hasPublicationYear "2021" @default.
- W3163497472 type Work @default.
- W3163497472 sameAs 3163497472 @default.
- W3163497472 citedByCount "3" @default.
- W3163497472 countsByYear W31634974722021 @default.
- W3163497472 crossrefType "posted-content" @default.
- W3163497472 hasAuthorship W3163497472A5018761586 @default.
- W3163497472 hasAuthorship W3163497472A5033827647 @default.
- W3163497472 hasAuthorship W3163497472A5035085176 @default.
- W3163497472 hasAuthorship W3163497472A5054737143 @default.
- W3163497472 hasConcept C102587632 @default.
- W3163497472 hasConcept C127413603 @default.
- W3163497472 hasConcept C154945302 @default.
- W3163497472 hasConcept C15744967 @default.
- W3163497472 hasConcept C177264268 @default.
- W3163497472 hasConcept C17744445 @default.
- W3163497472 hasConcept C18762648 @default.
- W3163497472 hasConcept C199360897 @default.
- W3163497472 hasConcept C199539241 @default.
- W3163497472 hasConcept C202565627 @default.
- W3163497472 hasConcept C2776436953 @default.
- W3163497472 hasConcept C41008148 @default.
- W3163497472 hasConcept C78519656 @default.
- W3163497472 hasConceptScore W3163497472C102587632 @default.
- W3163497472 hasConceptScore W3163497472C127413603 @default.
- W3163497472 hasConceptScore W3163497472C154945302 @default.
- W3163497472 hasConceptScore W3163497472C15744967 @default.
- W3163497472 hasConceptScore W3163497472C177264268 @default.
- W3163497472 hasConceptScore W3163497472C17744445 @default.
- W3163497472 hasConceptScore W3163497472C18762648 @default.
- W3163497472 hasConceptScore W3163497472C199360897 @default.
- W3163497472 hasConceptScore W3163497472C199539241 @default.
- W3163497472 hasConceptScore W3163497472C202565627 @default.
- W3163497472 hasConceptScore W3163497472C2776436953 @default.
- W3163497472 hasConceptScore W3163497472C41008148 @default.
- W3163497472 hasConceptScore W3163497472C78519656 @default.
- W3163497472 hasLocation W31634974721 @default.
- W3163497472 hasOpenAccess W3163497472 @default.
- W3163497472 hasPrimaryLocation W31634974721 @default.
- W3163497472 hasRelatedWork W15149605 @default.
- W3163497472 hasRelatedWork W1573448657 @default.
- W3163497472 hasRelatedWork W163239981 @default.
- W3163497472 hasRelatedWork W1934707494 @default.
- W3163497472 hasRelatedWork W2029256104 @default.
- W3163497472 hasRelatedWork W2052277161 @default.
- W3163497472 hasRelatedWork W2060706380 @default.
- W3163497472 hasRelatedWork W2085177922 @default.
- W3163497472 hasRelatedWork W2164243453 @default.
- W3163497472 hasRelatedWork W2220205136 @default.
- W3163497472 hasRelatedWork W2345722485 @default.
- W3163497472 hasRelatedWork W2766978323 @default.
- W3163497472 hasRelatedWork W2942298076 @default.
- W3163497472 hasRelatedWork W2973590692 @default.
- W3163497472 hasRelatedWork W3020144006 @default.
- W3163497472 hasRelatedWork W3028512677 @default.
- W3163497472 hasRelatedWork W3126028293 @default.
- W3163497472 hasRelatedWork W3138208266 @default.
- W3163497472 hasRelatedWork W3165573706 @default.
- W3163497472 hasRelatedWork W21004633 @default.
- W3163497472 isParatext "false" @default.
- W3163497472 isRetracted "false" @default.
- W3163497472 magId "3163497472" @default.
- W3163497472 workType "article" @default.