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"Behavior prediction"
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Why elephants cry : how observing unusual animal behaviours can predict the weather (and other environmental phenomena)
\"Why Elephants Cry is a fascinating frolic through the evidence surrounding the use of unusual behavior of animals to measure and predict the environment. The role of animals, from the smallest ant to the biggest elephant, as predictors of environmental changes is framed around the climate crisis, renowned Biologist John Hancock collecting anecdotal stories and myths along with scientific evidence to demonstrate that observation of animals can be of tangible use. He looks at the measurement of air temperature using ants, crickets and snakes, and how a wide range of animals can predict the weather or the imminent eruption of volcanoes and earthquakes\"-- Provided by publisher.
Book
Stereotypes as character evidence
Base rate evidence often connects a defendant to an act through the defendant's membership in a certain population. It includes evidence arising from forensic analysis, criminal profiling, statistical analysis, artificial intelligence, and many other common and emerging scientific methods. But while this evidence is prevalent in civil and criminal trials, it is poorly understood, and there is little predictability in how a court will decide its admissibility or even what standard the court will apply. In this article I show that although some forms of base rate evidence are desirable and even critical to achieving an accurate case outcome, a common form of base rate evidence called profile evidence often constitutes unrecognized character evidence - evidence that a defendant acted in accordance with a certain character trait - which is prohibited by federal and state evidentiary rules. To show this, and to describe precisely the relationship between base rate evidence and ordinary character evidence, I draw on a statistical tool called Bayesian inference to define a concept that I call predictive character evidence. Predictive character evidence describes a behavioral propensity of a population to suggest that an individual member of the population acted in accordance with this propensity. I show that this evidence - a form of base rate evidence that involves behavioral stereotyping-relies on character reasoning and is therefore impermissible under the rule against 'character evidence'.
Finally, I discuss critical implications of my analysis. First, I show how an understanding of 'predictive character evidence' helps resolve longstanding confusion and inconsistency surrounding base rate evidence and profile evidence in particular. I then demonstrate that applying the rule against character evidence to determine the admissibility of profile evidence is essential to achieving correct and predictable evidentiary decisions, to minimizing the influence of implicit biases based on race and other personal characteristics of a defendant, and to reaching accurate verdicts.
Journal Article
What even is a criminal attitude? - and other problems with attitude and associational factors in criminal risk assessment
Several widely used criminal risk assessment instruments factor a defendant's abstract beliefs, peer associations, and family relationships into their risk scores. The inclusion of those factors is empirically unsound and raises profound ethical and constitutional questions. This article is the first instance of legal scholarship on criminal risk assessment to: (a) conduct an in-depth review of risk assessment questionnaires, scoresheets, and reports; and (b) analyze the First and Fourteenth Amendment implications of attitude and associational factors. Additionally, this article challenges existing scholarship by critiquing widely accepted but dubious empirical justifications for the inclusion of attitude and associational items. The items are only weakly correlated with recidivism, have not been shown to be causal, and have in fact been shown to decrease the predictive validity of risk assessment instruments. Quantification of attitudes and associations should cease unless and until it is done in a way that is empirically sound, more useful than narrative reports, and consistent with the First and Fourteenth Amendments.
Journal Article
Extrinsic Behavior Prediction of Pedestrians via Maximum Entropy Markov Model and Graph-Based Features Mining
With the change of technology and innovation of the current era, retrieving data and data processing becomes a more challenging task for researchers. In particular, several types of sensors and cameras are used to collect multimedia data from various resources and domains, which have been used in different domains and platforms to analyze things such as educational and communicational setups, emergency services, and surveillance systems. In this paper, we propose a robust method to predict human behavior from indoor and outdoor crowd environments. While taking the crowd-based data as input, some preprocessing steps for noise reduction are performed. Then, human silhouettes are extracted that eventually help in the identification of human beings. After that, crowd analysis and crowd clustering are applied for more accurate and clear predictions. This step is followed by features extraction in which the deep flow, force interaction matrix and force flow features are extracted. Moreover, we applied the graph mining technique for data optimization, while the maximum entropy Markov model is applied for classification and predictions. The evaluation of the proposed system showed 87% of mean accuracy and 13% of error rate for the avenue dataset, while 89.50% of mean accuracy rate and 10.50% of error rate for the University of Minnesota (UMN) dataset. In addition, it showed a 90.50 mean accuracy rate and 9.50% of error rate for the A Day on Campus (ADOC) dataset. Therefore, these results showed a better accuracy rate and low error rate compared to state-of-the-art methods.
Journal Article
Chasing a serial killer : be a criminal profiler
Learn how a criminal profiler uses the investigative process and clues left behind at a crime scene to predict the next moves of a serial killer.
Book
Demystifying Artificial Intelligence based Behavior Prediction of Traffic Actors for Autonomous Vehicle- A Bibliometric Analysis of Trends and Techniques
Background: The purpose of this study is to examine, using bibliometric methods, the work done on behavior prediction of traffic actors for autonomous vehicles using various artificial intelligence algorithms from 2011 to 2020. Methods: Using one of the most common databases, Scopus, numerous papers on behavior prediction of traffic actors for autonomous vehicles were retrieved. The research papers are being considered for the period from 2011 to 2020. The Scopus analyzer is used to obtain some results of the study, such as documents by year, source, and country and so on. VOSviewer Version 1.6.16 is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis etc. Results: In our study, a database search outputs a total of 275 articles on behavior prediction for autonomous vehicle from 2011 to 2020. Statistical analysis and network analysis shows the maximum articles are published in the years 2019 and 2020 with United State contributed the largest number of documents. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scopus keyword search outcome has 272 articles with English language having the largest number. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of advanced algorithms of computer vision, deep learning, machine learning and explainable artificial intelligence.
Journal Article
Criminal Behavior and Accountability of Artificial Intelligence Systems
AI systems have the capacity to act in a way that can generally be considered as 'criminal' by society.Yet, it can be argued that they lack (criminal) agency - and the feeling of it.In the future, however, humans might develop expectations of norm-conforming behavior from machines.
eBook