A review of predictive policing from the perspective of fairness

Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While there is much research on fair machine learning in general, there is a need to investigate fair machine learning techniques as they pertain to the predictive policing. Therefore, we evaluate the existing publications in the field of fairness in machine learning and predictive policing to arrive at a set of standards for fair predictive policing. We also review the evaluations of ML applications in the area of criminal justice and potential techniques to improve these technologies going forward. We urge that the growing literature on fairness in ML be brought into conversation with the legal and social science concerns being raised about predictive policing. Lastly, in any area, including predictive policing, the pros and cons of the technology need to be evaluated holistically to determine whether and how the technology should be used in policing.

     

Usability Science. I: Foundations

In this article, we describe and analyze the emergence of a scientific discipline, usability science, which bridges basic research in cognition and perception and the design of usable technology. An analogy between usability science and medical science (which bridges basic biological science and medical practice) is discussed, with lessons drawn from the way in which medical practice translates practical problems into basic research and fosters technology transfer from research to technology. The similarities and differences of usability science to selected applied and basic research disciplines-human factors and human-computer interaction (HCI) is also described. The underlying philosophical differences between basic cognitive research and usability science are described as Wundtian structuralism versus Jamesian pragmatism. Finally, issues that usability science is likely to continue to address-presentation of information, user navigation, interaction, learning, and methods-are described with selective reviews of work in graph reading, controlled movement, and method development and validation.     

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Herein, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.     

Performance evaluation of the SX‐6 vector architecture for scientific computations

The growing gap between sustained and peak performance for scientific applications is a well‐known problem in high‐performance computing. The recent development of parallel vector systems offers the potential to reduce this gap for many computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX‐6 vector processor, and compares it against the cache‐based IBM Power3 and Power4 superscalar architectures, across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines many low‐level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Overall results demonstrate that the SX‐6 achieves high performance on a large fraction of our application suite and often significantly outperforms the cache‐based architectures. However, certain classes of applications are not easily amenable to vectorization and would require extensive algorithm and implementation reengineering to utilize the SX‐6 effectively. Copyright © 2005 John Wiley & Sons, Ltd.     

A Dynamic Interaction Between Machine Learning and the Philosophy of Science

The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.     

Comments on “The Replication of the Hard Problem of Consciousness in AI and Bio-AI”

In their joint paper entitled “The Replication of the Hard Problem of Consciousness in AI and BIO-AI” (Boltuc et al. Replication of the hard problem of conscious in AI and Bio- AI: An early conceptual framework 2008), Nicholas and Piotr Boltuc suggest that machines could be equipped with phenomenal consciousness, which is subjective consciousness that satisfies Chalmer’s hard problem (We will abbreviate the hard problem of consciousness as “H-consciousness”). The claim is that if we knew the inner workings of phenomenal consciousness and could understand its’ precise operation, we could instantiate such consciousness in a machine. This claim, called the extra-strong AI thesis, is an important claim because if true it would demystify the privileged access problem of first-person consciousness and cast it as an empirical problem of science and not a fundamental question of philosophy. A core assumption of the extra-strong AI thesis is that there is no logical argument that precludes the implementation of H-consciousness in an organic or in-organic machine provided we understand its algorithm. Another way of framing this conclusion is that there is nothing special about H-consciousness as compared to any other process. That is, in the same way that we do not preclude a machine from implementing photosynthesis, we also do not preclude a machine from implementing H-consciousness. While one may be more difficult in practice, it is a problem of science and engineering, and no longer a philosophical question. I propose that Boltuc’s conclusion, while plausible and convincing, comes at a very high price; the argument given for his conclusion does not exclude any conceivable process from machine implementation. In short, if we make some assumptions about the equivalence of a rough notion of algorithm and then tie this to human understanding, all logical preconditions vanish and the argument grants that any process can be implemented in a machine. The purpose of this paper is to comment on the argument for his conclusion and offer additional properties of H-consciousness that can be used to make the conclusion falsifiable through scientific investigation rather than relying on the limits of human understanding.     

Howard Aiken's third machine: the Howard Mark III calculator orAiken-Dahlgren electronic calculator

The lines of computing machines that had their origin in the days immediately preceding World War II include a series of calculators which Howard Aiken, a professor of applied mathematics at Harvard University, designed. Starting with the Mark I in 1944, Aiken spearheaded an effort that provided not only the physical means of computation but also the tools to direct them and the people to operate them. The third in this sequence of machines was an innovation in design and implementation, while at the same time being conservative in the selection of components. The Harvard Mark III Calculator had the potential to be a significant entry into the field of computing, but events slowed its completion until competitors finished other markedly superior systems. The Mark III was not a machine that would be emulated or replicated beyond its lifetime, but the people who planned it, built it, programmed it and operated it went on to make significant contributions to the science and practice of computing     

汶川地震研究信息化平台建设探讨

作为开展地震科学e-Science 的应用实践,我们建设的“汶川地震研究”网站为相关研究人员提供一个信息化平台,帮助科研人员及时地搜集与本专业相关的科研资源,加强相互之间的交流与分享。本平台收集整理了国内外发表的和汶川地震相关的约750 篇科学研究文献,录入网络数据库,建立了“汶川地震研究”专题网站(www.wceq.org)。应用实践表明,该平台在促进汶川地震研究以及中外科研成果交流和分享方面起到了较好作用。     

A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships

Conclusions Progress in machine learning must consist of periods of exploration followed by periods of more thorough careful investigation of issues raised during exploration. The research reported inCreating a Memory of Causal Relationships is exploratory in that it addresses a problem that was not previously investigated in the mainstream of machine learning research. However, I feel that the problem studied was worthy of investigation and is worthy of continued investigation since it corresponds to an important part of the human learning process.     

Performance evaluation of an online argumentation learning assistance agent

Recent research indicated that students’ ability to construct evidence-based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best of our knowledge, no research work in the literature addressed the issue of automatically assessing the student’s argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners’ arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student’s argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.     

Learning,goals, and learning goals: A perspective on goal-driven learning

In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet ofgoal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This article examines the motivations for adopting a goal-driven model of learning, the relationship between task goals and learning goals, the influences goals can have on learning, and the pragmatic implications of the goal-driven learning model. It presents a new integrative framework for understanding the goal-driven learning process and applies this framework to characterizing research on goal-driven learning.     

Editorial: Human and Machine Learning

Summary Although science can be characterized in terms of search, some search methods let one explore multiple paths in parallel. We have argued that more machine learning researchers should focus their efforts on modeling human behavior, but we have not argued that the field should limit itself to this approach. For those interested in general principles, the study of nonhuman learning methods is also necessary for useful results. In terms of applications, some of machine learning's greatest achievements have involved nonincremental methods that are clearly poor models of human learning. Planes are terrible imitations of birds (and fly less efficiently), but there are still excellent reasons for using aircraft.However, we do believe that too little research has focused on results from the literature on human learning, and that greater attention in this direction would benefit the field as a whole. Science is a complex and bewildering process, and the scientist should employ all available knowledge to direct his steps in useful directions. This strategy seems especially important in young fields like machine learning, in which conflicting views and methods abound. We encourage the reader to join us in applying machine learning techniques to explain the mysteries of human behavior, and in using knowledge of human behavior to constrain our computational theories of learning.     

De nouveaux guides pour l'alphabétisation scientifique

Abstract

It is now a decade since the first major curricula began to appear that were serious responses to the challenge that school science, particularly in the compulsory years, should be concerned primarily with general scientific literacy, that is, with a learning of science that would empower all students for life in societies increasingly influenced by science and technology. In almost all cases, these curricula have expectations for science teaching and learning that are quite unrealistically extensive. They are already proving difficult for teachers and unattractive to students. Furthermore, this elaboration of the content for school science has led to a questioning of the notion of scientific literacy itself. Scientists and science educators have been primarily responsible for suggesting the expanded curriculum content. In this paper it is argued that this is a consequence of seeing society through scientifically attuned eyes. It is time, therefore, to consider life in society itself as the starting point for determining the scientific knowledge that should be given priority in the school science curriculum. A current example of this type of analysis of life in society in three Chinese cities is given.     

基于留一交叉验证的类不平衡危害预评估策略

近年来,类不平衡问题已逐渐成为人工智能﹑机器学习和数据挖掘等领域的研究热点,目前已有大量实用有效的方法.然而,近期的研究结果却表明,并非所有的不平衡数据分类任务都是有害的,在无害的任务上采用类不平衡学习算法将很难提高,甚至会降低分类的性能,同时可能大幅度增加训练的时间开销.针对此问题,提出了一种危害预评估策略.该策略采用留一交叉验证法(LOOCV,Leave-one-out cross validation)测试训练集的分类性能,并据此计算一种称为危害测度(HM,Harmful-ness Measure)的新指标,用以量化危害的大小,从而为学习算法的选择提供指导.通过8个类不平衡数据集对所提策略进行了验证,表明该策略是有效和可行的.     

“Words lie in our way”

The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scientific claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation and digital computation are not essentially different, and so arguments such as Searle's hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scientific matter of fact. (4) A substantive scientific question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it's all too easy to become ensnared.     

A critique of the latent damage Expert System

Although, the Latent Damage System was produced in the late 1980s, Susskind—a co‐developer—asserts in his latest book that this and similar systems will have a profound influence upon the future direction and practice of law, by bringing about a shift in the legal paradigm (Susskind, The Future of Law Facing the Challenges of Information Technology, 2996. pp. 105, 286). As part of the research into the conflict which, in my view, exists between the artificial intelligence and law movement and adversarial argumentation in the litigatory process, I analyse the claims and objectives made by the developers of the Latent Damage System and suggest that the current technological know‐how is incapable of representing dynamic, adversarial, legal environments. In consequence, I contend that intelligent‐based applications cannot provide an authentic and automatic access to resolving adversarial legal disputes.     

A survey of multi-view machine learning

Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning.     

Expanded cloud plumes hiding Big Data ecosystem

Today, a paradigm shift is being observed in science, where the focus is gradually shifting away from operation to data, which is greatly influencing the decision making also. The data is being inundated proactively from several sources in various forms; especially social media and in modern data science vocabulary is being recognized as Big Data. Today, Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, X…X}classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term “cloud” in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed in the past couple of years.     

Identifying Strategies to Support Junior Secondary Students to Engage in Scientific Investigation Tasks

Abstract

This article describes the outcome of a collaborative project between the Hong Kong Institute of Education and four secondary schools that aims to promote the development of scientific investigation skills. The project team designed scientific investigation tasks collaboratively with the teachers and provided school-based support when the tasks were implemented. A total of six teachers and 575 students were involved. Data were collected through questionnaires completed by the students and individual interviews with science teachers about their classroom practice after the completion of the project. The findings suggest that the students did not meet many difficulties and that there were positive influences on students’ interest in learning science. The teachers perceived that there were challenges related to raising students’ self-regulated learning abilities, structuring tasks that were at appropriate levels of difficulty, and promoting group cooperation among the students. Finally, the article argues that the strategies implemented in this study were effective, though it takes much time and effort to help students develop self-regulated learning abilities. The conclusion suggests that teachers consider these challenges collectively and proposes a two-staged model for planning scientific investigation tasks.