Ethnic studies through social psychology lenses: a commentary on plausible perspectives

The current report present a review on some assumptions related to the theoretical lenses of a social psychology framework on ethnic and minority studies. The major implications of selected previous studies are reviewed, as well as some conceptual ambiguities related to the role of social cognition, social interaction and executive functioning of sociocultural planes of analyses in explaining ethnic and minority studies. The constructual validity of a proposed sociocultural framework on a study upon minority Orang Asli children, as well as the limitations of major social psychology theories, is discussed. Discussion is also given to: (a) identifying pertinent denominations in social cognition, (b) incorporating implicit cognition data, and (c) collaborative sociocultural lenses theories into future research practice in ethnic and minorities studies in Malaysia

The future of social is personal: the potential of the personal data store

This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges

Tsetlin Machine for Fake News Detection: Enhancing Accuracy and Reliability

This thesis aims to improve the accuracy of fake news detection by using Tsetlin Machines (TM). TMs are well suited for noisy and complex relations within the provided data, which on initial analysis, overlaps nicely with characteristics found in fake news. We provide a performant and deterministic preprocessor, which is responsible for tokenizing, lemmanzing, and encoding to a representation that the TM understands. We compare our approach with TMs against Neural Networks (NN) models over a variety of well-known datasets within the fake news domain. Our findings show from comparable results to significant improvements over state of the art. Additionally, we show how TMs allow for interpretable propositional logic rules. For datasets with 2 classifications, we further convey these rules during inference by applying a color between red and green, which shows the intensity and what direction each word pulls the classification towards

Causal inference for social network data

We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic result is the first to allow for dependence of each observation on a growing number of other units as sample size increases. While previous methods have generally implicitly focused on one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties, and for dependence due to latent similarities among nodes sharing ties. We describe estimation and inference for new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. Using our methods to reanalyze the Framingham Heart Study data used in one of the most influential and controversial causal analyses of social network data, we find that after accounting for network structure there is no evidence for the causal effects claimed in the original paper

Considerations in Assuring Safety of Increasingly Autonomous Systems

Recent technological advances have accelerated the development and application of increasingly autonomous (IA) systems in civil and military aviation. IA systems can provide automation of complex mission tasks-ranging across reduced crew operations, air-traffic management, and unmanned, autonomous aircraft-with most applications calling for collaboration and teaming among humans and IA agents. IA systems are expected to provide benefits in terms of safety, reliability, efficiency, affordability, and previously unattainable mission capability. There is also a potential for improving safety by removal of human errors. There are, however, several challenges in the safety assurance of these systems due to the highly adaptive and non-deterministic behavior of these systems, and vulnerabilities due to potential divergence of airplane state awareness between the IA system and humans. These systems must deal with external sensors and actuators, and they must respond in time commensurate with the activities of the system in its environment. One of the main challenges is that safety assurance, currently relying upon authority transfer from an autonomous function to a human to mitigate safety concerns, will need to address their mitigation by automation in a collaborative dynamic context. These challenges have a fundamental, multidimensional impact on the safety assurance methods, system architecture, and V&V capabilities to be employed. The goal of this report is to identify relevant issues to be addressed in these areas, the potential gaps in the current safety assurance techniques, and critical questions that would need to be answered to assure safety of IA systems. We focus on a scenario of reduced crew operation when an IA system is employed which reduces, changes or eliminates a human's role in transition from two-pilot operations

Structural Racism and Youth Development

Youth of color have experienced poor outcomes relative to their white counterparts historically, and these disparities persist today. Researchers have offered a number of explanations for these disparities, some of the more popular of which have focused on individual deficiencies. If one elucidates the underlying theories of change of dominant practices and public policies in the youth field, it appears that, despite variation in approach and emphasis, they too have focused on individual behavior. While behavior is clearly an important contributor to the outcomes that individuals experience, it is not the sole determinant. Rather, we contend that there are larger, structural factors that contribute to the racial disparities between youth of color and their white counterparts that deserve systematic and sustained attention

Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites

Dark patterns are user interface design choices that benefit an online service by coercing, steering, or deceiving users into making unintended and potentially harmful decisions. We present automated techniques that enable experts to identify dark patterns on a large set of websites. Using these techniques, we study shopping websites, which often use dark patterns to influence users into making more purchases or disclosing more information than they would otherwise. Analyzing ~53K product pages from ~11K shopping websites, we discover 1,818 dark pattern instances, together representing 15 types and 7 broader categories. We examine these dark patterns for deceptive practices, and find 183 websites that engage in such practices. We also uncover 22 third-party entities that offer dark patterns as a turnkey solution. Finally, we develop a taxonomy of dark pattern characteristics that describes the underlying influence of the dark patterns and their potential harm on user decision-making. Based on our findings, we make recommendations for stakeholders including researchers and regulators to study, mitigate, and minimize the use of these patterns.Comment: 32 pages, 11 figures, ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2019