Analysis of Trustworthiness in Machine Learning and Deep Learning

Trustworthy Machine Learning (TML) represents a set of mechanisms and explainable layers, which enrich the learning model in order to be clear, understood, thus trusted by users. A literature review has been conducted in this paper to provide a comprehensive analysis on TML perception. A quantitative study accompanied with qualitative observations have been discussed by categorizing machine learning algorithms and emphasising deep learning ones, the latter models have achieved very high performance as real-world function approximators (e.g., natural language and signal processing, robotics, etc.). However, to be fully adapted by humans, a level of transparency needs to be guaranteed which makes the task harder regarding recent techniques (e.g., fully connected layers in neural net-works, dynamic bias, parallelism, etc.). The paper covered both academics and practitioners works, some promising results have been covered, the goal is a high trade-off transparency/accuracy achievement towards a reliable learning approach

A generalized stereotype learning approach and its instantiation in trust modeling

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Owing to the lack of historical data regarding an entity in online communities, a user may rely on stereotyping to estimate its behavior based on historical data about others. However, these stereotypes cannot accurately reflect the user's evaluation if they are based on limited historical data about other entities. In view of this issue, we propose a novel generalized stereotype learning approach: the fuzzy semantic framework. Specifically, we propose a fuzzy semantic process, incorporated with traditional machine-learning techniques to construct stereotypes. It consists of two sub-processes: a fuzzy process that generalizes over non-nominal attributes (e.g., price) by splitting their values in a fuzzy manner, and a semantic process that generalizes over nominal attributes (e.g., location) by replacing their specific values with more general terms according to a predefined ontology. We also implement the proposed framework on the traditional decision tree method to learn users' stereotypes and validate the effectiveness of our framework for computing trust in e-marketplaces. Experiments on real data confirm that our proposed model can accurately measure the trustworthiness of sellers with which buyers have limited experience.National Natural Science Foundation of China ; Basic Academic Discipline Program for Shanghai University of Finance and Economic

O impacto da inteligência artificial no negócio eletrónico

Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos. A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais. Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments. The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data. After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works

Novel Directions for Multiagent Trust Modeling in Online Social Networks

This thesis presents two works with the shared goal of improving the capacity of multiagent trust modeling to be applied to social networks. The first demonstrates how analyzing the responses to content on a discussion forum can be used to detect certain types of undesirable behaviour. This technique can be used to extract quantified representations of the impact agents are having on the community, a critical component for trust modeling. The second work expands on the technique of multi-faceted trust modeling, determining whether a clustering step designed to group agents by similarity can improve the performance of trust link predictors. Specifically, we hypothesize that learning a distinct model for each cluster of similar users will result in more personalized, and therefore more accurate, predictions. Online social networks have exploded in popularity over the course of the last decade, becoming a central source of information and entertainment for millions of users. This radical democratization of the flow of information, while purporting many benefits, also raises a raft of new issues. These networks have proven to be a potent medium for the spread of misinformation and rumors, may contribute to the radicalization of communities, and are vulnerable to deliberate manipulation by bad actors. In this thesis, our primary aim is to examine content recommendation on social media through the lens of trust modeling. The central supposition along this path is that the behaviors of content creators and the consumers of their content can be fit into the trust modeling framework, supporting recommendations of content from creators who not only are popular, but have the support of trustworthy users and are trustworthy themselves. This research direction shows promise for tackling many of the issues we've mentioned. Our works show that a machine learning model can predict certain types of anti-social behaviour in a discussion starting comment solely on the basis of analyzing replies to that comment with accuracy in the range of 70% to 80%. Further, we show that a clustering based approach to personalization for multi-faceted trust models can increase accuracy on a down-stream trust aware item recommendation task, evaluated on a large data set of Yelp users

Geschäftsmodelle in der Plattformökonomie: Eine Untersuchung im deutschen Bekleidungshandel

Digitale Plattformen entwickeln sich zu dem dominanten Geschäftsmodell im E-Commerce. Durch ihre speziellen Logiken und Funktionsweisen – die so genannte Plattformökonomie – verändern sie ganze Branchen und Märkte. Die wissenschaftliche Auseinandersetzung mit diesem Forschungsfeld findet vor allem auf mikroökonomischer, transaktionskostenorientierter Ebene statt. Dabei steht häufig die institutionelle Rolle der Plattform im Vordergrund. Die konkreten Geschäftsmodelle in der Plattformökonomie sind hingegen bisher nur unzureichend erforscht. Die vorliegende Arbeit verschreibt sich daher dem Ziel, den Einfluss der Plattformökonomie auf Geschäftsmodelle im E-Commerce darzustellen. Dies soll am Beispiel des deutschen Bekleidungshandels geschehen. Insbesondere die praktische Relevanz dieses Themas wird in der Untersuchung theoretisch möglicher und praktisch bedeutsamer Geschäftsmodelle aufgezeigt. Darüber hinaus tragen zahlreiche Interviews mit Entscheidungsträgern wichtiger Branchenakteure dazu bei, die Wirkung der Plattformökonomie auf die wirtschaftliche Realität des deutschen Bekleidungshandels darzustellen. So soll ein Beitrag für das wissenschaftliche Verständnis von Geschäftsmodellen in der Plattformökonomie geleistet werden. Zudem werden konkrete Empfehlungen für die Gestaltung des Geschäftsmodells erarbeitet