A Comparative Usability Study of Two-Factor Authentication

Two-factor authentication (2F) aims to enhance resilience of password-based authentication by requiring users to provide an additional authentication factor, e.g., a code generated by a security token. However, it also introduces non-negligible costs for service providers and requires users to carry out additional actions during the authentication process. In this paper, we present an exploratory comparative study of the usability of 2F technologies. First, we conduct a pre-study interview to identify popular technologies as well as contexts and motivations in which they are used. We then present the results of a quantitative study based on a survey completed by 219 Mechanical Turk users, aiming to measure the usability of three popular 2F solutions: codes generated by security tokens, one-time PINs received via email or SMS, and dedicated smartphone apps (e.g., Google Authenticator). We record contexts and motivations, and study their impact on perceived usability. We find that 2F technologies are overall perceived as usable, regardless of motivation and/or context of use. We also present an exploratory factor analysis, highlighting that three metrics -- ease-of-use, required cognitive efforts, and trustworthiness -- are enough to capture key factors affecting 2F usability.Comment: A preliminary version of this paper appears in USEC 201

On Behalf of a Bi-Level Account of Trust

A bi-level account of trust is developed and defended, one with relevance in ethics as well as epistemology. The proposed account of trust—on which trusting is modelled within a virtue-theoretic framework as a performance-type with an aim—distinguishes between two distinct levels of trust, apt and convictive, that take us beyond previous assessments of its nature, value, and relationship to risk assessment. While Ernest Sosa (2009; 2015; 2017), in particular, has shown how a performance normativity model may be fruitfully applied to belief, my objective is to apply this kind of model in a novel and principled way to trust. I conclude by outlining some of the key advantages of the performance-theoretic bi-level account of trust defended over more traditional univocal proposals

Know What Not To Know: Users' Perception of Abstaining Classifiers

Machine learning systems can help humans to make decisions by providing decision suggestions (i.e., a label for a datapoint). However, individual datapoints do not always provide enough clear evidence to make confident suggestions. Although methods exist that enable systems to identify those datapoints and subsequently abstain from suggesting a label, it remains unclear how users would react to such system behavior. This paper presents first findings from a user study on systems that do or do not abstain from labeling ambiguous datapoints. Our results show that label suggestions on ambiguous datapoints bear a high risk of unconsciously influencing the users' decisions, even toward incorrect ones. Furthermore, participants perceived a system that abstains from labeling uncertain datapoints as equally competent and trustworthy as a system that delivers label suggestions for all datapoints. Consequently, if abstaining does not impair a system's credibility, it can be a useful mechanism to increase decision quality

Conversational AI Agents: Investigating AI-Specific Characteristics that Induce Anthropomorphism and Trust in Human-AI Interaction

The investment in AI agents has steadily increased over the past few years, yet the adoption of these agents has been uneven. Industry reports show that the majority of people do not trust AI agents with important tasks. While the existing IS theories explain users’ trust in IT artifacts, several new studies have raised doubts about the applicability of current theories in the context of AI agents. At first glance, an AI agent might seem like any other technological artifact. However, a more in-depth assessment exposes some fundamental characteristics that make AI agents different from previous IT artifacts. The aim of this dissertation, therefore, is to identify the AI-specific characteristics and behaviors that hinder and contribute to trust and distrust, thereby shaping users’ behavior in human-AI interaction. Using a custom-developed conversational AI agent, this dissertation extends the human-AI literature by introducing and empirically testing six new constructs, namely, AI indeterminacy, task fulfillment indeterminacy, verbal indeterminacy, AI inheritability, AI trainability, and AI freewill

Ethical and Governance Challenges in Population Biobanking: the case of the global Anti-Doping Administration & Management System

This study is an ethical analysis of the governance and regulatory dimensions of biobanking with specific reference to the Anti-Doping Administration Management System (ADAMS) of the global regulator of anti-doping in sports, the World Anti-Doping Agency (WADA). The study focuses on four key ethico-governance issues: (i) consent; (ii) benefit-sharing; (iii) harmonization of ethics and governance; and (iv) conditions for the secondary research uses of data originally collected for doping control purposes. It is argued that the consent process prior to data collection, storage and analysis is problematic, since athletes may not refuse the request to provide data sought by anti-doping authorities without forfeiting their eligibility to compete. The process requires simultaneous permission for research and testing which creates ambiguity, compounded by the unequal relationship between athletes and WADA. A range of alternative models are explored and a case is made for an approach that combines broad consent with iterative, or ‘reflexive’ governance and stakeholder involvement including education around research. Furthermore, ethical issues remain concerning governance and regulation for population research and use of data more generally between legal jurisdictions and within diverse populations. It is also argued that WADA’s claim to harmonization through its operational methods, regulation and governance, is not sufficiently well-defined outside of specific legal uses and is therefore too blunt a tool for ethical governance in global sport contexts. This thesis proposes reforms to existing WADA processes including consent processes and moves toward more reflexive governance frameworks that allow contextual nuance and iterative development, respecting differing needs within a shared structure. Specific recommendations are made to enhance accountability for potential secondary uses of ADAMS data for research. A distinction is drawn between anti-doping and broader biomedical research in developing ethically justifiable pathways that reduce the potential for coercion and empower athletes as contributors and potential beneficiaries

An Empirical Examination of the Antecedents and Consequences of Investment Patterns in Crowd-Funded Markets.

Crowd-funded markets have recently emerged as a novel source of capital for entrepreneurs. As the economic potential of these markets is now being realized, they are beginning to go mainstream, a trend reflected by the explicit attention crowdfunding has received in the American Jobs Act as a potential avenue for economic growth, as well as the recent focus that regulators such as the U.S. Securities and Exchange Commission have placed upon it. Although the formulation of regulation and policy surrounding crowd-funded markets is becoming increasingly important, the behavior of crowdfunders, an important aspect that must be considered in this formulation effort, is not yet well understood. A key factor that can influence the behavior of crowd funders is information on prior contribution behavior, including the amount and timing of others\u27 contributions, which is published for general consumption. With that in mind, in this study, we empirically examine social influence in a crowd-funded marketplace for online journalism projects, employing a unique data set that incorporates contribution events and Web traffic statistics for approximately 100 story pitches. This data set allows us to examine both the antecedents and consequences of the contribution process. First, noting that digital journalism is a form of public good, we evaluate the applicability of two competing classes of economic models that explain private contribution toward public goods in the presence of social information: substitution models and reinforcement models. We also propose a new measure that captures both the amount and the timing of others\u27 contribution behavior: contribution frequency (dollars per unit time). We find evidence in support of a substitution model, which suggests a partial crowding-out effect, where contributors may experience a decrease in their marginal utility from making a contribution as it becomes less important to the recipient. Further, we find that the duration of funding and, more importantly, the degree of exposure that a pitch receives over the course of the funding process, are positively associated with readership upon the story\u27s publication. This appears to validate the widely held belief that a key benefit of the crowdfunding model is the potential it offers for awareness and attention-building around causes and ventures. This last aspect is a major contribution of the study, as it demonstrates a clear linkage between marketing effort and the success of crowd-funded projects

Imbalance and the state of research : emergent challenges to scientific independence & objectivity

In this dissertation, I provide a conceptual framework for the analysis of impediments to the independence and objectivity of the overall state of research. I argue that we need a new approach beyond existing conceptions of scientific objectivity. This is because—when concerned with states of research—not only do we have to account for problems with individual findings but also with imbalance, that is the neglect or overabundance of specific types of research relative to others. In the first of the three parts of the dissertation, I define the concept “state of research”, and introduce the idea of its imbalance. The latter concept is based on a discussion of various examples from the literature. In the second part, I analyze three major concepts that can help explain why these cases can and should be considered problems for the independence and objectivity of science: epistemic trustworthiness, productiveness, and justice. These three normative criteria and their interrelations form the general structure of the conceptual framework. In the third part, I operationalize the criteria to show that and how they can be applied to the cases introduced in the first part, and present the results. I conclude with a discussion of the implications for higher-order concepts such as bias, the independence of science, and objectivity. I argue that when we look at the application of these concepts to the state of research, in addition to purely epistemic considerations, we also have to emphasize the social responsibility of science

Strategically Managing the Value Creation and Productivity Paradox of Artificial Intelligence : The General Purpose Technology View

ABSTRACT This doctoral dissertation explores the strategic management of artificial intelligence as a general purpose technology and its value creation in the context of multiple industries. I study what makes AI-based value creation challenging from the management and organization perspective despite the high technological performance of AI. I analyze this through five sub-research questions, and by applying grounded theory. Empirically, I turn to 34 AI solution developers from 18 different industries who have both technical and business understanding of using AI. The AI solution developers suit this study because of their skills, capabilities, and power position to shape the present and future through the combined use of machine learning (ML) and other AI related technologies that are already impacting our daily lives in and out of work context. The extant literature on AI in premium outlets on general management and organizational studies can be typified into five AI use phases: 1) antecedents of AI use, 2) AI use, 3) (empirical) impacts of AI use, 4) expected (cumulative) impacts of AI, and 5) AI-related paradigm shift. The five sub-research questions of this doctoral dissertation explore the definition of AI and the use phases 1-4 by approaching AI as the subject of study. The fifth AI use phase is excluded from this study as it would require using AI also as the research method. The main contributions of this doctoral thesis include giving an overview of AI in management and organization, and pre-theoretically identifying the technical and socially constructed decision-making criteria for AI investments, six AI use types, how empirical AI impacts have been measured, what temporal dimensions are expected to be impacted by AI, and what AI strategies organizations have already adopted to create AI-based value and overcome its productivity paradox. KEYWORDS: Artificial intelligence, machine learning, strategic management, value creation, automation, augmentation, hybrid intelligence, conjoined agencyTIIVISTELMÄ Tämä väitöskirja keskittyy tekoälypohjaisen arvonluonnin strategiseen johtamiseen yli teollisuudenalarajojen. Lähestyn tekoälyä korkean suorituskyvyn omaavana yleiskäyttöisenä teknologiana ja analysoin ilmiö- ja aineistopohjaisesti sitä, mikä tekee tekoälypohjaisesta arvonluonnista silti haastavaa johtamisen ja organisoinnin näkökulmasta viiden alatutkimuskysymyksen avulla. Haastattelin tätä työtä varten 34 tekoälyratkaisuja 18 eri teollisuudenalalla kehittävää asiantuntijaa. He sopivat haastateltaviksi, koska heillä on alan osaamista sekä teknisestä että käytännön sovellusten näkökulmasta, ja koska heillä on valta-asema kehittää koneoppimiseen ja muihin tekoälyteknologoihin pohjautuvia ratkaisuja, jotka jo vaikuttavat päivittäiseen elämäämme työelämässä ja sen ulkopuolella. Yleisen johtamisen ja organisaatiotutkimuksen huippujulkaisuista kerätty kirjallisuus voidaan jakaa viiteen tekoälyn käyttövaiheeseen: 1) tekoälyn käyttöä edeltävät tekijät, 2) tekoälyn käyttö, 3) tekoälyn (empiiriset) vaikutukset, 4) odotettavissa olevat tekoälyn (kumulatiiviset) vaikutukset, sekä 5) tekoälyyn liittyvät paradigman muutokset. Tämän väitöskirjan viisi alatutkimuskysymystä keskittyvät tekoälyn määritelmään sekä tekoälyn käyttövaiheisiin 1-4. Viides tekoälyn käyttövaihe on jätetty tämän tutkimuksen ulkopuolelle, koska se vaatisi tekoälyn käyttöä myös tutkimusmetodina. Tämän tutkimuksen päätuotokset luovat yleiskuvan tekoälystä johtamisen ja organisoinnin kirjallisuudessa. Empiiriset tulokset tyypittelevät investointi-päätöksiin vaikuttavia tekijöitä, sekä kuusi erilaista tekoälyn käyttötapausta. Analysoin, miten tekoälyn vaikutuksia on mitattu, mihin aikaan liittyviin tekijöihin tekoälyn odotetaan vaikuttavan, ja mitä tekoälystrategioita organisaatiot ovat jo omaksuneet luodakseen arvoa ja ylittääkseen tekoälyn tuottavuusparadoksin. ASIASANAT: Artificial intelligence, machine learning, strategic management, value creation, automation, augmentation, hybrid intelligence, conjoined agenc

Formal Models of the Network Co-occurrence Underlying Mental Operations

International audienceSystems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-uncon-strained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition

Action Research

Action research is a common journey for graduate students in education and other human science fields. This book attempts to meet the needs of graduate students, in-service teachers, and any other educators interested in action research and/or self-study. The chapters of this book draw on our collective experiences as educators in a variety of educational contexts, and our roles guiding educator/researchers in various settings. All of our experiences have enabled us to question and refine our own understanding of action research as a process and means for pedagogical improvement. The primary purpose of this book is to offer clear steps and practical guidance to those who intend to carry out action research for the first time. As educators begin their action research journey, we feel it is vital to pose four questions: 1) What is action research, and how is it distinct from other educational research?; 2) When is it appropriate for an educator to conduct an action research project in their context?; 3) How does an educator conduct an action research project?; 4) What does an educator do with the data once the action research project has been conducted? We have attempted to address all four questions in the chapters of this book.https://newprairiepress.org/ebooks/1034/thumbnail.jp