Perceived Intelligence and Perceived Anthropomorphism of Personal Intelligent Agents: Scale Development and Validation

Personal intelligent agents are systems that are autonomous, aware of their environment, continuously learning and adapting to change, able to interact using natural language and capable of completing tasks within a favorable timeframe in a proactive manner. Examples include Siri and Alexa. Several unique characteristics distinguish these agents from other traditional information systems. Of particular interest in this work are characteristics of intelligence and anthropomorphism. This paper describes the process of developing two new measures with satisfactory psychometric properties that can be adapted by researchers to assess the users’ perceptions of intelligence and anthropomorphism of PIAs. The measures are validated using data collected from 232 experienced PIA users

Working on Low-Paid Micro-Task Crowdsourcing Platforms: An Existence, Relatedness and Growth View

Low-paid micro-task crowdsourcing sites present a new workplace that has been increasingly popular. Given recently reported crowd demographics and relevant literature we believe that the understanding of higher-level motivations for workers on these sites is still an under-explored area. Using a qualitative research methodology, we explore workers’ motivations in their natural settings. We conduct interviews with Amazon Mechanical Turk workers and analyze the data through the lens of Alderfer’s existence, relatedness, and growth theory. Our paper contributes new insights to the crowdsourcing literature, specifically that low-paid micro-task crowdsourcing workers aim to satisfy relatedness (connectedness and societal impact), existence (income, basic rights and rewarding experience), and growth needs (impact on self and skill development). We also discuss three additional categories that emerge from our data: sense of control and power, having fun and passing the time. Our findings provide new contributions that are of high relevance to both theory and practice

A Value Sensitive Design Perspective on AI Biases

Artificial Intelligence (AI) technology has made profound impacts in our society but concerns about AI biases are rising. This paper classifies AI-related biases and proposes strategies to tackle them. To inform our study, we review AI research on human values to identify three categories of AI biases: pre-existing, technical, and emergent. Informed by the value sensitive design (VSD) framework, we then map the AI biases to the three phases (conceptual, empirical, and technical) of VSD investigation. Our analysis shows that both conceptual and empirical investigations are helpful for addressing pre-existing bias, technical investigation for technical bias, and both technical and empirical investigations for emerging bias. The paper highlights that to effectively tackle AI-related biases, it is important for AI developers and the user community to understand human values in an AI context and to advocate for developing AI-specific value-oriented standards that are agreed upon and adopted by all stakeholders

The Crowd on the Assembly Line: Designing Tasks for a Better Crowdsourcing Experience

Leveraging crowd potentials through low paid crowdsourcing micro-tasks has attracted great attention in the last decade as it proves to be a powerful new paradigm to get large amounts of work done quickly. A main challenge for crowdsourcers has been to design tasks that trigger optimum outputs from the crowd while providing crowdsourcees with an experience that would attract them to the platform in the future. Drawing mainly from expectancy theory and the motivation through design of work model, we develop and test a theoretical framework to explore the impact of extrinsic reward valence and perceived task characteristics on perceived output measures in crowdsourcing contexts. We specifically focus on the impact of three crowdsourcing task dimensions: autonomy, skill use, and meaningfulness. Our findings provide support for our model and suggest ways to improve task design, use extrinsic rewards, and provide an enhanced crowdsourcing experience for participants

Investigating Personal Intelligent Agents in Everyday Life through a Behavioral Lens

Personal intelligent agents (PIA), such as Apple’s Siri, Google Now, Facebook’s M, and Microsoft’s Cortana, are pervading our lives. These systems are taking the shape of a companion, and acting on our behalf to help us manage our everyday activities. The proliferation of these PIAs is largely due to their wide availability on mobile devices which themselves have become commonly available for billions of people. Our continuous interaction with these PIAs is impacting our sense of self, sense of being human, perception of technology, and relationships with others. The Information Systems (IS) literature on PIAs has been scarce. In this dissertation, we investigate the users’ relationship with PIAs in pre- and post-adoption contexts. We create and develop scales for two new constructs, perceived intelligence and perceived anthropomorphism, which are essential to investigate the holistic users’ experience with PIAs and similar systems. We also investigate perceptions of self-extension and possible antecedents of self-extension for the first time in IS. Additionally, we explore design issues with PIAs and examine voice and humor, which are independently present in currently available PIAs. Humor is a pervasive social phenomenon that shapes the dynamics of human interactions and is investigated for the first time in an IS experiment. We find that the current adoption and continuance of use models may not be sufficient to investigate the adoption and continuance of use of PIAs and similar systems since they do not capture the whole interaction between the user and the PIA. Our results underline the important role of the new perceptions, the utilitarian and hedonic aspects of use, and the cognitive and emotional trust in these social actors. Our findings highlight an astonishing change in the users’ perception of technology from being a tool distant from the self to a tool that they develop emotional connections with and consider part of their self-identity. This dissertation’s findings provide interesting theoretical and practical implications and stress a changing relationship between the user and the technology with this new wave of systems. Our research answers important questions in the context of PIAs’ adoption and continued used, contributes to various streams in the IS literature (adoption, continuance of use, trust, intelligence, anthropomorphism, dual-purpose IS, and self-extension) and creates new opportunities for future research

Pathways to Meaningful Work on Micro-Task Crowdsourcing Platforms

In this paper we investigate different mechanisms for meaningful work on micro-task crowdsourcing platforms. We adapt the pathways to meaningful work framework from the organizational behavior literature to build a theoretical model in the context of micro-task crowdsourcing. We conduct a field study with Amazon Mechanical Turk workers on a simulated crowdsourcing platform to test the research model. Our results show that motivations to connect with the self (self-connection pathway) and others (unification pathway) form two major paths to meaningful work on these platforms. Our findings contribute to the work meaningfulness research in information systems and confirm that how workers derive meaning on micro-task crowdsourcing platforms that center on the information technology (IT) artifact is comparable to traditional organizational settings

Does the Accent of an Intelligent Conversational Agent Matter?

Accents are an extensively researched topic in social psychology. Research shows that individuals with nonnative accents are likely to experience stigmatization. Prior research has also shown that humans treat computers as they would other humans and that voice agents’ accents affect perceptions and evaluations of these systems. As the market for intelligent conversational agents (ICAs) like Siri and Alexa continues to grow, it becomes increasingly relevant to understand how ICA accents are used and how they might be utilized to improve the user relationship. Guided by and looking to address several gaps in the literature, two studies are proposed. The first aims to collect information on ICA accent functionality usage while the second aims to explore if human biases related to accents transfer to ICAs. As a novel area of research, findings of the research would impact future design and development of ICAs, as well as contribute to existing literature on ICAs

Are Anthropomorphic Intelligent Agents More Intelligent?

As the usage of intelligent agents increases, understanding end-user perceptions becomes necessary to enhance human-computer interactions. While perceived anthropomorphism and perceived intelligence have been studied previously, there has not been any direct research showing the effect perceived anthropomorphism has on the end-user’s perception of intelligence. The focus of this paper is to measure how the perceived intelligence of an intelligent agent changes due to different levels of congruency between the agent and the human category schema. The results of this proposed research could help designers choose anthropomorphic features that can increase users’ perceived intelligence of intelligent agents