Designing Women: Essentializing Femininity in AI Linguistics

Since the eighties, feminists have considered technology a force capable of subverting sexism because of technology’s ability to produce unbiased logic. Most famously, Donna Haraway’s “A Cyborg Manifesto” posits that the cyborg has the inherent capability to transcend gender because of its removal from social construct and lack of loyalty to the natural world. But while humanoids and artificial intelligence have been imagined as inherently subversive to gender, current artificial intelligence perpetuates gender divides in labor and language as their programmers imbue them with traits considered “feminine.” A majority of 21st century AI and humanoids are programmed to fit female stereotypes as they fulfill emotional labor and perform pink-collar tasks, whether through roles as therapists, query-fillers, or companions. This paper examines four specific chat-based AI --ELIZA, XiaoIce, Sophia, and Erica-- and examines how their feminine linguistic patterns are used to maintain the illusion of emotional understanding in regards to the tasks that they perform. Overall, chat-based AI fails to subvert gender roles, as feminine AI are relegated to the realm of emotional intelligence and labor

Educational Scaffolding for Students Stuck in a Virtual World

Virtual worlds provide students with educational opportunities to explore and have experiences that are difficult to provide in reality. However, ensuring that students stay motivated and on task is important if the learning goals are to be achieved. Building on the findings of previous studies involving agent-based virtual worlds, adaptive collaborative learning and intelligent agents, we have designed an empathic intelligent virtual agent that provides educational scaffolding to encourage and support the students to understand what they are learning with less frustration. We have identified models of ‘stuck’ behaviour and corresponding empathic response patterns that we have incorporated into the behaviours of the intelligent virtual agents in the XXX Virtual World for science inquiry

Breaking In: Female Intelligence and Agency in British Children's Fantasy Literature

Honors (Bachelor's)EnglishUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/112126/1/macarp.pd

Conversing with personal digital assistants: on gender and artificial intelligence

This paper aims to explore the relationship between gender and artificial intelligence, seeking to understand how and why chatbots and digital assistants appear to be mostly female. To this end, it begins by addressing artificial intelligence and the questions that emerge with its evolution and integration in our daily lives. It then approaches the concept of gender in light of a binary framework, focusing on femininity. These topics are then related, in order to shed some light on how chatbots and digital assistants tend to display feminine attributes. In an attempt to observe these aspects, an analysis of Alexa, Cortana and Siri is developed, focusing on their anthropomorphization, the tasks they perform and their interactions. Complementing this discussion, the project Conversations with ELIZA is presented as an exploration of femininity in AI, through the development of four chatbots integrated into a web-based platform, each performing specific tasks and simulating particular personalities, with the purpose of emphasizing feminine roles and stereotypes. In this manner, this study aims to understand and explore how gender relates to AI, why femininity seems to be often present in AI and which gender roles or stereotypes are reinforced in this process

Understanding the Digital Companions of Our Future Generation

The main protagonist in Kazuo Ishiguro’s latest novel is Klara, an artificial friend whose existential goal is to be children’s companion. Some aspects of this fictional narrative have begun to gradually enter our daily lives. Products reminiscent of Klara are available abundantly on the market: smart toys, adaptive learning applications, and companion robots. Children can relate to these products and perform activities together with them. Preliminary research has shown fundamental differences between existing technologies and these emerging children’s digital companions. However, we still do not know much about their benefits and risks. This paper explores different and even contradicting perspectives on the phenomenon. We present the discussion from four perspectives - temporality, use, trust and ethics, and sociotechnical design - and conclude the paper with an agenda for interdisciplinary IS research. The agenda points to the needs for a psychological, medical, engineering, and temporal research community to understand this emerging sociotechnical phenomenon and design its future for the better

To Affinity and Beyond: Interactive Digital Humans as a Human Computer Interface

The field of human computer interaction is increasingly exploring the use of more natural, human-like user interfaces to build intelligent agents to aid in everyday life. This is coupled with a move to people using ever more realistic avatars to represent themselves in their digital lives. As the ability to produce emotionally engaging digital human representations is only just now becoming technically possible, there is little research into how to approach such tasks. This is due to both technical complexity and operational implementation cost. This is now changing as we are at a nexus point with new approaches, faster graphics processing and enabling new technologies in machine learning and computer vision becoming available. I articulate the issues required for such digital humans to be considered successfully located on the other side of the phenomenon known as the Uncanny Valley. My results show that a complex mix of perceived and contextual aspects affect the sense making on digital humans and highlights previously undocumented effects of interactivity on the affinity. Users are willing to accept digital humans as a new form of user interface and they react to them emotionally in previously unanticipated ways. My research shows that it is possible to build an effective interactive digital human that crosses the Uncanny Valley. I directly explore what is required to build a visually realistic digital human as a primary research question and I explore if such a realistic face provides sufficient benefit to justify the challenges involved in building it. I conducted a Delphi study to inform the research approaches and then produced a complex digital human character based on these insights. This interactive and realistic digital human avatar represents a major technical undertaking involving multiple teams around the world. Finally, I explored a framework for examining the ethical implications and signpost future research areas

Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables