Augmented Cognition: Life as we don't know it DOI Open Access
Inês Hipólito

Published: Feb. 14, 2023

This paper proposes a framework for comprehending the integration of Artificial Intelligence (AI) as Augmented Cognition (AugCog). AugCog is viewed an emergent bio-cultural process, reflecting AI's design, implementation, and usage. Section 1 establishes smart societies complex system. 2 defends that development AI analogous to biological process niche construction. 3 defines socioculturally embodied expansion by which shaped shapes human experience, resulting in various forms AugCog. 4 highlights mixed realities such social media, neurotechnology, environments, illustrating its emergence from multiscale interdependent sociocultural perspectives. AugCog's perspective situates species state space evolution, signifying our existence individuals.

Language: Английский

Generative AI and human–robot interaction: implications and future agenda for business, society and ethics DOI

Bojan Obrenovic,

Xiao Gu, Guoyu Wang

et al.

AI & Society, Journal Year: 2024, Volume and Issue: unknown

Published: March 15, 2024

Language: Английский

Citations

47
Study on Harmonizing Human-Robot (Drone) Collaboration DOI

Tejaswini A. Puranik,

Nazeer Shaik, Ramu Vankudoth

et al.

Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 26

Published: Jan. 26, 2024

This chapter delves into the intricate dynamics and possibilities of fostering cohesive interactions between humans robots within collaborative environments. By examining current landscape human-robot collaboration, focus shifts towards identifying pivotal factors that facilitate seamless integration synergy these entities. Exploring technological advancements behavioral paradigms, this underscores significance intuitive interfaces, adaptive communication models, ergonomic design in augmenting cooperative interactions. Furthermore, it investigates challenges posed by varying cognitive capacities preferences, proposing strategies for harmonizing disparate elements to enhance efficiency effectiveness tasks. Through an interdisciplinary lens, work not only elucidates evolving engagements but also offers insights future trajectory environments where are paramount.

Language: Английский

Citations

26
Gender Stereotypes toward Non-gendered Generative AI: The Role of Gendered Expertise and Gendered Linguistic Cues DOI Creative Commons
Wen Duan, Nathan J. McNeese, Lingyuan Li

et al.

Proceedings of the ACM on Human-Computer Interaction, Journal Year: 2025, Volume and Issue: 9(1), P. 1 - 35

Published: Jan. 10, 2025

With rapid advancements in large language models (LLMs), generative AI (GenAI) is transforming people's life and work across various domains. Unlike previous technologies that are often feminized, most of these GenAI tools non-gendered, potentially preventing users from applying gender stereotypes. However, GenAI's use natural can evoke social perceptions including attribution, making it susceptible to associations. Using two online experiments, we explored how removal could mitigate individuals' stereotypes toward it, certain linguistic cues trigger even if non-gendered. We found the AI's did but only an extent. Additionally, gendered such as politeness, apologies, tentative (or lack thereof) non-gendered AI. contribute HCI CSCW research by providing a timely investigation into agents, one first examine profound impact style on users' attributions characteristics GenAI. Our findings shed light purposeful responsible design prioritizes promoting equality, thereby ensuring technological align with evolving values.

Language: Английский

Citations

2
Mitigating Gender Stereotypes Toward AI Agents Through an eXplainable AI (XAI) Approach DOI Creative Commons
Wen Duan, Nathan J. McNeese, Guo Freeman

et al.

Proceedings of the ACM on Human-Computer Interaction, Journal Year: 2024, Volume and Issue: 8(CSCW2), P. 1 - 35

Published: Nov. 7, 2024

People often apply gender stereotypes toward computerized agents. Rather than challenging these stereotypes, modern AI technologies controversially use them in creating agents that underline stereotypical gendered roles. This approach thus further reinforces the male-dominated societal norms and disenfranchises women non-binary individuals. While this issue has raised concerns HCI CSCW communities, still little is known regarding how to mitigate negative impacts of embedding In paper, we propose an eXplainable (XAI) mitigating individuals' We conducted online video vignette experiment with 350 participants randomly assigned one eighteen conditions a 3 (gender agent: woman, man, gender-neutral) x (task gender: feminine, masculine, neutral) 2 (presence or absence explanation) between-subjects design. Our findings indeed suggest XAI helped avoid applying agents, by increasing their understanding agent came its decision decreasing rating agent's humanlikeness. contribute research providing timely investigation into state-of-the-art advancing empirical cognitive processes mechanisms underlying stereotypes. also demonstrate can effectively suppress application social characteristics (i.e., stereotypes) disrupting said processes. Insights from study inform future should be designed create progressive reality will gradually reshape humans' experience ingrained ideologies.

Language: Английский

Citations

5
Enactive artificial intelligence: subverting gender norms in human-robot interaction DOI Creative Commons
Inês Hipólito, Katie Winkle, Merete Lie

et al.

Frontiers in Neurorobotics, Journal Year: 2023, Volume and Issue: 17

Published: June 8, 2023

This paper presents Enactive Artificial Intelligence (eAI) as a gender-inclusive approach to AI, emphasizing the need address social marginalization resulting from unrepresentative AI design.The study employs multidisciplinary framework explore intersectionality of gender and technoscience, focusing on subversion norms within Robot-Human Interaction in AI.The results reveal development four ethical vectors, namely explainability, fairness, transparency, auditability, essential components for adopting an inclusive stance promoting AI.By considering these we can ensure that aligns with societal values, promotes equity justice, facilitates creation more just equitable society.

Language: Английский

Citations

13
Mind Meets Robots: A Review of EEG-Based Brain-Robot Interaction Systems DOI Creative Commons
Yuchong Zhang, Nona Rajabi, Farzaneh Taleb

et al.

International Journal of Human-Computer Interaction, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 32

Published: March 17, 2025

Language: Английский

Citations

0
Designing Robot Identity: The Role of Voice, Clothing, and Task on Robot Gender Perception DOI Creative Commons
Nathaniel Dennler,

Mina Kian,

Stefanos Nikolaidis

et al.

International Journal of Social Robotics, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Abstract Perceptions of gender have a significant impact on human-human interaction, and has wide-reaching social implications for robots intended to interact with humans. This work explored two flexible modalities communicating in robots–voice appearance–and we studied their individual combined influences robot’s perceived gender. We evaluated the perception through three online studies. First, conducted voice design study (n = 65) robot voices by varying speaker identity pitch. Second, clothing 93) designed different tasks. Finally, building results first studies, completed large integrative video 273) involving human-robot interaction found that can be used reliably establish gender, combining these effects Taken together, inform as interacting components perceptions

Language: Английский

Citations

0
Can a gender-ambiguous voice reduce gender stereotypes in human-robot interactions? DOI
Ilaria Torre, Erik Lagerstedt, Nathaniel Dennler

et al.

Published: Aug. 28, 2023

When deploying robots, its physical characteristics, role, and tasks are often fixed. Such factors can also be associated with gender stereotypes among humans, which then transfer to the robots. One factor that induce gendering but is comparatively easy change robot's voice. Designing voice in a way interferes fixed might therefore reduce human-robot interaction contexts. To this end, we have conducted video-based online study investigate how inspire of robot interact. In particular, investigated giving gender-ambiguous affect perception robot. We compared assessments (n=111) videos body presentation occupation mis/matched human stereotypes. found evidence endowed stereotypically feminine or masculine attributes. The results inform more just design while opening new questions regarding phenomenon gendering.

Language: Английский

Citations

9
More Than Binary: Transgender and Non-binary Perspectives on Human Robot Interaction DOI Creative Commons
Michael Stolp-Smith,

Tom Williams

Published: March 10, 2024

Research has shown that gendered robot designs prompt users to carry their gender biases into human-robot interactions. Yet avoiding in interaction may be infeasible, as humans readily robots based on factors like name, voice, and pronouns. One solution this challenge could use an intentionally agender design. it is unclear whether trans, non-binary, or otherwise nonconforming people would view a positive inclusive step, appropriative problematic. In fact, little known about trans nonbinary perspectives interaction, which have not been previously studied. work, we thus present the first study of non-binary design, with particular focus perceptions Our results suggest accept depicted agender, design strategy help normalize non-cisgender identities. our also highlight key risks posed by strategy, including backlash, caricature, dehumanization, show how those are shaped political economic factors.

Language: Английский

Citations

3
The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language DOI Creative Commons
Linus Nwankwo, Elmar Rueckert

Published: March 11, 2024

In recent years, autonomous agents have surged in real-world environments such as our homes, offices, and public spaces. However, natural human-robot interaction remains a key challenge. this paper, we introduce an approach that synergistically exploits the capabilities of large language models (LLMs) multimodal vision-language (VLMs) to enable humans interact naturally with robots through conversational dialogue. We leveraged LLMs decode high-level instructions from abstract them into precise robot actionable commands or queries. Further, utilised VLMs provide visual semantic understanding robot's task environment. Our results 99.13% command recognition accuracy 97.96% execution success show can enhance applications. The video demonstrations paper be found at https://osf.io/wzyf6 code is available GitHub repository.

Language: Английский

Citations

3