Search Results for author: Mary Lou Maher

Found 8 papers, 0 papers with code

AI and Identity

no code implementations29 Feb 2024 Sri Yash Tadimalla, Mary Lou Maher

In this context, This paper examines the intersection of AI and identity as a pathway to understand biases, inequalities, and ethical considerations in AI development and deployment.

The Tyranny of Possibilities in the Design of Task-Oriented LLM Systems: A Scoping Survey

no code implementations29 Dec 2023 Dhruv Dhamani, Mary Lou Maher

The paper discusses the implications of this lens, for the cross-pollination of research between LLM prompting and LLM-based multi-agent systems; and also, for the generation of synthetic training data based on existing prompting techniques in research.

Understanding User Perceptions, Collaborative Experience and User Engagement in Different Human-AI Interaction Designs for Co-Creative Systems

no code implementations27 Apr 2022 Jeba Rezwana, Mary Lou Maher

Typically, the AI in co-creative systems cannot communicate back to humans, limiting their potential to be perceived as partners rather than just a tool.

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

no code implementations15 Apr 2022 Jeba Rezwana, Mary Lou Maher

There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems.

Identifying Ethical Issues in AI Partners in Human-AI Co-Creation

no code implementations15 Apr 2022 Jeba Rezwana, Mary Lou Maher

Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners.

Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System

no code implementations24 Jun 2019 Pegah Karimi, Mary Lou Maher, Nicholas Davis, Kazjon Grace

This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning.

Evaluating Creativity in Computational Co-Creative Systems

no code implementations25 Jul 2018 Pegah Karimi, Kazjon Grace, Mary Lou Maher, Nicholas Davis

This paper provides a framework for evaluating creativity in co-creative systems: those that involve computer programs collaborating with human users on creative tasks.

Deep Learning for Identifying Potential Conceptual Shifts for Co-creative Drawing

no code implementations2 Jan 2018 Pegah Karimi, Nicholas Davis, Kazjon Grace, Mary Lou Maher

We present a system for identifying conceptual shifts between visual categories, which will form the basis for a co-creative drawing system to help users draw more creative sketches.

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