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Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach

Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, the AI agent produces a response. However, the interaction dynamics are more complex within group settings. How should an agent behave in th…

Stephanie Houde et al.IBM

Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation

AI systems powered by large language models can act as capable assistants for writing and editing. In these tasks, the AI system acts as a co-creative partner, making novel contributions to an artifact-under-creation alongside its human partner(s). One question that arises in these scenarios is the extent to which AI…

Jessica He et al.IBM

Design Principles for Generative AI Applications

Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need for guidance on how to design user experiences that foster effective and safe use. We present six principles for the design of generative…

Justin D. Weisz et al.IBM

Investigating Explainability of Generative Models for Code through Scenario-based Design

What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI…

Jiao Sun et al.University of Southern California

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Better Together? An Evaluation of AI-Supported Code Translation

Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study wit…

Justin D. Weisz et al.IBM

Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

Automated Machine Learning (AutoML) is a rapidly growing set of technology to automate the model development pipeline, by automatically searching the model space and generating candidate models. A critical final step of AutoML is to have the users, often data scientists, selecting the final model from dozens of candid…

Shweta Narkar et al.Rensselaer Polytechnic Institute

Perfection Not Required? Human-AI Partnerships in Code Translation

Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised neural machine translation (NMT), have recently been applied to the task of generating source code, translating it from one programming langu…

Justin D. Weisz et al.IBM

Expanding Explainability: Towards Social Transparency in AI systems

As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approach…

Upol Ehsan et al.Georgia Institute of Technology

AutoDS: Towards Human-Centered Automation of Data Science

Data science (DS) projects often follow a \textit{lifecycle} that consists of laborious \textit{tasks} for data scientists and domain experts (e.g., data exploration, model training, etc.). Only till recently, machine learning(ML) researchers have developed promising automation techniques to aid data workers in these…

Dakuo Wang et al.IBM

BigBlueBot: Teaching Strategies for Successful Human-Agent Interactions

Chatbots are becoming quite popular, with many brands developing conversational experiences using platforms such as IBM's Watson Assistant and Facebook Messenger. However, previous research reveals that users' expectations of what conversational agents can understand and do far outpace their actual technical capabilit…

Justin D. Weisz et al.IBM

Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and p…

Dakuo Wang et al.IBM

Thinking Too Classically: Toward a Research Agenda for Human-Quantum Computer Interaction

Quantum computing is a fundamentally different way of performing computation than classical computing. Many problems that are considered hard for classical computers may have efficient solutions using quantum computers. Recently, technology companies including IBM, Microsoft, and Google have invested in developing bot…

Zahra Ashktorab et al.IBM

Resilient Chatbots: Repair Strategy Preferences for Conversational Breakdowns

Text-based conversational systems, also referred to as chatbots, have grown widely popular. Current natural language understanding technologies are not yet ready to tackle the complexities in conversational interactions. Breakdowns are common, leading to negative user experiences. Guided by communication theories, we…

Zahra Ashktorab et al.IBM

Paper Title	Authors	Research Topics
Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, the AI agent produces a response. However, the interaction dynamics are more complex within group settings. How should an agent behave in th…	SH Stephanie Houde et al.IBM
Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation AI systems powered by large language models can act as capable assistants for writing and editing. In these tasks, the AI system acts as a co-creative partner, making novel contributions to an artifact-under-creation alongside its human partner(s). One question that arises in these scenarios is the extent to which AI…	JH Jessica He et al.IBM
Design Principles for Generative AI Applications Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need for guidance on how to design user experiences that foster effective and safe use. We present six principles for the design of generative…	JW Justin D. Weisz et al.IBM
Investigating Explainability of Generative Models for Code through Scenario-based Design What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI…	JS Jiao Sun et al.University of Southern California
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
Better Together? An Evaluation of AI-Supported Code Translation Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study wit…	JW Justin D. Weisz et al.IBM
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML Automated Machine Learning (AutoML) is a rapidly growing set of technology to automate the model development pipeline, by automatically searching the model space and generating candidate models. A critical final step of AutoML is to have the users, often data scientists, selecting the final model from dozens of candid…	SN Shweta Narkar et al.Rensselaer Polytechnic Institute
Perfection Not Required? Human-AI Partnerships in Code Translation Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised neural machine translation (NMT), have recently been applied to the task of generating source code, translating it from one programming langu…	JW Justin D. Weisz et al.IBM
emoji_events Expanding Explainability: Towards Social Transparency in AI systems As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approach…	UE Upol Ehsan et al.Georgia Institute of Technology
AutoDS: Towards Human-Centered Automation of Data Science Data science (DS) projects often follow a \textit{lifecycle} that consists of laborious \textit{tasks} for data scientists and domain experts (e.g., data exploration, model training, etc.). Only till recently, machine learning(ML) researchers have developed promising automation techniques to aid data workers in these…	DW Dakuo Wang et al.IBM
BigBlueBot: Teaching Strategies for Successful Human-Agent Interactions Chatbots are becoming quite popular, with many brands developing conversational experiences using platforms such as IBM's Watson Assistant and Facebook Messenger. However, previous research reveals that users' expectations of what conversational agents can understand and do far outpace their actual technical capabilit…	JW Justin D. Weisz et al.IBM
Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and p…	DW Dakuo Wang et al.IBM	AI
Thinking Too Classically: Toward a Research Agenda for Human-Quantum Computer Interaction Quantum computing is a fundamentally different way of performing computation than classical computing. Many problems that are considered hard for classical computers may have efficient solutions using quantum computers. Recently, technology companies including IBM, Microsoft, and Google have invested in developing bot…	ZA Zahra Ashktorab et al.IBM
Resilient Chatbots: Repair Strategy Preferences for Conversational Breakdowns Text-based conversational systems, also referred to as chatbots, have grown widely popular. Current natural language understanding technologies are not yet ready to tackle the complexities in conversational interactions. Breakdowns are common, leading to negative user experiences. Guided by communication theories, we…	ZA Zahra Ashktorab et al.IBM