Shaping inclusive representations that embrace diversity and ensure fair participation and reflections of values is at the core of many conversation-based models. However, many existing methods rely on surface inclusion using mention of user demographics or behavioral attributes of social groups. Such methods overlook the nuanced, implicit expression of opinion embedded in conversations. Furthermore, the over-reliance on overt cues can exacerbate misalignment and reinforce harmful or stereotypical representations in model outputs. Thus, we took a step back and recognized that equitable inclusion needs to account for the implicit expression of opinion and use the stance of responses to validate the normative alignment. This study aims to evaluate how opinions are represented in NLP or computational models by introducing an alignment evaluation framework that foregrounds implicit, often overlooked conversations and evaluates the normative social views and discourse. Our approach models the stance of responses as a proxy for the underlying opinion, enabling a considerate and reflective representation of diverse social viewpoints. We evaluate the framework using both (i) positive-unlabeled (PU) online learning with base classifiers, and (ii) instruction-tuned language models to assess post-training alignment. Through this, we provide a principled and structured lens on how implicit opinions are (mis)represented and offer a pathway toward more inclusive model behavior.

Robots and other artificial intelligence (AI) systems are widely perceived as moral agents responsible for their actions. As AI proliferates, these perceptions may become entangled via the moral spillover of attitudes towards one AI to attitudes towards other AIs. We tested how the seemingly harmful and immoral actions of an AI or human agent spill over to attitudes towards other AIs or humans in two preregistered experiments. In Study 1 (N = 720), we established the moral spillover effect in human-AI interaction by showing that immoral actions increased attributions of negative moral agency (i.e., acting immorally) and decreased attributions of positive moral agency (i.e., acting morally) and moral patiency (i.e., deserving moral concern) to both the agent (a chatbot or human assistant) and the group to which they belong (all chatbot or human assistants). There was no significant difference in the spillover effects between the AI and human contexts. In Study 2 (N = 684), we tested whether spillover persisted when the agent was individuated with a name and described as an AI or human, rather than specifically as a chatbot or personal assistant. We found that spillover persisted in the AI context but not in the human context, possibly because AIs were perceived as more homogeneous due to their outgroup status relative to humans. This asymmetry suggests a double standard whereby AIs are judged more harshly than humans when one agent morally transgresses. With the proliferation of diverse, autonomous AI systems, HCI research and design should account for the fact that experiences with one AI could easily generalize to perceptions of all AIs and negative HCI outcomes, such as reduced trust.

AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has intensified the significance of this problem, as interactions increasingly involve users specifying desired results for AI systems. In order to support better AI intent alignment, we aim to explore human strategies for intent specification in human-human communication. By studying and comparing human-human and human-LLM communication, we identify key strategies that can be applied to the design of AI systems that are more effective at understanding and aligning with user intent. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems.

But it's obvious that the time has come. We need to ditch big tech's virtual assistants and calmly demand a little more autonomy in our AI. Up front: The dream has always been to make personal assistants accessible to everyone. Since most of us can't afford our own human assistant, big tech decided to combine chatbots and natural language processing (NLP) to create a virtual version of the real thing. Billions of people use these AI-powered tools everyday.

We envision robots that can collaborate and communicate seamlessly with humans. It is necessary for such robots to decide both what to say and how to act, while interacting with humans. To this end, we introduce a new task, dialogue object search: A robot is tasked to search for a target object (e.g. fork) in a human environment (e.g., kitchen), while engaging in a "video call" with a remote human who has additional but inexact knowledge about the target's location. That is, the robot conducts speech-based dialogue with the human, while sharing the image from its mounted camera. This task is challenging at multiple levels, from data collection, algorithm and system development,to evaluation. Despite these challenges, we believe such a task blocks the path towards more intelligent and collaborative robots. In this extended abstract, we motivate and introduce the dialogue object search task and analyze examples collected from a pilot study. We then discuss our next steps and conclude with several challenges on which we hope to receive feedback.

Join executive leaders at the Conversational AI & Intelligent AI Assistants Summit, presented by Five9. Capital One has a 99% success rate when it comes to understanding customer responses to an SMS fraud alert, Ken Dodelin, vice president of mobile, web, conversational AI and messaging products at Capital One, said in a discussion about how the bank harnesses the power of personalization and automation at VentureBeat's Transform 2021 virtual conference today. When Capital One notices an anomaly in a customer's transactions, it reaches out to the customer over SMS and asks the customer to verify the transaction details. If the customer doesn't recognize the transaction, then it is clear it was fraudulent and Capital One marks it accordingly. By adding a third-party natural language processing/understanding solution, the AI assistant Eno is able to understand written responses from the customers, such as "that was me shopping in Philadelphia," which is not easy for machines to understand, Dodelin said in a conversation with VentureBeat senior reporter Sage Lazzaro.

For years, Amazon's Mechanical Turk (mTurk) has been a kind of open secret in the tech world, a place where fledgling algorithms can hire human labor on the cheap. If you need a hundred people to trace the boundaries of an object or fill out a survey, it's the single best place to make it happen. But while the project itself is well-known, it's always slightly embarrassing when a company turns up there. In 2017, Expensify was spotted asking mTurk workers to enter data from receipts, leading the company to rush out a statement insisting that the mTurk project had nothing to do with Expensify's main app. In part, it was a privacy issue, but mostly it was embarrassing: Expensify was built on a simple piece of technology -- the ability to extract data from a photo of a receipt -- and the mTurk tasks made it look like that technology was a sham. What if it was human beings extracting that data all along?

In this work, we explore human-robot collaborative planning using the \emph{functional object-oriented network} (FOON), a graphical knowledge representation for manipulations that can be performed by domestic robots. The knowledge retrieval procedure, used for acquiring the necessary steps (as a task tree) to solve a given problem, is modified to account for weights that reflect the difficulty of performing motions in a universal FOON. These weights are given as success rates, which describe the likelihood of a robot successfully completing the action(s) on its own. However, certain manipulations may be too difficult for it to perform on its own based on its own physical limitations. To make it easier for the robot, a human can assist to the minimal extent needed to perform the activity to completion by identifying those actions with low success rates for the human to do. From our experiments, it is shown that tasks can be executed successfully with the aid of the assistant. Our results show that the best task tree can be found with the adequate chance of success in completing three activities while minimizing the effort needed from the human assistant.

As we look back to 2018, we can trace some technologies which impacted the lives of an ordinary man both in the personal and professional terms. Artificial intelligence which was thought to be a simple assistant for making a person's life easier has now become an integral part of many people's lives in certain spheres. Did you ever imagine in your wildest dreams that in future you will be conversing with an artificial assistant? We always used to think it as a scene straight out from the Hollywood science fiction movie, but as we usher into the brave new world of incredible inventions, fictions are turning into reality making us question the very existence of the statement "This is not possible." This was highly questioned as consumers were in a dilemma how trustworthy would it be, but as in case of every new invention people are skeptical in the beginning but get used to it with each passing year.

Computers are advancing in leaps and bounds and are increasingly displaying attributes of practical Artificial Intelligence: they may not yet clash with astronauts, but they definitely take over many daily tasks that used to depend entirely on human brainpower. One area where this is felt is knowledge work tools: just look at Google's uncanny ability to figure what search results you really need despite your ill-formed and misspelled query… and as this trend continues to accelerate, you can't help but think: what capabilities would we want to surrender to this AI? What should it do for us – and what would we rather it didn't? Because if we don't discuss this, the software vendors will push at us whatever they think of, whether we like it or not! I'm not talking about better spell checking, mind you; that is happening. But we can expect far more powerful stuff.