arXiv subjects

Meredith Ringel Morris

Publications and source records attributed to Meredith Ringel Morris.

32 records · Page 2Linked to original sources

arXiv2022

LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia

Prior work has explored the writing challenges experienced by people with dyslexia, and the potential for new spelling, grammar, and word retrieval technologies to address these challenges. However, the capabilities for natural language generation demonstrated by the latest class of large language models (LLMs) highlight an opportunity to explore new forms of human-AI writing support tools. In this paper, we introduce LaMPost, a prototype email-writing interface that explores the potential for LLMs to power writing support tools that address the varied needs of people with dyslexia. LaMPost draws from our understanding of these needs and introduces novel AI-powered features for email-writing, including: outlining main ideas, generating a subject line, suggesting changes, rewriting a selection. We evaluated LaMPost with 19 adults with dyslexia, identifying many promising routes for further exploration (including the popularity of the "rewrite" and "subject line" features), but also finding that the current generation of LLMs may not surpass the accuracy and quality thresholds required to meet the needs of writers with dyslexia. Surprisingly, we found that participants' awareness of the AI had no effect on their perception of the system, nor on their feelings of autonomy, expression, and self-efficacy when writing emails. Our findings yield further insight into the benefits and drawbacks of using LLMs as writing support for adults with dyslexia and provide a foundation to build upon in future research.

cs.HC↗

arXiv2022

Context-Aware Abbreviation Expansion Using Large Language Models

Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters. Our approach is to expand the abbreviations into full-phrase options by leveraging conversation context with the power of pretrained large language models (LLMs). Through zero-shot, few-shot, and fine-tuning experiments on four public conversation datasets, we show that for replies to the initial turn of a dialog, an LLM with 64B parameters is able to exactly expand over 70% of phrases with abbreviation length up to 10, leading to an effective keystroke saving rate of up to about 77% on these exact expansions. Including a small amount of context in the form of a single conversation turn more than doubles abbreviation expansion accuracies compared to having no context, an effect that is more pronounced for longer phrases. Additionally, the robustness of models against typo noise can be enhanced through fine-tuning on noisy data.

cs.CL↗

arXiv2022

LaMDA: Language Models for Dialog Applications

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency.

cs.CL↗

arXiv2021

Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs

Disaggregated evaluations of AI systems, in which system performance is assessed and reported separately for different groups of people, are conceptually simple. However, their design involves a variety of choices. Some of these choices influence the results that will be obtained, and thus the conclusions that can be drawn; others influence the impacts -- both beneficial and harmful -- that a disaggregated evaluation will have on people, including the people whose data is used to conduct the evaluation. We argue that a deeper understanding of these choices will enable researchers and practitioners to design careful and conclusive disaggregated evaluations. We also argue that better documentation of these choices, along with the underlying considerations and tradeoffs that have been made, will help others when interpreting an evaluation's results and conclusions.

cs.CY↗

arXiv2021

Understanding the Representation and Representativeness of Age in AI Data Sets

A diverse representation of different demographic groups in AI training data sets is important in ensuring that the models will work for a large range of users. To this end, recent efforts in AI fairness and inclusion have advocated for creating AI data sets that are well-balanced across race, gender, socioeconomic status, and disability status. In this paper, we contribute to this line of work by focusing on the representation of age by asking whether older adults are represented proportionally to the population at large in AI data sets. We examine publicly-available information about 92 face data sets to understand how they codify age as a case study to investigate how the subjects' ages are recorded and whether older generations are represented. We find that older adults are very under-represented; five data sets in the study that explicitly documented the closed age intervals of their subjects included older adults (defined as older than 65 years), while only one included oldest-old adults (defined as older than 85 years). Additionally, we find that only 24 of the data sets include any age-related information in their documentation or metadata, and that there is no consistent method followed across these data sets to collect and record the subjects' ages. We recognize the unique difficulties in creating representative data sets in terms of age, but raise it as an important dimension that researchers and engineers interested in inclusive AI should consider.

cs.CY↗

arXiv2020

Social App Accessibility for Deaf Signers

Social media platforms support the sharing of written text, video, and audio. All of these formats may be inaccessible to people who are deaf or hard of hearing (DHH), particularly those who primarily communicate via sign language, people who we call Deaf signers. We study how Deaf signers engage with social platforms, focusing on how they share content and the barriers they face. We employ a mixed-methods approach involving seven in-depth interviews and a survey of a larger population (n = 60). We find that Deaf signers share the most in written English, despite their desire to share in sign language. We further identify key areas of difficulty in consuming content (e.g., lack of captions for spoken content in videos) and producing content (e.g., captioning signed videos, signing into a phone camera) on social media platforms. Our results both provide novel insights into social media use by Deaf signers and reinforce prior findings on DHH communication more generally, while revealing potential ways to make social media platforms more accessible to Deaf signers.

cs.HC↗

arXiv2020

AI and Accessibility: A Discussion of Ethical Considerations

According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered.

cs.CY↗

arXiv2019

Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective

Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in separate disciplinary silos, and tackles separate portions of the sign language processing pipeline. This leads to three key questions: 1) What does an interdisciplinary view of the current landscape reveal? 2) What are the biggest challenges facing the field? and 3) What are the calls to action for people working in the field? To help answer these questions, we brought together a diverse group of experts for a two-day workshop. This paper presents the results of that interdisciplinary workshop, providing key background that is often overlooked by computer scientists, a review of the state-of-the-art, a set of pressing challenges, and a call to action for the research community.

cs.CV↗

arXiv2019

Toward Fairness in AI for People with Disabilities: A Research Roadmap

AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.

cs.CY↗

arXiv2015

Is Anyone Out There? Unpacking Q&A Hashtags on Twitter

In addition to posting news and status updates, many Twitter users post questions that seek various types of subjective and objective information. These questions are often labeled with "Q&A" hashtags, such as #lazyweb or #twoogle. We surveyed Twitter users and found they employ these Q&A hashtags both as a topical signifier (this tweet needs an answer!) and to reach out to those beyond their immediate followers (a community of helpful tweeters who monitor the hashtag). However, our log analysis of thousands of hashtagged Q&A exchanges reveals that nearly all replies to hashtagged questions come from a user's immediate follower network, contradicting user's beliefs that they are tapping into a larger community by tagging their question tweets. This finding has implications for designing next-generation social search systems that reach and engage a wide audience of answerers.

cs.SI↗

arXiv2015

RIMES: Embedding Interactive Multimedia Exercises in Lecture Videos

Teachers in conventional classrooms often ask learners to express themselves and show their thought processes by speaking out loud, drawing on a whiteboard, or even using physical objects. Despite the pedagogical value of such activities, interactive exercises available in most online learning platforms are constrained to multiple-choice and short answer questions. We introduce RIMES, a system for easily authoring, recording, and reviewing interactive multimedia exercises embedded in lecture videos. With RIMES, teachers can prompt learners to record their responses to an activity using video, audio, and inking while watching lecture videos. Teachers can then review and interact with all the learners' responses in an aggregated gallery. We evaluated RIMES with 19 teachers and 25 students. Teachers created a diverse set of activities across multiple subjects that tested deep conceptual and procedural knowledge. Teachers found the exercises useful for capturing students' thought processes, identifying misconceptions, and engaging students with content.

cs.CY↗

arXiv2015

Mudslide: A Spatially Anchored Census of Student Confusion for Online Lecture Videos

Educators have developed an effective technique to get feedback after in-person lectures, called "muddy card." Students are given time to reflect and write the "muddiest" (least clear) point on an index card, to hand in as they leave class. This practice of assigning end-of-lecture reflection tasks to generate explicit student feedback is well suited for adaptation to the challenge of supporting feedback in online video lectures. We describe the design and evaluation of Mudslide, a prototype system that translates the practice of muddy cards into the realm of online lecture videos. Based on an in-lab study of students and teachers, we find that spatially contextualizing students' muddy point feedback with respect to particular lecture slides is advantageous to both students and teachers. We also reflect on further opportunities for enhancing this feedback method based on teachers' and students' experiences with our prototype.

cs.CY↗

arXiv2009

Proceedings of 1st International Workshop on Collaborative Information Seeking

The goal of the workshop is to bring together researchers interested in various aspects of small-team collaborative search to share ideas, to stimulate research in the area, and to increase the visibility of this emerging area. We expect to identify promising directions for further exploration and to establish collaborative links among research groups. The workshop took place on June 20, 2008 in Pittsburgh, Pennsylvania, USA, in conjunction with the JCDL 2008 conference. The workshop was organized around three themes: practices, models, and evaluation. We started with a discussion (still ongoing) about terminology, about how to situate our work in the existing research space. We also wanted to motivate our modeling and design discussions with real-world examples of collaboration. We discussed examples from the healthcare domain, students, faculty members, the military, and businesses such as pharmaceutical companies that conduct research. We discussed several models of collaborative information seeking, including sense-making, communication, and information seeking theory based on Marcia Bates' Berrypicking theory. Finally, presenters described several systems that implement various aspects of collaboration, including using search paths, simulations of user behavior to model system performance, and characterizing properties of groups that lead to more effective collaboration.

cs.IR↗

arXiv2009

Understanding Groups' Properties as a Means of Improving Collaborative Search Systems

Understanding the similar properties of people involved in group search sessions has the potential to significantly improve collaborative search systems; such systems could be enhanced by information retrieval algorithms and user interface modifications that take advantage of important properties, for example by re-ordering search results using information from group members' combined user profiles. Understanding what makes group members similar can also assist with the identification of groups, which can be valuable for connecting users with others with whom they might undertake a collaborative search. In this workshop paper, we describe our current research efforts towards studying the properties of a variety of group types. We discuss properties of groups that may be relevant to designers of collaborative search systems, and propose ways in which understanding such properties could influence the design of interfaces and algorithms for collaborative Web search.

cs.IR↗