Saleema Amershi's research while affiliated with Microsoft and other places

Publications (55)

Preprint
Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies the...
Preprint
Full-text available
Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible. Despite these benefits, the machine learning (ML) research community lacks well-developed norms around disclosing and discussing limitations. To address this gap, we condu...
Article
This work contributes a research protocol for evaluating human-AI interaction in the context of specific AI products. The research protocol enables UX and HCI researchers to assess different human-AI interaction solutions and validate design decisions before investing in engineering. We present a detailed account of the research protocol and demons...
Conference Paper
Full-text available
Prototyping AI user experiences is challenging due in part to probabilistic AI models making it difficult to anticipate, test, and mitigate AI failures before deployment. In this work, we set out to support practitioners with early AI prototyping, with a focus on natural language (NL)-based technologies. Our interviews with 12 NL practitioners from...
Conference Paper
Full-text available
This paper investigates how to sketch NLP-powered user experiences. Sketching is a cornerstone of design innovation. When sketching designers rapidly experiment with a number of abstract ideas using simple, tangible instruments such as drawings and paper prototypes. Sketching NLP-powered experiences, however, presents unique challenges. It can be h...
Conference Paper
Full-text available
Advances in artificial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing a...
Conference Paper
AI technologies have been incorporated into many end-user applications. However, expectations of the capabilities of such systems vary among people. Furthermore, bloated expectations have been identified as negatively affecting perception and acceptance of such systems. Although the intelligibility of ML algorithms has been well studied, there has...
Conference Paper
Current Machine Learning (ML) models can make predictions that are as good as or better than those made by people. The rapid adoption of this technology puts it at the forefront of systems that impact the lives of many, yet the consequences of this adoption are not fully understood. Therefore, work at the intersection of people's needs and ML syste...
Article
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The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them. We believe that in...
Conference Paper
Crowdsourcing provides a scalable and efficient way to construct labeled datasets for training machine learning systems. However, creating comprehensive label guidelines for crowdworkers is often prohibitive even for seemingly simple concepts. Incomplete or ambiguous label guidelines can then result in differing interpretations of concepts and inco...
Conference Paper
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Machine learning is one of the most important and successful techniques in contemporary computer science. It involves the statistical inference of models (such as classifiers) from data. It is often conceived in a very impersonal way, with algorithms working autonomously on passively collected data. However, this viewpoint hides considerable human...
Article
Performance analysis is critical in applied machine learning because it influences the models practitioners produce. Current performance analysis tools suffer from issues including obscuring important characteristics of model behavior and dissociating performance from data. In this work, we present Squares, a performance visualization for multiclas...
Conference Paper
Model building in machine learning is an iterative process. The performance analysis and debugging step typically involves a disruptive cognitive switch from model building to error analysis, discouraging an informed approach to model building. We present ModelTracker, an interactive visualization that subsumes information contained in numerous tra...
Article
Systems that can learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can resul...
Article
Full-text available
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. W...
Conference Paper
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Labeling data is a seemingly simple task required for training many machine learning systems, but is actually fraught with problems. This paper introduces the notion of concept evolution, the changing nature of a person's underlying concept (the abstract notion of the target class a person is labeling for, e.g., spam email, travel related web pages...
Article
Task automation systems promise to increase human productivity by assisting us with our mundane and difficult tasks. These systems often rely on people to (1) identify the tasks they want automated and (2) specify the procedural steps necessary to accomplish those tasks (i.e., to create task models). However, our interviews with users of a Web task...
Conference Paper
Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, tr...
Article
We present ReGroup, a novel end-user interactive machine learning system for helping people create custom, on demand groups in online social networks. As a person adds members to a group, ReGroup iteratively learns a probabilistic model of group membership specific to that group. ReGroup then uses its currently learned model to suggest additional m...
Conference Paper
Full-text available
Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to learn from the triaging dec...
Conference Paper
End-user interactive machine learning is a promising tool for enhancing human capabilities with large data. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine lear...
Article
Traditional approaches to developing user models, especially for computer-based learning environments, are notoriously difficult and time-consuming because they rely heavily on expert-elicited knowledge about the target application and domain. Furthermore, because the necessary expert knowledge is application and domain specific, the entire model d...
Article
The current information explosion fundamentally changes how people live and work with computing: vast numbers of documents and images are available on the Web; ubiquitous sensing enables near-continuous tracking and monitoring of people and objects; and inexpensive storage allows people to keep near-unlimited personal data and sensing archives. One...
Conference Paper
End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user intera...
Conference Paper
Full-text available
Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing rule-based tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to constantly learn...
Conference Paper
Full-text available
A recent trend in interface design for classrooms in developing regions has many students interacting on the same display using mice. Text entry has emerged as an important problem preventing such mouse-based single- display groupware systems from offering compelling interactive activities. We explore the design space of mouse-based text entry and...
Conference Paper
End-user interactive concept learning is a technique for interacting with large unstructured datasets, requiring insights from both human-computer interaction and machine learning. This note re-examines an assumption implicit in prior interactive machine learning research, that interaction should focus on the question "what class is this object?"....
Article
Internet connections in developing regions are scarce and of-ten unreliable. While options for connecting to the Internet are gradually being realized, progress is slow. We observed people performing web search and browsing in a low band-width environment in Kerala, India. We found that people in this environment experienced frustration and boredom...
Conference Paper
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Although existing work has explored both information extraction and community content creation, most research has focused on them in isolation. In contrast, we see the greatest leverage in the synergistic pairing of these methods as two interlocking feedback cycles. This paper explores the potential synergy promised if these cycles can be made to a...
Conference Paper
Co-located collaborative Web search is a surprisingly common activity, despite the fact that Web browsers and search engines are not designed to support collaboration. We report the findings of two studies (a diary study and an observational study) that provide insights regarding the frequency of co-located collaborative searching, the strategies p...
Conference Paper
Interaction with large unstructured datasets is difficult because existing approaches, such as keyword search, are not always suited to describing concepts corresponding to the distinctions people want to make within datasets. One possible solution is to allow end-users to train machine learning systems to identify desired concepts, a strategy know...
Article
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data)....
Article
Current Web search tools, such as browsers and search engine sites, are designed for a single user, working alone. However, users frequently need to collaborate on information-finding tasks; for example, students often work together in groups on homework assignments. To address this need, we have prototyped and evaluated several collaborative web s...
Conference Paper
Full-text available
The Intelligence in Wikipedia project at the University of Washington is combining self-supervised information ex- traction (IE) techniques with a mixed initiative interface designed to encourage communal content creation (CCC). Since IE and CCC are each powerful ways to produce large amounts of structured information, they have been studied extens...
Article
Interactive algorithm visualizations (AVs) are powerful tools for teaching and learning concepts that are difficult to describe with static media alone. However, while countless AVs exist, their widespread adoption by the academic community has not occurred due to usability problems and mixed results of pedagogical effectiveness reported in the AV...
Conference Paper
Web search is often viewed as a solitary task; however, there are many situations in which groups of people gather around a single computer to jointly search for information online. We present the findings of interviews with teachers, librarians, and developing world researchers that provide details about users' collaborative search habits in share...
Conference Paper
In recent years, there has been substantial research on exploring how AI can contribute to Human-Computer Interaction by enabling an interface to understand a user's needs and act accordingly. Understanding user needs is especially challenging when it involves assessing the user's high-level mental states not easily reflected by interface actions....
Conference Paper
In this research, we outline a user modeling framework that uses both unsupervised and supervised machine learning in order to reduce development costs of building user models, and facilitate transferability. We apply the framework to model student learning during interaction with the Adaptive Coach for Exploration (ACE) learning environment (using...
Conference Paper
In this paper, we present the application of unsupervised learning techniques to automatically recognize behaviors that may be detrimental to learning during interaction with an Exploratory Learning Environment (ELE). First, we describe how we use the k-means clustering algorithm for off-line identification of learner groups with distinguishing int...
Article
Full-text available
This paper describes the design of the CIspace interactive visualization tools for teaching and learning Artificial Intelligence. Our approach to design is to iterate through three phases: identifying pedagogical and usability goals for supporting both educators and students, designing to achieve these goals, and then evaluating our system. We beli...
Conference Paper
This paper describes the design of the CIspace interactive visualization tools for teaching and learning Artificial Intelligence. Our approach to design is to iterate through three phases: identifying pedagogical and usability goals for supporting both educators and students, designing to achieve these goals, and then evaluating our system. We beli...
Article
Full-text available
There are inherent challenges in teaching and learning Artificial Intelligence (AI) due to the complex dynamics of the many fundamental AI concepts and algorithms. Interactive visualization tools have the potential to overcome these challenges. However, there are reservations towards adopting interactive visualizations due to mixed results on their...
Article
Educational games can induce a wide range of emotions, and so recognizing specific emotions may be valuable for an intelligent system that aims to adapt to varying student needs so as to improve learning. The long-term goal of this work is to understand how user affect impacts overall learning in an educational game. The main contribution of this p...
Article
It is common for large groups of people to simultaneously share a single computer in resource-constrained communities in the developing world. However, sharing computing resources can be frustrating and can limit users' experience with and exposure to technology. In this paper, we present the motivation behind the CoSearch system, a tool to facilit...

Citations

... Last, we randomize question, block, and interface order to control for biases due to showing interfaces or questions first. Metrics Following previous work on evaluating human and ML coordination and trust, we assess several metrics to evaluate user experiences [15,20,22]. We evaluate the following statements along 1-7 Likert scale at the end of the survey: ...
... Secondly, testing different ML errors in advance would allow designers to create user interfaces that respond or adapt to ML errors, making the overall UX more pleasant or understandable. Previous studies have shown that ML errors can greatly impact the UX [1,10,11], the mental models users form to interact with the system, and their trust in the ML model [17]. Different error types could differently influence the UX, e.g. ...
... Therefore, analogical search engines should help to reduce the cognitive effort required in the process, for example by proactively retrieving results that are 'usefully' misaligned such that searchers can better recognize (as opposed to having to recall) salient constraints and refine their problem representation. This process is deeply exploratory [93,115,118] in nature, and suggest the importance of both providing end-users a sense of progress over time [103] as well as adequate feedback mechanisms for the machine to adjust according to the changing end-user search intent [72,95,96]. For example, while the machine may 'correctly' recognize a significant anaogical relevance at a higher level of purpose representation and recommend macro-scale mechanisms to a scientist who studies nano-scale phenomena (P1 Study 1 ) or solid and semiconductor-based cooling mechanisms to a scientist in liquid and evaporative cooling systems (P3 Study 1 ), these analogs may be critically misaligned on the specific constraints of the problem (i.e. the scale or materialistic phase) and thus considered by end-users as useless and even harmful. ...
... Such high user-trust in AI systems played out in several ways in our study: ready acceptances of terms, conditions, and loan decisions, often to the extent of users reevaluating their own competencies and abilities. However, design and research in user-centered AI often assumes low trust in AI, and begins with questions of 'how might we design for increased user trust in AI'? Instead, designs must plan for appropriate failures assuming high-user trust in AI systems [14]. Research must address questions such as decreasing user trust or increasing user distrust in AI systems. ...
... To complicate things further, there is often some ambiguity about whether the change the control offers is transient (e.g., for the current session) or for the longer term. One proposed design is to highlight items that will be added and removed from the user's feed when a control is changed (Schnabel et al., 2020). ...
... But the absence of any necessary condition explains that organizations cannot successfully leverage the ML value creation mechanism. While previous studies recognize and mention many resources and capabilities necessary for successful implementation of ML applications (e.g., Akkiraju et al., 2020;Amershi et al., 2019;Fountaine et al., 2019;Mikalef & Gupta, 2021;Tarafdar et al., 2019), these resources and capabilities together with environmental factors have not yet been linked to specific ML value creation mechanisms. Without this integrated view, previous studies attribute the high failure rate to organizational implementation and restructuring time lags (Brynjolfsson et al., 2017). ...
... HCML has captured the interest of ML and other computing disciplines. This interest has been reflected in a series of workshops and activities on human-centered perspectives at major HCI venues, such as ACM SIGCHI in 2016 [22], and two in 2019 [26,38]. There has been attention in ML too, as shown through the rapid ascent of the FAT ML workshop to its own independent ACM FAccT conference. ...
... In a personalized context, Buschek et al. [8] examined the potential pitfalls for achieving user interests in the co-creation context. The limitations on the machine side, identified as lack of machine creativity [41], and usability [37], and thus, highlight a biased AI with trained system bias but lacks discussions on the source of bias and the mismatch between individual expectations and system abilities. In terms of mismatched expectations, Eiband et al. [15] reported that users might intentionally provide flawed inputs when a system fails to achieve their satisfaction in everyday intelligent applications. ...
... At the software development stage, it has been recommended to include experts in user interface design 8,18 . Designing a good user interface and interaction requires careful consideration of the cognitive load of the end users 10,18,68,99,100 , by showing only relevant information in the right context, and by allowing adjustment of its behavior by end users 99 . ...
... The automation aspect of NLP was a successful conflict element that evoked a range of reactions (both individual and collaborative [96]). In that way, we used design fiction to co-speculate with the participants (implication, ideation), to learn about their values expectations, neither the underlying details of model workings [144], nor solutions [20,21]. ...