Academic literature on the topic 'Conversational systems'
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Journal articles on the topic "Conversational systems"
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Qin, Zhen (Luther). "Conversational Breakdown Detector for a Motivational Interviewing Conversational Agent." IJournal: Student Journal of the Faculty of Information 9, no. 1 (December 19, 2023): 60–77. http://dx.doi.org/10.33137/ijournal.v9i1.42237.
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A conversational breakdown in human-chatbot interaction refers to a disruption or failure in the communicative flow between the human user and the chatbot. To recover a disrupted conversation, the first step is to detect the breakdown. Researchers have proposed methods using supervised learning and semi-supervised learning in dialogue systems to achieve the goal of detecting conversational breakdown. However, few studies have focused on detecting breakdowns in automated therapeutic conversations, especially conversations led by motivational interviewing chatbots. The presence of conversational breakdowns has negative impacts on the human-chatbot interaction, such as frustration, dissatisfaction, or loss of trust. This gap suggests a need to build a robust and efficient conversational breakdown detector that recognizes interruptions during the conversation. Conversational breakdown detection paves the way for further action to recover conversations. In this paper, I develop a novel, unifying framework called “CIMIC” for characterizing the conversational breakdowns of “MIBot,” a motivational interviewing conversational agent for smoking cessation. I collect 200 pieces of conversational data through Prolific and annotate them using the CIMIC framework with a group of four trained annotators. The annotated dataset is then applied as the training set to fine-tune GPT-3 models to build a conversational breakdown detector for the MIBot.
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Watkinson, Neftali, Fedor Zaitsev, Aniket Shivam, Michael Demirev, Mike Heddes, Tony Givargis, Alexandru Nicolau, and Alexander Veidenbaum. "EdgeAvatar: An Edge Computing System for Building Virtual Beings." Electronics 10, no. 3 (January 20, 2021): 229. http://dx.doi.org/10.3390/electronics10030229.
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Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the length of the conversation without any specific targeted task. When a chatbot is embellished with an artistic approach that is meant to evoke an emotional response, then it is called a virtual being. On the other hand, conversational agents that interact with the physical world require the use of specialized hardware to sense and process captured information. In this article we describe EdgeAvatar, a system based on Edge Computing principles for the creation of virtual beings. The objective of the EdgeAvatar system is to provide a streamlined and modular framework for virtual being applications that are to be deployed in public settings. We also present two implementations that use EdgeAvatar and are inspired by historical figures to interact with visitors of the Venice Biennale 2019. EdgeAvatar can adapt to fit different approaches for AI powered conversations.
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Kiesel, Johannes, Lars Meyer, Martin Potthast, and Benno Stein. "Meta-Information in Conversational Search." ACM Transactions on Information Systems 39, no. 4 (October 31, 2021): 1–44. http://dx.doi.org/10.1145/3468868.
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The exchange of meta-information has always formed part of information behavior. In this article, we show that this rule also extends to conversational search. Information about the user’s information need, their preferences, and the quality of search results are only some of the most salient examples of meta-information that are exchanged as a matter of course in a search conversation. To understand the importance of meta-information for conversational search, we revisit its definition and survey how meta-information has been taken into account in the past in information retrieval. Meta-information has gone by many names, about which a concise overview is provided. An in-depth analysis of the role of meta-information in search and conversation theories reveals that they provide significant support for the importance of meta-information in conversational search. We further identify conversational search datasets are suitable for a deeper inspection with regard to meta-information, namely, Spoken Conversational Search and Microsoft Information-Seeking Conversations. A quantitative data analysis demonstrates the practical significance of meta-information in information-seeking conversations, whereas a qualitative analysis shows the effects of exchanging different types. Finally, we discuss practical applications and challenges of meta-information in conversational search, including a case study of VERSE, an existing search system for the visually impaired.
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Lin, Dongding, Jian Wang, and Wenjie Li. "COLA: Improving Conversational Recommender Systems by Collaborative Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4462–70. http://dx.doi.org/10.1609/aaai.v37i4.25567.
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Conversational recommender systems (CRS) aim to employ natural language conversations to suggest suitable products to users. Understanding user preferences for prospective items and learning efficient item representations are crucial for CRS. Despite various attempts, earlier studies mostly learned item representations based on individual conversations, ignoring item popularity embodied among all others. Besides, they still need support in efficiently capturing user preferences since the information reflected in a single conversation is limited. Inspired by collaborative filtering, we propose a collaborative augmentation (COLA) method to simultaneously improve both item representation learning and user preference modeling to address these issues. We construct an interactive user-item graph from all conversations, which augments item representations with user-aware information, i.e., item popularity. To improve user preference modeling, we retrieve similar conversations from the training corpus, where the involved items and attributes that reflect the user's potential interests are used to augment the user representation through gate control. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our method. Our code and data are available at https://github.com/DongdingLin/COLA.
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Kum, Junyeong, and Myungho Lee. "Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?" Applied Sciences 12, no. 21 (October 29, 2022): 10972. http://dx.doi.org/10.3390/app122110972.
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The demand for a conversational system with digital humans has increased with the development of artificial intelligence. Latency can occur in such conversational systems because of natural language processing and network issues, which can deteriorate the user’s performance and the availability of the systems. There have been attempts to mitigate user-perceived latency by using conversational fillers in human–agent interaction and human–robot interaction. However, non-verbal cues, such as gestures, have received less attention in such attempts, despite their essential roles in communication. Therefore, we designed gestural fillers for the digital humans. This study examined the effects of whether the conversation type and gesture filler matched or not. We also compared the effects of the gestural fillers with conversational fillers. The results showed that the gestural fillers mitigate user-perceived latency and affect the willingness, impression, competence, and discomfort in conversations with digital humans.
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Elvir, Miguel, Avelino J. Gonzalez, Christopher Walls, and Bryan Wilder. "Remembering a Conversation – A Conversational Memory Architecture for Embodied Conversational Agents." Journal of Intelligent Systems 26, no. 1 (January 1, 2017): 1–21. http://dx.doi.org/10.1515/jisys-2015-0094.
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AbstractThis paper addresses the role of conversational memory in Embodied Conversational Agents (ECAs). It describes an investigation into developing such a memory architecture and integrating it into an ECA. ECAs are virtual agents whose purpose is to engage in conversations with human users, typically through natural language speech. While several works in the literature seek to produce viable ECA dialog architectures, only a few authors have addressed the episodic memory architectures in conversational agents and their role in enhancing their intelligence. In this work, we propose, implement, and test a unified episodic memory architecture for ECAs. We describe a process that determines the prevalent contexts in the conversations obtained from the interactions. The process presented demonstrates the use of multiple techniques to extract and store relevant snippets from long conversations, most of whose contents are unremarkable and need not be remembered. The mechanisms used to store, retrieve, and recall episodes from previous conversations are presented and discussed. Finally, we test our episodic memory architecture to assess its effectiveness. The results indicate moderate success in some aspects of the memory-enhanced ECAs, as well as some work still to be done in other aspects.
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Huang, Ting-Hao, Walter Lasecki, Amos Azaria, and Jeffrey Bigham. ""Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 79–88. http://dx.doi.org/10.1609/hcomp.v4i1.13292.
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Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly converses with end users over time by recruiting workers on demand, which in turn decide what might be the best response for each user sentence. Up to the first month of our deployment, 59 users have held conversations with Chorus during 320 conversational sessions. In this paper, we present an account of Chorus' deployment, with a focus on four challenges: (i) identifying when conversations are over, (ii) malicious users and workers, (iii) on-demand recruiting, and (iv) settings in which consensus is not enough. Our observations could assist the deployment of crowd-powered conversation systems and crowd-powered systems in general.
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Ford, Nigel. "“Conversational” information systems." Journal of Documentation 61, no. 3 (June 2005): 362–84. http://dx.doi.org/10.1108/00220410510598535.
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Ren, Pengjie, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, and Maarten De Rijke. "Conversations with Search Engines: SERP-based Conversational Response Generation." ACM Transactions on Information Systems 39, no. 4 (October 31, 2021): 1–29. http://dx.doi.org/10.1145/3432726.
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In this article, we address the problem of answering complex information needs by conducting conversations with search engines , in the sense that users can express their queries in natural language and directly receive the information they need from a short system response in a conversational manner. Recently, there have been some attempts towards a similar goal, e.g., studies on Conversational Agent s (CAs) and Conversational Search (CS). However, they either do not address complex information needs in search scenarios or they are limited to the development of conceptual frameworks and/or laboratory-based user studies. We pursue two goals in this article: (1) the creation of a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines, and (2) the development of a state-of-the-art pipeline for conversations with search engines, Conversations with Search Engines (CaSE), using this dataset. SaaC is built based on a multi-turn conversational search dataset, where we further employ workers from a crowdsourcing platform to summarize each relevant passage into a short, conversational response. CaSE enhances the state-of-the-art by introducing a supporting token identification module and a prior-aware pointer generator, which enables us to generate more accurate responses. We carry out experiments to show that CaSE is able to outperform strong baselines. We also conduct extensive analyses on the SaaC dataset to show where there is room for further improvement beyond CaSE. Finally, we release the SaaC dataset and the code for CaSE and all models used for comparison to facilitate future research on this topic.
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Yan, Rui, Weiheng Liao, Dongyan Zhao, and Ji-Rong Wen. "Multi-Response Awareness for Retrieval-Based Conversations: Respond with Diversity via Dynamic Representation Learning." ACM Transactions on Information Systems 39, no. 4 (October 31, 2021): 1–29. http://dx.doi.org/10.1145/3470450.
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Conversational systems now attract great attention due to their promising potential and commercial values. To build a conversational system with moderate intelligence is challenging and requires big (conversational) data, as well as interdisciplinary techniques. Thanks to the prosperity of the Web, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversational systems. In general, retrieval-based conversational systems apply various matching schema between query utterances and responses, but the classic retrieval paradigm suffers from prominent weakness for conversations: the system finds similar responses given a particular query. For real human-to-human conversations, on the contrary, responses can be greatly different yet all are possibly appropriate. The observation reveals the diversity phenomenon in conversations. In this article, we ascribe the lack of conversational diversity to the reason that the query utterances are statically modeled regardless of candidate responses through traditional methods. To this end, we propose a dynamic representation learning strategy that models the query utterances and different response candidates in an interactive way. To be more specific, we propose a Respond-with-Diversity model augmented by the memory module interacting with both the query utterances and multiple candidate responses. Hence, we obtain dynamic representations for the input queries conditioned on different response candidates. We frame the model as an end-to-end learnable neural network. In the experiments, we demonstrate the effectiveness of the proposed model by achieving a good appropriateness score and much better diversity in retrieval-based conversations between humans and computers.
Dissertations / Theses on the topic "Conversational systems"
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Sidås, Albin, and Simon Sandberg. "Conversational Engine for Transportation Systems." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176810.
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Today's communication between operators and professional drivers takes place through direct conversations between the parties. This thesis project explores the possibility to support the operators in classifying the topic of incoming communications and which entities are affected through the use of named entity recognition and topic classifications. By developing a synthetic training dataset, a NER model and a topic classification model was developed and evaluated to achieve F1-scores of 71.4 and 61.8 respectively. These results were explained by a low variance in the synthetic dataset in comparison to a transcribed dataset from the real world which included anomalies not represented in the synthetic dataset. The aforementioned models were integrated into the dialogue framework Emora to seamlessly handle the back and forth communication and generating responses.
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Lee, John Sie Yuen 1977. "Translingual grammar induction for conversational systems." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28719.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 75-77).
We propose an induction algorithm to semi-automate grammar authoring in an interlingua-based machine translation framework. This algorithm is designed for restricted domains within the context of multilingual conversational systems. It uses a pre-existing one-way translation system from some other language to the target language as prior information. It then infers a grammar for the target language. We demonstrate the system's effectiveness on a weather domain and on a travel domain. We automatically induced Chinese and French grammars for these domains from their English counterparts, and then showed that they can produce high-quality interlingua to be used in translation.
by John Sie Yuen Lee.
S.M.
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Baheti, Ashutosh. "Improving Conversation Quality of Data-driven Dialog Systems and Applications in Conversational Question Answering." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1596469447727479.
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Wärnestål, Pontus. "Dialogue behavior management in conversational recommender systems /." Linköping : Department of Computer and Information Science, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9624.
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Wärnestål, Pontus. "Dialogue Behavior Management in Conversational Recommender Systems." Doctoral thesis, Linköpings universitet, NLPLAB - Laboratoriet för databehandling av naturligt språk, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9624.
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This thesis examines recommendation dialogue, in the context of dialogue strategy design for conversational recommender systems. The purpose of a recommender system is to produce personalized recommendations of potentially useful items from a large space of possible options. In a conversational recommender system, this task is approached by utilizing natural language recommendation dialogue for detecting user preferences, as well as for providing recommendations. The fundamental idea of a conversational recommender system is that it relies on dialogue sessions to detect, continuously update, and utilize the user's preferences in order to predict potential interest in domain items modeled in a system. Designing the dialogue strategy management is thus one of the most important tasks for such systems. Based on empirical studies as well as design and implementation of conversational recommender systems, a behavior-based dialogue model called bcorn is presented. bcorn is based on three constructs, which are presented in the thesis. It utilizes a user preference modeling framework (preflets) that supports and utilizes natural language dialogue, and allows for descriptive, comparative, and superlative preference statements, in various situations. Another component of bcorn is its message-passing formalism, pcql, which is a notation used when describing preferential and factual statements and requests. bcorn is designed to be a generic recommendation dialogue strategy with conventional, information-providing, and recommendation capabilities, that each describes a natural chunk of a recommender agent's dialogue strategy, modeled in dialogue behavior diagrams that are run in parallel to give rise to coherent, flexible, and effective dialogue in conversational recommender systems. Three empirical studies have been carried out in order to explore the problem space of recommendation dialogue, and to verify the solutions put forward in this work. Study I is a corpus study in the domain of movie recommendations. The result of the study is a characterization of recommendation dialogue, and forms a base for a first prototype implementation of a human-computer recommendation dialogue control strategy. Study II is an end-user evaluation of the acorn system that implements the dialogue control strategy and results in a verification of the effectiveness and usability of the dialogue strategy. There are also implications that influence the refinement of the model that are used in the bcorn dialogue strategy model. Study III is an overhearer evaluation of a functional conversational recommender system called CoreSong, which implements the bcorn model. The result of the study is indicative of the soundness of the behavior-based approach to conversational recommender system design, as well as the informativeness, naturalness, and coherence of the individual bcorn dialogue behaviors.I denna avhandling undersöks rekommendationsdialog med avseende på utformningen av dialogstrategier f¨or konverserande rekommendationssystem. Syftet med ett rekommendationssystem är att generera personaliserade rekommendationer utifrån potentiellt användbara domänobjekt i stora informationsrymder. I ett konverserande rekommendationssystem angrips detta problem genom att utnyttja naturligt språkk och dialog för att modellera användarpreferenser, liksom för att ge rekommendationer. Grundidén med konverserande rekommendationssystem är att utnyttja dialogsessioner för att upptäcka, uppdatera och utnyttja en användares preferenser för att förutsäga användarens intresse för domänobjekten som modelleras i ett system. Utformningen av dialogstrategihantering är därför en av de viktigaste uppgifterna för sådana system. Baserat på empiriska studier, liksom på utformning och implementering av konverserande rekommendationssystem, presenteras en beteendebaserad dialogmodell som kallas bcorn. bcorns bas utgörs av tre konstruktioner, vilka alla presenteras i denna avhandling. bcorn utnyttjar ett preferensmodelleringsramverk (preflets) som stöder och anv¨ander sig av naturligt språk i dialog och tillåter deskriptiva, komparativa och superlativa preferensuttryck i olika situationer. Den andra komponenten i bcorn är dess interna meddelande-formalism pcql, som är en notation som kan beskriva preferens- och faktiska påståenden och frågor. bcorn är utformat som en generell rekommendationshanteringsstrategi med konventionella, informationsgivande och rekommenderande förmågor, som var och en beskriver naturliga delar av en rekommendationsagents dialogstrategi. Dessa delar modelleras i dialogbeteendediagram som exekveras parallellt för att ge upphov till koherent, flexibel och effektiv dialog i konverserande rekommendationssystem. Tre empiriska studier har utförts för att utforska problemkomplexet som utgör rekommendationsdialog och för att verifiera de lösningar som tagits fram inom ramen för detta arbete. Studie I är en korpusstudie i filmrekommendationsdomänen. Studien resulterar i en karakteristik av rekommendationsdialog, och utgör basen för en första prototyp av dialoghanteringsstrategi för rekommendationsdialog mellan människa och dator. Studie II är en slutanvändarutvärdering av systemet acorn som implementerar denna dialoghanteringsstrategi och resulterar i en verifiering av effektivitet och användbarhet av strategin. Studien resulterar också i implikationer som påverkar utformningen av den modell som används i bcorn. Studie III är en medhörningsutvärdering av det funktionella konverserande rekommendationssystemet CoreSong, som implementerar bcorn-modellen. Resultatet av studien indikerar att det beteendebaserade angreppssättet är funktionellt och att de olika dialogbeteendena i bcorn ger upphov till h¨og informationskvalitet, naturlighet och koherens i rekommendationsdialog.
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Boulis, Constantinos. "Topic learning in text and conversational speech /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/5914.
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Pérez, Rojas Daniel. "Tlatoa Communicator a Framework to Create Conversational Systems." Thesis, Universidad de las Américas Puebla, 2010. http://catarina.udlap.mx/u_dl_a/tales/documentos/dsc/perez_r_d/.
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The work presented in this proposal describes an approach to solve an actual
problem in the conversational systems area: to enable non-experts to design
and develop in which the user can establish a spoken dialogue systems. In
most of the systems of this type, the specific information relative to the
domain, or the specific information that delimits the specific capacities
and limitations of the system is embedded within the source code. Due to
this, people who want to modify or change actual systems experiment a great
number of difficulties, because they must search and investigate in which
parts of the source code this information is represented. Nowadays there
are some efforts to solve this problem, some of them on the part of private
sector companies, and some others by research centers mainly in universities
around the world; the objective is to separate the domain specific information
from the application, making the creation of new systems easier for people
with few or no programming skills.(cont.) Our proposal was born, when some years ago, in our research laboratory; we tried to part from an existing conversational system to modify it, first to make it work in Spanish language, then to change the domain of the dialogue. However these efforts did not bear satisfying results because the specific information for the tasks that were carried out was in several different parts of the source code, in different programming languages. The idea of the creation of a conversational system framework where the task specific information could be separated from the source code was born, and this idea was approved in 2003 by a committee conformed by UDLAP researchers and external participants, Dr. Luis Villaseñor from INOEP and Dr. Wayne Ward from the Center for Spoken Language Research of University of Colorado at Boulder. Our proposal consists of the creation of a new module that deals with all the aspects relative to the specific information of every task and instance of a dialogue system.
(cont.) This task specific information is specified in text files with XML format that are loaded first by this module, that scans and analyzes them, looking for errors, validating these archives against its respective document type definition (DTD) file. Next a consistency check is made to make sure that these configuration files keep the consistency with others according to the type of information handled. Once the information and its format is validated, this module instructs the rest of the modules in charge of the dialogue (DM, TTS, NLG, NLP, ASR, etc.) how to carry out all interaction in the dialogue system, where they can find the data which they require. The proposal presents the complete architecture of the system, together with a Graphical User Interface by means of which, a user with few or null programming skills will be able to specify a task describing all the elements a conversational system requires. The main contributions of this work are: The idea of a conversational system module that verifies and manages all the task specific information in a conversational system A GUI to allow non-expert users to create his/her own conversational system guided step by step in a simple web based interface.
(cont.) This work has been published in: "An approach to separate the Task-Specific-Information from the source code in Galaxy Based Conversational Systems" D. Perez, , I. Kirschning, WSEAS Transactions on Communications, Issue 1, Volume 3, January 2004. "TLATOA COMMUNICATOR: A framework to create Task-Independent Conversational Systems" D. Perez, I. Kirschning CISSE 2009 Volume 2: Innovations in Computing Sciences and Software Engineering SCSS, presented on December 16, 2009 (to be published).
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Leonhardt, Michelle Denise. "Enhancing affective communication in embodied conversational agents through personality-based hidden conversational goals." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/49756.
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Embodied Conversational Agents (ECAs) are intelligent software entities with an embodiment used to communicate with users, using natural language. Their purpose is to exhibit the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. Researchers in the field of ECAs try to create agents that can be more natural, believable and easy to use. Designing an ECA requires understanding that manner, personality, emotion, and appearance are very important issues to be considered. In this thesis, we are interested in increasing believability of ECAs by placing personality at the heart of the human-agent verbal interaction. We propose a model relating personality facets and hidden communication goals that can influence ECA behaviors. Moreover, we apply our model in agents that interact in a puzzle game application. We develop five distinct personality oriented agents using an expressive communication language and a plan-based BDI approach for modeling and managing dialogue according to our proposed model. In summary, we present and test an innovative approach to model mental aspects of ECAs trying to increase their believability and to enhance human-agent affective communication. With this research, we hope to improve the understanding on how ECAs with expressive and affective characteristics can establish and maintain long-term human-agent relationships.
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Sahay, Saurav. "Socio-semantic conversational information access." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42855.
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The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is 'aware' of users' preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users' verbal intentions in conversations while making recommendation decision.
One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information.
Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain. Cobot leverages these interactions to maintain users' episodic and long term semantic models. The agent
analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community
preferred, contextually relevant resources.
The nodes of the semantic memory are frequent concepts extracted from user's interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational
facets are matched with episodic memories and a spreading activation search on the
semantic net is performed for generating the top candidate user recommendations for the conversation.
The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.
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Nooraei, Beidokht Bahador. "A Real-Time Architecture for Conversational Agents." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/972.
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"Consider two people having a face-to-face conversation. They sometimes listen, sometimes talk, and sometimes interrupt each other. They use facial expressions to signal that they are confused. They point at objects. They jump from topic to topic opportunistically. When another acquaintance walks by, they nod and say hello. All the while they have other concerns on their mind, such as not missing the meeting that starts in 10 minutes. Like many other humans behaviors, these are not easy to replicate in artificial agents. In this work we look into the design requirements of an embodied agent that can participate in such natural conversations in a mixed-initiative, multi-modal setting. Such an agent needs to understand participating in a conversation is not merely a matter of sending a message and then waiting to receive a response -- both partners are simultaneously active at all times. This agent should be able to deal with different, sometimes conflicting goals, and be always ready to address events that may interrupt the current topic of conversation. To address those requirements, we have created a modular architecture that includes distributed functional units that compete with each other to gain control over available resources. Each of these units, called a schema, has its own sense- think-act cycle. In the field of robotics, this design is often referred to as "behavior-based" or "schema-based." The major contribution of this work is merging behavior-based robotics with plan- based human-computer interaction."
Books on the topic "Conversational systems"
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Voice communication with computers: Conversational systems. New York: Van Nostrand Reinhold, 1994.
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D'Haro, Luis Fernando, Zoraida Callejas, and Satoshi Nakamura, eds. Conversational Dialogue Systems for the Next Decade. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8395-7.
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Voice interaction design: Crafting the new conversational speech systems. San Francisco, CA: Morgan Kaufmann Publishers, 2005.
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UTLAS. Utlas systems manual : guide to using REFCATSS II : conversational. Toronto: Utlas, 1986.
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1951-, Waterworth John A., Talbot Mike, and Campbell J. A. 1940-, eds. Speech and language-based interaction with machines: Towards the conversational computer. Chichester, West Sussex, England: E. Horwood, 1987.
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Spoken dialogue technology: Toward the conversational user interface. London: Springer, 2004.
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UTLAS. UTLAS systems manual : guide to using CATSS II (conversational mode). Toronto: UTLAS, 1985.
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Rethinking university teaching: A conversational framework for the effective use of learning technologies. 2nd ed. London: RoutledgeFalmer, 2002.
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Olivier, Pietquin, and SpringerLink (Online service), eds. Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces. New York, NY: Springer New York, 2012.
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Practical Christian socialism: A conversational exposition of the true system of human society; in 3 parts: Fundamental principles. Constitutional polity. Superiority to other systems. New York: Cornell University Library, 2009.
Find full textBook chapters on the topic "Conversational systems"
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McTear, Michael. "Evaluating Dialogue Systems." In Conversational AI, 91–123. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02176-3_4.
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McTear, Michael, and Marina Ashurkina. "Designing Conversational Systems." In Transforming Conversational AI, 17–42. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_2.
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McTear, Michael. "Statistical Data-Driven Dialogue Systems." In Conversational AI, 71–89. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02176-3_3.
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Nishida, Toyoaki. "Conversation Quantization for Conversational Knowledge Process." In Databases in Networked Information Systems, 15–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31970-2_2.
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McTear, Michael. "End-to-End Neural Dialogue Systems." In Conversational AI, 125–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02176-3_5.
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McTear, Michael. "Rule-Based Dialogue Systems: Architecture, Methods, and Tools." In Conversational AI, 43–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02176-3_2.
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Lemon, Oliver. "Conversational Interfaces." In Data-Driven Methods for Adaptive Spoken Dialogue Systems, 1–4. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4803-7_1.
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Nair, S. Arun, Amit Anil Nanavati, and Nitendra Rajput. "Conspeakuous: Contextualising Conversational Systems." In Human-Computer Interaction. HCI Intelligent Multimodal Interaction Environments, 165–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73110-8_18.
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Lin, Allen, Jianling Wang, Ziwei Zhu, and James Caverlee. "Federated Conversational Recommender Systems." In Lecture Notes in Computer Science, 50–65. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56069-9_4.
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Li, Shuokai, Yongchun Zhu, Ruobing Xie, Zhenwei Tang, Zhao Zhang, Fuzhen Zhuang, Qing He, and Hui Xiong. "Customized Conversational Recommender Systems." In Machine Learning and Knowledge Discovery in Databases, 740–56. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_43.
Full textConference papers on the topic "Conversational systems"
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Yan, Rui, and Dongyan Zhao. "Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/629.
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Conversational systems are becoming more and more promising by playing an important role in human-computer communications. A conversational system is supposed to be intelligent to enable human-like interactions. The long-term goal of smart human-computer conversations is challenging and heavily driven by data. Thanks to the prosperity of Web 2.0, a large volume of conversational data become available to establish human-computer conversational systems. Given a human issued message, namely a query, a traditional conversational system would provide a response after proper training of how to respond like humans. In this paper, we propose a new paradigm for neural generative conversations: smarter response with a suggestion is provided given the query. We assume that the new conversation mode which proactively introduces contents as next utterances, keeping user actively engaged. To address the task, we propose a novel integrated model to handle both the response generation and the suggestion generation. From the experimental results, we verify the effectiveness of the new neural generative conversation paradigm.
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Deng, Yang, Wenqiang Lei, Wai Lam, and Tat-Seng Chua. "A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/738.
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Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that this first survey of proactive dialogue systems can provide the community with a quick access and an overall picture to this practical problem, and stimulate more progresses on conversational AI to the next level.
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Jia, Fusheng, Xinyu Chen, and Renke He. "Conversational Interaction Design for shopping apps: Situational awareness theory." In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002918.
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Conversational interaction is crucial to the development of e-commerce. In order to meet the needs of users, more and more enterprises begin to build chatbots based on conversational interaction. Conversational chatbots are becoming more and more popular, gradually becoming the preferred way for users to communicate with enterprises. Chatbots can provide users with personalized experience and high-quality services. However, there is no complete and systematic design principle to guide the design of its new conversational interface interaction. Many designers have borrowed the methods of social chat software for their design, which is lack of pertinence and difficult to ensure the rationality of interaction. The academic research on the shopping behavior of chatbots is relatively few. Even the Taobao APP, which is frequently used at present, has not formed a good user experience in the design of conversational interaction. How to improve the experience of conversational interaction is an urgent research topic. From the perspective of user experience, the behavior mode, information expression and thinking mode displayed by conversational chatbots should conform to the thinking and behavior characteristics of users. The design of conversational interaction should pay special attention to user experience in addition to technical competition. The social shortcomings of e-commerce sites are a major obstacle to the growth of companies' online businesses, and there is a lot of distrust among users of such technologies. With the disappearance of the structure and formality of the session, the difficulty of maintaining a good user experience increases and the system loses control of the human-computer interaction experience. In most studies, the focus is on the usability of conversational interactions and the ability to identify user queries.This paper mainly discusses the application of context awareness theory in conversational interaction of shopping APP, proposes a conversational interaction design process of shopping APP based on context awareness model and KANO-AHP model, and explores and summarizes the user experience-oriented conversational interaction design strategy of shopping APP by obtaining user needs qualitatively and quantitatively. On the basis of the context awareness theory, the user's experience demand set of four dimensions of conversational interaction vision, interaction, function and emotion of shopping APP is obtained based on the context awareness model from three aspects of user context factors, product context factors and environmental context factors. The KANO model is used to screen and classify the obtained demand set. The hierarchical structure of conversational interactive user needs of shopping APP is obtained. Then combined with the analytic hierarchy process (AHP) to calculate the weight of each demand and order the importance, finally put forward the design strategy. Based on the situation perception theory and KANO-AHP model to analyze user needs, the design strategies and suggestions of conversational interactive experience of shopping APP are proposed from four aspects of user visual interface experience, functional experience, interactive experience and emotional experience. In particular, some strategies such as personality characteristics, high-context conversation mechanism and the same emotional feedback mechanism are proposed to improve the user experience and satisfaction of conversational interaction of shopping APP. However, this paper only conducts in-depth research on the user side of conversational interaction of shopping APP, and obtains various experience design principles based on users. However, in the process of shopping, it is usually the transaction between merchants and users, and the subsequent research should be carried out in combination with the experience needs of merchants, so as to balance users and merchants in terms of experience.
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Yan, Rui. ""Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/778.
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Conversational AI is of growing importance since it enables easy interaction interface between humans and computers. Due to its promising potential and alluring commercial values to serve as virtual assistants and/or social chatbots, major AI, NLP, and Search & Mining conferences are explicitly calling-out for contributions from conversational studies. It is an active research area and of considerable interest. To build a conversational system with moderate intelligence is challenging, and requires abundant dialogue data and interdisciplinary techniques. Along with the Web 2.0, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversations. In general, conversational systems can be categorized into 1) task-oriented systems which aim to help users accomplish goals in vertical domains, and 2) social chat bots which can converse seamlessly and appropriately with humans, playing the role of a chat companion. In this paper, we focus on the survey of non-task-oriented chit-chat bots.
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Yu, Zhou, Alexander Rudnicky, and Alan Black. "Learning Conversational Systems that Interleave Task and Non-Task Content." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/589.
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Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit.To address this shortcoming, we propose a framework to interleave non-task content (i.e.everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content.To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.
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Nguyen, Thuy Ngoc. "Conversational Group Recommender Systems." In UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3079628.3079704.
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Christakopoulou, Konstantina, Filip Radlinski, and Katja Hofmann. "Towards Conversational Recommender Systems." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939746.
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Jacques, Richard, Asbjørn Følstad, Elizabeth Gerber, Jonathan Grudin, Ewa Luger, Andrés Monroy-Hernández, and Dakuo Wang. "Conversational Agents." In CHI '19: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3290607.3299034.
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Johnson, Ian G., Alistair MacDonald, Jo Briggs, Jennifer Manuel, Karen Salt, Emma Flynn, and John Vines. "Community Conversational." In CHI '17: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3025453.3025559.
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Lee, Minha, Renee Noortman, Cristina Zaga, Alain Starke, Gijs Huisman, and Kristina Andersen. "Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting." In CHI '21: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3411764.3445244.
Full textReports on the topic "Conversational systems"
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Murray, William R., Michelle Sams, William H. DeSmedt, and Donald Loritz. Mentor: Dialog Agent System for Mentoring and Conversational Role-Playing. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada395194.
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Noronha, Sujata, and Beena Choksi. Facing Caste: Engaging with the Privileged. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/tesf0505.2023.
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The caste system in India and the structural inequalities perpetuated by casteism are significant challenges to creating a just society. The problem is exacerbated because the education system, including libraries and school curricula, does not fully acknowledge and confront this complex issue. Historically, libraries have emerged as safe spaces for collective conversations, diverse collections and dialogue around sensitive subjects. This project worked closely with a selective sample of seven library educators—most of them with caste privilege—to: examine their knowledge, attitudes, and practices on caste issues by reading caste literature, with reflective discussions, and introspective journaling; and develop their preparedness to conceptualise, design, and pilot a library unit on caste with children from privileged backgrounds.
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Washington, Sally. Ministers and Officials: How to Get the Relationship Right. Australia and New Zealand School of Government, August 2023. http://dx.doi.org/10.54810/wqsl1871.
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Relationships between ministers and officials are fundamental to our system of government. The foundation of those relationships, like any relationship, is trust and mutual respect. It needs to be based on honesty, openness, and an understanding of each other’s roles and responsibilities. ANZSOG ran a series of events to explore both the supply and the ‘demand side’ of good policy and good decision making, and the crucial relationships at the political administrative interface. The ANZSOG series of conversations involved politicians and public servants, and were designed to shed light on what both sides can do to ensure the relationship is working at its best
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Colombo, Marea S., Ruthie E. Holmes, Cameron D. Young, and Stephen Scott. How Can Course Advising Better Support Pacific Student Success? Journal of the Australian and New Zealand Student Services Association, October 2023. http://dx.doi.org/10.30688/janzssa.2023-2-09.
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Previous research has explored the importance of effective course advising to academic success. Course advising that is student-focused and takes a “whole-of-student” approach has been found to be especially important for students that have historically been minoritised in tertiary education. This includes students who are Indigenous, first-in-family, or from low socio-economic backgrounds. However, no research, to our knowledge, has investigated how Pacific students and staff envision course advising. Given Pacific students are among the fastest growing academic cohort in New Zealand and Australia, it is important for universities to understand how to foster Pacific student success. This research involved a series of talanoa (conversations) with both Pacific students and staff to better understand the current role of course advising in student decision-making, future hopes for the development of course advising, and suggestions to improve Pacific support in course advice. Understanding the perspective of Pacific students and staff helps to highlight the current gaps in course advising systems and encourages universities to acknowledge the importance of relationship building, the development of cultural competencies, and increasing Pacific representation in the course advising process. Results support reassessing course advising systems to help improve retention rates of Pacific students.
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Houzer, Ella, and Ian Scoones. Are Livestock Always Bad for the Planet? Rethinking the Protein Transition and Climate Change Debate. Institute of Development Studies (IDS), September 2021. http://dx.doi.org/10.19088/steps.2021.003.
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Urgent climate challenges have triggered calls for radical, widespread changes in what we eat, pushing for the drastic reduction if not elimination of animal-source foods from our diets. But high-profile debates, based on patchy evidence, are failing to differentiate between varied landscapes, environments and production methods. Relatively low-impact, extensive livestock production, such as pastoralism, is being lumped in with industrial systems in the conversation about the future of food. This report warns that the dominant picture of livestock’s impacts on climate change has been distorted by faulty assumptions that focus on intensive, industrial farming in rich countries. Millions of people worldwide who depend on extensive livestock production, with relatively lower climate impacts, are being ignored by debates on the future of food. The report identifies ten flaws in the way that livestock’s climate impacts have been assessed, and suggests how pastoralists could be better included in future debates about food and the climate.
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KHUDALOVA, M., V. FILONENKO, and E. KUDZOEVA. PSYCHOSOMATICS IN CONNECTION WITH THE AFFECTIVE DISORDERS OF PERSONALITY. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-365-374.
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In recent years, we can notice a significant increase in psychosomatic disorders among borderline mental pathology, which are reasonably considered “the pathology of modern civilization”. The purpose of this study is to identify the relationship between psychosomatic disorders and the affective disorders of the personality. The study used the following methods: a diagnostic conversation and analysis of medical documents with the results of clinical examination, a scale for psychological express diagnostics of semi-structured depressive disorders (based on MMPI), a self-assessment scale by Ch.D. Spielberger - Yu.L. Hanin, Toronto Alexithymia Scale (TAS). Statistical methods of processing the empirical research results in the SPSS 22.0 program: descriptive statistics, correlation analysis (p-Spearman’s rank correlation). As a result of the study we can assert that psychosomatic disorders in respondents in the form of functional pathology of various organs and systems are connected with affective disorders in the form of moderate or severe depression of a neurotic level of various origins, alexithymia and high personal anxiety.
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Савченко, Лариса Олексіївна. Characteristic of the future specialists professional preparation to the quality educational assessment. Педагогічна думка, 2015. http://dx.doi.org/10.31812/0564/365.
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To consider the characteristics of the levels of formation of professional readiness of future specialists to pedagogical diagnostics of quality of education. Diagnostics of levels of formation of professional training of future teachers is realized through a number of research methods: observation, testing, interview, analysis of the results. The basis of the diagnostic systems research on three-level assessment scale, supplemented by «high level», which allows to adapt to local conditions and to enrich the features of a particular region. Analysis of modern works on the organization of control of educational achievements of students; the log books of progress and attendance of students in classes, conversations with teachers and our own observations have proved that in educational practice there are different models of the organization of control of educational achievements of students in pedagogical disciplines and professional subject training, validation should be carried out using various schemes and scales of evaluation present different approaches to the calculation of rating of students (in some cases even within the same University) and others. The analysis proved that the existing complex control tasks and tasks for independent work is only seventy percent of jobs differentiated by professional orientation, the rest of the job for the overall development of pedagogical competence of students. In our opinion, well developed task, that is, those that consist mainly of problems of professional and pedagogical orientation that enhance future teachers ‘ motivation to learn pedagogical disciplines. The quality of education becomes the main reference point that determines the credibility and competitiveness of educational institutions on regional, national level and international arena.
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Bragge, Peter, Veronica Delafosse, Ngo Cong-Lem, Diki Tsering, and Breanna Wright. General practitioners raising and discussing sensitive health issues with patients. The Sax Institute, June 2023. http://dx.doi.org/10.57022/rseh3974.
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This Evidence Check was commissioned by the NSW Ministry of Health, as part of a project to improve how preventive, sensitive health issues are raised in general practice. The review looked at what is known about discussing sensitive preventive health issues from both patients and GPs perspectives and approaches and factors that have been shown to be effective. The identified evidence was generally of moderate to high methodological quality. General behaviour change approaches that are applicable to this challenge include creating non-judgemental environments that normalise sensitive health issues; simulation training; and public campaigns that reduce stigma and challenge unhelpful cultural norms. Lack of time in consultations was identified as a challenging issue. Significant system-level change would be required to extend standard consultation times; focusing on optimising workflows may therefore be more feasible. Addressing GP patient–gender mismatch through diverse GP representation may also be feasible in larger practices. The key theme identified was the use of prompting, screening or other structured tools by GPs. Collectively, these approaches have two main features. First, they are a way of approaching sensitive health conversations less directly, for example by focusing on underlying risk factors for sensitive health conditions such as obesity and mental illness rather than addressing the issues directly. Second, through either risk-factor or more general question prompts, these approaches take the onus away from GPs and patients to come up with a way of asking the question using their own words.
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Mayfield, Colin. Higher Education in the Water Sector: A Global Overview. United Nations University Institute for Water, Environment and Health, May 2019. http://dx.doi.org/10.53328/guxy9244.
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Higher education related to water is a critical component of capacity development necessary to support countries’ progress towards Sustainable Development Goals (SDGs) overall, and towards the SDG6 water and sanitation goal in particular. Although the precise number is unknown, there are at least 28,000 higher education institutions in the world. The actual number is likely higher and constantly changing. Water education programmes are very diverse and complex and can include components of engineering, biology, chemistry, physics, hydrology, hydrogeology, ecology, geography, earth sciences, public health, sociology, law, and political sciences, to mention a few areas. In addition, various levels of qualifications are offered, ranging from certificate, diploma, baccalaureate, to the master’s and doctorate (or equivalent) levels. The percentage of universities offering programmes in ‘water’ ranges from 40% in the USA and Europe to 1% in subSaharan Africa. There are no specific data sets available for the extent or quality of teaching ‘water’ in universities. Consequently, insights on this have to be drawn or inferred from data sources on overall research and teaching excellence such as Scopus, the Shanghai Academic Ranking of World Universities, the Times Higher Education, the Ranking Web of Universities, the Our World in Data website and the UN Statistics Division data. Using a combination of measures of research excellence in water resources and related topics, and overall rankings of university teaching excellence, universities with representation in both categories were identified. Very few universities are represented in both categories. Countries that have at least three universities in the list of the top 50 include USA, Australia, China, UK, Netherlands and Canada. There are universities that have excellent reputations for both teaching excellence and for excellent and diverse research activities in water-related topics. They are mainly in the USA, Europe, Australia and China. Other universities scored well on research in water resources but did not in teaching excellence. The approach proposed in this report has potential to guide the development of comprehensive programmes in water. No specific comparative data on the quality of teaching in water-related topics has been identified. This report further shows the variety of pathways which most water education programmes are associated with or built in – through science, technology and engineering post-secondary and professional education systems. The multitude of possible institutions and pathways to acquire a qualification in water means that a better ‘roadmap’ is needed to chart the programmes. A global database with details on programme curricula, qualifications offered, duration, prerequisites, cost, transfer opportunities and other programme parameters would be ideal for this purpose, showing country-level, regional and global search capabilities. Cooperation between institutions in preparing or presenting water programmes is currently rather limited. Regional consortia of institutions may facilitate cooperation. A similar process could be used for technical and vocational education and training, although a more local approach would be better since conditions, regulations and technologies vary between relatively small areas. Finally, this report examines various factors affecting the future availability of water professionals. This includes the availability of suitable education and training programmes, choices that students make to pursue different areas of study, employment prospects, increasing gender equity, costs of education, and students’ and graduates’ mobility, especially between developing and developed countries. This report aims to inform and open a conversation with educators and administrators in higher education especially those engaged in water education or preparing to enter that field. It will also benefit students intending to enter the water resources field, professionals seeking an overview of educational activities for continuing education on water and government officials and politicians responsible for educational activities
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Tipton, Kelley, Brian F. Leas, Emilia Flores, Christopher Jepson, Jaya Aysola, Jordana Cohen, Michael Harhay, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), December 2023. http://dx.doi.org/10.23970/ahrqepccer268.
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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-searched reference lists of relevant studies and reviewed suggestions from experts and stakeholders. Review methods. Searches identified 11,500 unique records. Using predefined criteria and dual review, we screened and selected studies to assess one or both Key Questions (KQs): (1) the effect of algorithms on racial and ethnic disparities in health and healthcare outcomes and (2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Outcomes of interest included access to healthcare, quality of care, and health outcomes. We assessed studies’ methodologic risk of bias (ROB) using the ROBINS-I tool and piloted an appraisal supplement to assess racial and ethnic equity-related ROB. We completed a narrative synthesis and cataloged study characteristics and outcome data. We also examined four Contextual Questions (CQs) designed to explore the context and capture insights on practical aspects of potential algorithmic bias. CQ 1 examines the problem’s scope within healthcare. CQ 2 describes recently emerging standards and guidance on how racial and ethnic bias can be prevented or mitigated during algorithm development and deployment. CQ 3 explores stakeholder awareness and perspectives about the interaction of algorithms and racial and ethnic disparities in health and healthcare. We addressed these CQs through supplemental literature reviews and conversations with experts and key stakeholders. For CQ 4, we conducted an in-depth analysis of a sample of six algorithms that have not been widely evaluated before in the published literature to better understand how their design and implementation might contribute to disparities. Results. Fifty-eight studies met inclusion criteria, of which three were included for both KQs. One study was a randomized controlled trial, and all others used cohort, pre-post, or modeling approaches. The studies included numerous types of clinical assessments: need for intensive care or high-risk care management; measurement of kidney or lung function; suitability for kidney or lung transplant; risk of cardiovascular disease, stroke, lung cancer, prostate cancer, postpartum depression, or opioid misuse; and warfarin dosing. We found evidence suggesting that algorithms may: (a) reduce disparities (i.e., revised Kidney Allocation System, prostate cancer screening tools); (b) perpetuate or exacerbate disparities (e.g., estimated glomerular filtration rate [eGFR] for kidney function measurement, cardiovascular disease risk assessments); and/or (c) have no effect on racial or ethnic disparities. Algorithms for which mitigation strategies were identified are included in KQ 2. We identified six types of strategies often used to mitigate the potential of algorithms to contribute to disparities: removing an input variable; replacing a variable; adding one or more variables; changing or diversifying the racial and ethnic composition of the patient population used to train or validate a model; creating separate algorithms or thresholds for different populations; and modifying the statistical or analytic techniques used by an algorithm. Most mitigation efforts improved proximal outcomes (e.g., algorithmic calibration) for targeted populations, but it is more challenging to infer or extrapolate effects on longer term outcomes, such as racial and ethnic disparities. The scope of racial and ethnic bias related to algorithms and their application is difficult to quantify, but it clearly extends across the spectrum of medicine. Regulatory, professional, and corporate stakeholders are undertaking numerous efforts to develop standards for algorithms, often emphasizing the need for transparency, accountability, and representativeness. Conclusions. Algorithms have been shown to potentially perpetuate, exacerbate, and sometimes reduce racial and ethnic disparities. Disparities were reduced when race and ethnicity were incorporated into an algorithm to intentionally tackle known racial and ethnic disparities in resource allocation (e.g., kidney transplant allocation) or disparities in care (e.g., prostate cancer screening that historically led to Black men receiving more low-yield biopsies). It is important to note that in such cases the rationale for using race and ethnicity was clearly delineated and did not conflate race and ethnicity with ancestry and/or genetic predisposition. However, when algorithms include race and ethnicity without clear rationale, they may perpetuate the incorrect notion that race is a biologic construct and contribute to disparities. Finally, some algorithms may reduce or perpetuate disparities without containing race and ethnicity as an input. Several modeling studies showed that applying algorithms out of context of original development (e.g., illness severity scores used for crisis standards of care) could perpetuate or exacerbate disparities. On the other hand, algorithms may also reduce disparities by standardizing care and reducing opportunities for implicit bias (e.g., Lung Allocation Score for lung transplantation). Several mitigation strategies have been shown to potentially reduce the contribution of algorithms to racial and ethnic disparities. Results of mitigation efforts are highly context specific, relating to unique combinations of algorithm, clinical condition, population, setting, and outcomes. Important future steps include increasing transparency in algorithm development and implementation, increasing diversity of research and leadership teams, engaging diverse patient and community groups in the development to implementation lifecycle, promoting stakeholder awareness (including patients) of potential algorithmic risk, and investing in further research to assess the real-world effect of algorithms on racial and ethnic disparities before widespread implementation.