Personalized Chatbot Trustworthiness Ratings

Srivastava, Biplav; Rossi, Francesca; Usmani, Sheema; Bernagozzi, Mariana

doi:10.1109/tts.2020.3023919

Cited by 24 publications

(6 citation statements)

References 18 publications

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“…We know that in healthcare, a perceived relationship of trust and mutual understanding can be vital for successful therapy. 39 Chatbots that facilitate emotional connections and that promote trust 40 and understanding may therefore benefit a range of user groups.…”

Section: Chatbots For Mental Health: Some Benefits and Challengesmentioning

confidence: 99%

To chat or bot to chat: Ethical issues with using chatbots in mental health

Coghlan,

Leins,

Sheldrick

et al. 2023

DIGITAL HEALTH

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This paper presents a critical review of key ethical issues raised by the emergence of mental health chatbots. Chatbots use varying degrees of artificial intelligence and are increasingly deployed in many different domains including mental health. The technology may sometimes be beneficial, such as when it promotes access to mental health information and services. Yet, chatbots raise a variety of ethical concerns that are often magnified in people experiencing mental ill-health. These ethical challenges need to be appreciated and addressed throughout the technology pipeline. After identifying and examining four important ethical issues by means of a recognised ethical framework comprised of five key principles, the paper offers recommendations to guide chatbot designers, purveyers, researchers and mental health practitioners in the ethical creation and deployment of chatbots for mental health.

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Section: Chatbots For Mental Health: Some Benefits and Challengesmentioning

confidence: 99%

To chat or bot to chat: Ethical issues with using chatbots in mental health

Coghlan,

Leins,

Sheldrick

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…There are many emerging techniques to handle them and in particular, rating chatbot for trustworthiness based on automated independent testing, so that users are aware of the chatbot behavior (Srivastava et al. 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Other significant trust issues with chatbots relate to usage of abusive language, leaking information, and usage of complex terms (Henderson et al 2018). There are many emerging techniques to handle them and in particular, rating chatbot for trustworthiness based on automated independent testing, so that users are aware of the chatbot behavior (Srivastava et al 2020). Domain-specific "Do Not Answer" prototypes are yet to be explored at a large scale.…”

Section: Discussionmentioning

confidence: 99%

On safe and usable chatbots for promoting voter participation

Muppasani,

Pallagani,

Lakkaraju

et al. 2023

AI Magazine

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Chatbots, or bots for short, are multimodal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis‐information and hacking. Instead, in this work, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first‐time voters. In particular, we have built a system that amplifies official information while personalizing it to users' unique needs transparently (e.g., language, cognitive abilities, linguistic abilities). The uniqueness of this work are (a) a safe design where only responses that are grounded and traceable to an allowed source (e.g., official question/answer) will be answered via system's self‐awareness (metacognition), (b) a do‐not‐respond strategy that can handle customizable responses/deflection, and (c) a low‐programming design‐pattern based on the open‐source Rasa platform to generate chatbots quickly for any region. Our current prototypes use frequently asked questions (FAQ) election information for two US states that are low on an ease‐of‐voting scale, and have performed initial evaluations using focus groups with senior citizens. Our approach can be a win‐win for voters, election agencies trying to fulfill their mandate and democracy at large.

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“…The areas where the current research and application work are more focused are AI-based social infrastructure and management and business applications. Tables 13 and 14 summarize common applications and case studies with a comparison of the approaches and challenges, respectively, including education [273][274][275][276][277] health care [52,[278][279][280] criminal justice and sentencing [88,[281][282][283], hiring and recruiting [284][285][286], lending and credit decisions [287][288][289][290][291][292], online advertising [8,[293][294][295], customer service and chatbots [296][297][298][299][300][301][302][303].…”

Section: Ai Fairness In Practicementioning

confidence: 99%

AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

Chen,

Wu,

Wang

2023

Applied Sciences

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This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and fairness training as key research directions. The paper explains in detail the concept, implementation, characteristics, and use cases of each method. The paper explores strategies to reduce bias and improve fairness in AI systems, reviews challenges and solutions to real-world AI fairness applications, and proposes future research directions. In addition, this study provides an in-depth comparative analysis of the various approaches, utilizing cutting-edge research information to elucidate their different characteristics, strengths, and weaknesses. The results of the comparison provide guidance for future research. The paper concludes with an overview of existing challenges in practical applications and suggests priorities and solutions for future research. The conclusions provide insights for promoting fairness in AI systems. The information reviewed in this paper is drawn from reputable sources, including leading academic journals, prominent conference proceedings, and well-established online repositories dedicated to AI fairness. However, it is important to recognize that research nuances, sample sizes, and contextual factors may create limitations that affect the generalizability of the findings.

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Personalized Chatbot Trustworthiness Ratings

Cited by 24 publications

References 18 publications

To chat or bot to chat: Ethical issues with using chatbots in mental health

To chat or bot to chat: Ethical issues with using chatbots in mental health

On safe and usable chatbots for promoting voter participation

AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

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