As artificial intelligence (AI) becomes integral to economy and society, communication gaps between developers, users, and stakeholders hinder trust and informed decision-making. High-level AI labels, inspired by frameworks like EU energy labels, have been proposed to make the properties of AI models more transparent. Without requiring deep technical expertise, they can inform on the trade-off between predictive performance and resource efficiency. However, the practical benefits and limitations of AI labeling remain underexplored. This study evaluates AI labeling through qualitative interviews along four key research questions. Based on thematic analysis and inductive coding, we found a broad range of practitioners to be interested in AI labeling (RQ1). They see benefits for alleviating communication gaps and aiding non-expert decision-makers, however limitations, misunderstandings, and suggestions for improvement were also discussed (RQ2). Compared to other reporting formats, interviewees positively evaluated the reduced complexity of labels, increasing overall comprehensibility (RQ3). Trust was influenced most by usability and the credibility of the responsible labeling authority, with mixed preferences for self-certification versus third-party certification (RQ4). Our Insights highlight that AI labels pose a trade-off between simplicity and complexity, which could be resolved by developing customizable and interactive labeling frameworks to address diverse user needs. Transparent labeling of resource efficiency also nudged interviewee priorities towards paying more attention to sustainability aspects during AI development. This study validates AI labels as a valuable tool for enhancing trust and communication in AI, offering actionable guidelines for their refinement and standardization.

This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support. The study revealed a more nuanced role for ML explanation models, when these are pragmatically embedded in the clinical context. Despite the general positive attitude of healthcare professionals (HCPs) towards explanations as a safety and trust mechanism, for a significant set of participants there were negative effects associated with confirmation bias, accentuating model over-reliance and increased effort to interact with the model. Also, contradicting one of its main intended functions, standard explanatory models showed limited ability to support a critical understanding of the limitations of the model. However, we found new significant positive effects which repositions the role of explanations within a clinical context: these include reduction of automation bias, addressing ambiguous clinical cases (cases where HCPs were not certain about their decision) and support of less experienced HCPs in the acquisition of new domain knowledge.

The deaf community is large and diverse, with members living all over the world. If you know sign language, you can communicate with a wide range of hearing, hard of hearing, and deaf people, including students in mainstream and deaf school or university programs, as well as deaf or hard of hearing citizens and business people in your community. Sign language is a type of nonverbal human communication in which the recipient receives information through hand motions. The shape of the hands, their posture, and how they move are all distinct features of each sign. When vocal communication is problematic, such as between speakers of mutually incomprehensible languages or when one or more would-be communicators is deaf, sign language can help.

The use of AI in recruitment will help bridge thecommunication gap between recruiters and candidates and provide a more enhanced experience to both, eventually leading to improved hiring. According to a study, about 83% of candidates reported that they would never apply to a job at the same company after having a bad interview experience. One of the major reasons for having a poor experience while applying for work is the delay in communication or other issues related to communication. About three out of five candidate complaints in the recruitment process are related to communication. And it's no secret that most candidate calls and emails go unanswered or get a late response from recruiters.

The use of AI in recruitment will help bridge the communication gap between recruiters and candidates and provide a more enhanced experience to both, eventually leading to improved hiring. According to a study, about 83% of candidates reported that they would never apply to a job at the same company after having a bad interview experience. One of the major reasons for having a poor experience while applying for work is the delay in communication or other issues related to communication. About three out of five candidate complaints in the recruitment process are related to communication. And it's no secret that most candidate calls and emails go unanswered or get a late response from recruiters.

The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborations, there is a knowledge mismatch between AI developers, who are skilled in data science, and external stakeholders who are typically not. This difference leads to communication gaps, and the onus falls on AI developers to explain data science concepts to their collaborators. In this paper, we report on a study including analyses of both interviews with AI developers and artifacts they produced for communication. Using the analytic lens of shared mental models, we report on the types of communication gaps that AI developers face, how AI developers communicate across disciplinary and organizational boundaries, and how they simultaneously manage issues regarding trust and expectations.

Researchers have used artificial intelligence to reduce the'communication gap' for nonverbal people with motor disabilities who rely on computers to converse with others. The team, from the University of Cambridge and the University of Dundee, developed a new context-aware method that reduces this communication gap by eliminating between 50% and 96% of the keystrokes the person has to type to communicate. "This method gives us hope for more innovative AI-infused systems to help people with motor disabilities to communicate in the future" – Per Ola Kristensson The system is specifically tailored for nonverbal people and uses a range of context'clues' – such as the user's location, the time of day or the identity of the user's speaking partner – to assist in suggesting sentences that are the most relevant for the user. Nonverbal people with motor disabilities often use a computer with speech output to communicate with others. However, even without a physical disability that affects the typing process, these communication aids are too slow and error-prone for meaningful conversation: typical typing rates are between five and 20 words per minute, while a typical speaking rate is in the range of 100 to 140 words per minute.

Reviewed by Emily Henderson, B.Sc.Jun 15 2020 Researchers have used artificial intelligence to reduce the'communication gap' for nonverbal people with motor disabilities who rely on computers to converse with others. The team, from the University of Cambridge and the University of Dundee, developed a new context-aware method that reduces this communication gap by eliminating between 50% and 96% of the keystrokes the person has to type to communicate. The system is specifically tailed for nonverbal people and uses a range of context'clues' – such as the user's location, the time of day or the identity of the user's speaking partner – to assist in suggesting sentences that are the most relevant for the user. Nonverbal people with motor disabilities often use a computer with speech output to communicate with others. However, even without a physical disability that affects the typing process, these communication aids are too slow and error prone for meaningful conversation: typical typing rates are between five and 20 words per minute, while a typical speaking rate is in the range of 100 to 140 words per minute.

Researchers have used artificial intelligence to reduce the'communication gap' for nonverbal people with motor disabilities who rely on computers to converse with others. The team, from the University of Cambridge and the University of Dundee, developed a new context-aware method that reduces this communication gap by eliminating between 50% and 96% of the keystrokes the person has to type to communicate. The system is specifically tailed for nonverbal people and uses a range of context'clues' - such as the user's location, the time of day or the identity of the user's speaking partner - to assist in suggesting sentences that are the most relevant for the user. Nonverbal people with motor disabilities often use a computer with speech output to communicate with others. However, even without a physical disability that affects the typing process, these communication aids are too slow and error prone for meaningful conversation: typical typing rates are between five and 20 words per minute, while a typical speaking rate is in the range of 100 to 140 words per minute.

Artificial intelligence is seeping into an increasing number of industries, like finance and manufacturing. Now Chicago-based Narrative Science is successfully bringing AI into writing. Founded in 2010 to automatically turn statistics into baseball stories, the organization has evolved into a powerhouse in natural-language processing. Stuart Frankel is the CEO of Narrative Science and has helped guide this transition from sports statistics to business insights. We spoke to Frankel about how technology like this is changing daily workflow in different industries and bridging the language gap between human workers and machines.