Believing Journalists, AI, or Fake News: The Role of Trust in Media

An increasing amount of news is generated automatically by artificial intelligence (AI). While the technology has advantages for content production, e.g., regarding efficiency in aggregating information, it is also viewed critically due to little transparency in obtaining results and possible biases. As news media are dependent on trust and credibility, introducing AI to facilitate mass communication with consumers seems to be a risky endeavor. We expand research on consumer perception of AI-based news by comparing machine-written and human-written texts to fake news and by examining the role of trust that consumers exhibit when evaluating news. Through an experiment with 263 participants, we find that consumers judge AI-based texts similar to true journalistic content when it comes to credibility, but similar to fake news regarding readability. Furthermore, our results indicate that consumers with low trust in media are less averse to AI-based texts than consumers with high trust in media

Saudi Journalists Employing Artificial Intelligence Algorithms to Detect Fake News

The study aimed at quantitative monitoring and qualitative interpretation of the perceptions and attitudes of Saudi journalists, who are the study sample, towards the use of artificial intelligence algorithms in detecting false news. Saudi artificial intelligence algorithms rely on detecting false news, based on the media survey approach, both quantitative and qualitative, through the questionnaire tool to survey a sample of (35) Saudi journalists working in journalistic news sites in the Eastern Province. The study concluded, through what was confirmed by Rogers in the theory of the spread of new ideas, and what was concluded by Davis in the technology acceptance model, in the variation of expected reactions towards the introduction and use of technology in institutions. , that the spread and application of artificial intelligence algorithms in detecting false news depends on the extent of journalists awareness of these algorithms, their conviction in them, and the extent of their awareness of their benefits and advantages, and their need and use. And the need for international press institutions to keep up with and follow the successive developments in the use of these algorithms in detecting false news, in addition to the existence of some obstacles to their use, such as the lack of incentive methods for using these algorithms. Algorithms, the high cost of obtaining such software and the poor skills of journalists. The proportions of the respondents proposals converged to enhance the benefit from the need to provide the necessary technical infrastructure in all press institutions, benefit from global experiences, maximize the resources of press institutions, and establish clear policies for working using technologies that preserve property. Also take advantage of online self-learning resources

Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism

Science and technology journalists today face challenges in finding newsworthy leads due to increased workloads, reduced resources, and expanding scientific publishing ecosystems. Given this context, we explore computational methods to aid these journalists' news discovery in terms of time-efficiency and agency. In particular, we prototyped three computational information subsidies into an interactive tool that we used as a probe to better understand how such a tool may offer utility or more broadly shape the practices of professional science journalists. Our findings highlight central considerations around science journalists' agency, context, and responsibilities that such tools can influence and could account for in design. Based on this, we suggest design opportunities for greater and longer-term user agency; incorporating contextual, personal and collaborative notions of newsworthiness; and leveraging flexible interfaces and generative models. Overall, our findings contribute a richer view of the sociotechnical system around computational news discovery tools, and suggest ways to improve such tools to better support the practices of science journalists.Comment: To be published in CSCW 202

An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions

Generative AI (GenAI) has shown remarkable capabilities in generating diverse and realistic content across different formats like images, videos, and text. In Generative AI, human involvement is essential, thus HCI literature has investigated how to effectively create collaborations between humans and GenAI systems. However, the current literature lacks a comprehensive framework to better understand Human-GenAI Interactions, as the holistic aspects of human-centered GenAI systems are rarely analyzed systematically. In this paper, we present a survey of 291 papers, providing a novel taxonomy and analysis of Human-GenAI Interactions from both human and Gen-AI perspectives. The dimensions of design space include 1) Purposes of Using Generative AI, 2) Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of Engagement, 5) Application Domains, and 6) Evaluation Strategies. Our work is also timely at the current development stage of GenAI, where the Human-GenAI interaction design is of paramount importance. We also highlight challenges and opportunities to guide the design of Gen-AI systems and interactions towards the future design of human-centered Generative AI applications

Designing Exploratory Search Systems that Stimulate Memory and Reduce Cognitive Load

From music fans finding new songs in a genre, graphic designers brainstorming ways to depict a message, and journalists scrutinizing documents for angles, people often conduct exploratory searches to understand complex topics. In contrast to traditional search, which is done to quickly answer simple questions, exploratory search is an iterative learning process that involves understanding an information space in order to find useful pieces of information. Exploratory search is composed of two, closely-related sub-processes: (1) information foraging, choosing sources and collecting information, and (2) sensemaking, organizing this information into a mental framework. Both of these sub-processes are cognitively taxing and heavily rely on our memory. For information foraging, users need to read long, complex resources and recognize useful pieces of information. For sensemaking, as users encounter more information, it becomes harder to relate new information to their current knowledge. The spreading activation theory of memory purports that the information we encounter materializes in our working memory, which spreads activation into our long-term memory, enabling us to recall related semantic information to make sense of newly found information. From this theory, this thesis introduces three strategies for creating organizations that better stimulate memory: (1) constructing overviews that are association networks that mimic our memory's structure, (2) incorporating our prior knowledge in these overviews, and (3) providing concrete information to help us make sense of abstract ideas. This thesis demonstrates how to employ these strategies through three exploratory search systems across three domains: (A) SymbolFinder helps graphic designers explore visual symbols for abstract concepts, (B) TastePaths helps music fans explore artists within a genre, and (C) AngleKindling supports journalists explore story angles for a press release. Through this body of work, I demonstrate that by designing exploratory search systems to stimulate our memory, we can make acquiring and making sense of knowledge less cognitively demanding