Ai Magazine最新文献

{"title":"Combating misinformation in the age of LLMs: Opportunities and challenges","authors":"Canyu Chen,&nbsp;Kai Shu","doi":"10.1002/aaai.12188","DOIUrl":"https://doi.org/10.1002/aaai.12188","url":null,"abstract":"<p>Misinformation such as fake news and rumors is a serious threat for information ecosystems and public trust. The emergence of large language models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double-edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emerging question is: <i>can we utilize LLMs to combat misinformation?</i> On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is: <i>how to combat LLM-generated misinformation?</i> In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions, respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM-generated misinformation.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 3","pages":"354-368"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Food information engineering","authors":"Azanzi Jiomekong,&nbsp;Allard Oelen,&nbsp;Soren Auer,&nbsp;Lorenz Anna-Lena,&nbsp;Vogt Lars","doi":"10.1002/aaai.12185","DOIUrl":"https://doi.org/10.1002/aaai.12185","url":null,"abstract":"<p>Food information engineering relies on statistical and AI techniques (e.g., symbolic, connectionist, and neurosymbolic AI) for collecting, storing, processing, diffusing, and putting food information in a form exploitable by humans and machines. Food information is collected manually and automatically. Once collected, food information is organized using tabular data representation schema, symbolic, connectionist or neurosymbolic AI techniques. Once collected, processed, and stored, food information is diffused to different stakeholders using appropriate formats. Even if neurosymbolic AI has shown promising results in many domains, we found that this approach is rarely used in the domain of food information engineering. This paper aims to serve as a good reference for food information engineering researchers. Unlike existing reviews on the subject, we cover all the aspects of food information engineering and we linked the paper to online resources built using Open Research Knowledge Graph. These resources are composed of templates, comparison tables of research contributions and smart reviews. All these resources are organized in the “Food Information Engineering” observatory and will be continually updated with new research contributions.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 3","pages":"338-353"},"PeriodicalIF":2.5,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"XAI is in trouble","authors":"Rosina O Weber,&nbsp;Adam J Johs,&nbsp;Prateek Goel,&nbsp;João Marques Silva","doi":"10.1002/aaai.12184","DOIUrl":"https://doi.org/10.1002/aaai.12184","url":null,"abstract":"<p>Researchers focusing on how artificial intelligence (AI) methods explain their decisions often discuss controversies and limitations. Some even assert that most publications offer little to no valuable contributions. In this article, we substantiate the claim that explainable AI (XAI) is in trouble by describing and illustrating four problems: the disagreements on the scope of XAI, the lack of definitional cohesion, precision, and adoption, the issues with motivations for XAI research, and limited and inconsistent evaluations. As we delve into their potential underlying sources, our analysis finds these problems seem to originate from AI researchers succumbing to the pitfalls of interdisciplinarity or from insufficient scientific rigor. Analyzing these potential factors, we discuss the literature at times coming across unexplored research questions. Hoping to alleviate existing problems, we make recommendations on precautions against the challenges of interdisciplinarity and propose directions in support of scientific rigor.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 3","pages":"300-316"},"PeriodicalIF":2.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of the EU AI act calls for interdisciplinary governance","authors":"Huixin Zhong","doi":"10.1002/aaai.12183","DOIUrl":"10.1002/aaai.12183","url":null,"abstract":"<p>The European Union Parliament passed the EU AI Act in 2024, which is an important milestone towards the world's first comprehensive AI law to formally take effect. Although this is a significant achievement, the real work begins with putting these rules into action, a journey filled with challenges and opportunities. This perspective article reviews recent interdisciplinary research aimed at facilitating the implementation of the prohibited AI practices outlined in the EU AI Act. It also explores the necessary future efforts to effectively enforce the banning of those prohibited practices across the EU market and the challenges associated with such enforcement. Addressing these future tasks and challenges calls for the establishment of an interdisciplinary governance framework. This framework may contain a workflow that can identify the necessary expertise and coordinate experts’ collaboration at different stages of AI governance. Additionally, it involves developing and implementing a set of compliance and ethical safeguards to ensure effective management and supervision of AI practices.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 3","pages":"333-337"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In search of verifiability: Explanations rarely enable complementary performance in AI-advised decision making","authors":"Raymond Fok,&nbsp;Daniel S. Weld","doi":"10.1002/aaai.12182","DOIUrl":"https://doi.org/10.1002/aaai.12182","url":null,"abstract":"<p>The current literature on AI-advised decision making—involving explainable AI systems advising human decision makers—presents a series of inconclusive and confounding results. To synthesize these findings, we propose a simple theory that elucidates the frequent failure of AI explanations to engender appropriate reliance and complementary decision making performance. In contrast to other common desiderata, for example, interpretability or spelling out the AI's reasoning process, we argue that explanations are only useful to the extent that they <i>allow a human decision maker to verify the correctness of the AI's prediction</i>. Prior studies find in many decision making contexts that AI explanations <i>do not</i> facilitate such verification. Moreover, most tasks fundamentally do not allow easy verification, regardless of explanation method, limiting the potential benefit of any type of explanation. We also compare the objective of complementary performance with that of appropriate reliance, decomposing the latter into the notions of outcome-graded and strategy-graded reliance.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 3","pages":"317-332"},"PeriodicalIF":2.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new era of AI-assisted journalism at Bloomberg","authors":"Claudia Quinonez,&nbsp;Edgar Meij","doi":"10.1002/aaai.12181","DOIUrl":"10.1002/aaai.12181","url":null,"abstract":"&lt;p&gt;Artificial intelligence (AI) is impacting and has the potential to upend entire business models and structures. The adoption of such new technologies to support newsgathering processes is established practice for newsrooms. For AI specifically, we are seeing a new era of AI-assisted journalism emerge with trust in the AI-driven analyses and accuracy of results as core tenets.&lt;/p&gt;&lt;p&gt;In Part I of this position paper, we discuss the contributions of six recently published research papers co-authored by Bloomberg's Artificial Intelligence Engineering team that show the intricacies of training AI models for reliable newsgathering processes. The papers investigate (a) the creation of models for updated headline generation, showing that headline generation models benefit from access to the past state of the article, (b) sequentially controlled text generation, which is a novel task and we show that in general, more structured awareness results in higher control accuracy and grammatical coherence, (c) chart summarization, which looks into identifying the key message and generating sentences that describe salient information in the multimodal documents, (d) a semistructured natural language inference task to develop a framework for data augmentation for tabular inference, (e) the introduction of a human-annotated dataset (ENTSUM) for controllable summarization with a focus on named entities as the aspect to control, and (f) a novel defense mechanism against adversarial attacks (ATINTER). We also examine Bloomberg's research work, building its own internal, not-for-commercial-use large language model, BloombergGPT, and training it with the goal of demonstrating support for a wide range of tasks within the financial industry.&lt;/p&gt;&lt;p&gt;In Part II, we analyze the evolution of automation tasks in the Bloomberg newsroom that led to the creation of Bloomberg's News Innovation Lab. Technology-assisted content creation has been a reality at Bloomberg News for nearly a decade and has evolved from rules-based headline generation from structured files to the constant exploration of potential ways to assist story creation and storytelling in the financial domain. The Lab now oversees the operation of hundreds of software bots that create semi- and fully automated stories of financial relevance, providing journalists with depth in terms of data and analysis, speed in terms of reacting to breaking news, and transparency to corners of the financial world where data investigation is a gigantic undertaking. The Lab recently introduced new tools that provide journalists with the ability to explore automation on demand while it continues to experiment with ways to assist story production.&lt;/p&gt;&lt;p&gt;In Part III, we conceptually discuss the transformative impact that generative AI can have in any newsroom, along with considerations about the technology's shortcomings in its current state of development. As with any revolutionary new technology, as well as with exciting research op","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"187-199"},"PeriodicalIF":0.9,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the impact of automated correction of misinformation in social media","authors":"Grégoire Burel,&nbsp;Mohammadali Tavakoli,&nbsp;Harith Alani","doi":"10.1002/aaai.12180","DOIUrl":"10.1002/aaai.12180","url":null,"abstract":"<p>Correcting misinformation is a complex task, influenced by various psychological, social, and technical factors. Most research evaluation methods for identifying effective correction approaches tend to rely on either crowdsourcing, questionnaires, lab-based simulations, or hypothetical scenarios. However, the translation of these methods and findings into real-world settings, where individuals willingly and freely disseminate misinformation, remains largely unexplored. Consequently, we lack a comprehensive understanding of how individuals who share misinformation in natural online environments would respond to corrective interventions. In this study, we explore the effectiveness of corrective messaging on 3898 users who shared misinformation on Twitter/X over 2 years. We designed and deployed a bot to automatically identify individuals who share misinformation and subsequently alert them to related fact-checks in various message formats. Our analysis shows that only a small minority of users react positively to the corrective messages, with most users either ignoring them or reacting negatively. Nevertheless, we also found that more active users were proportionally more likely to react positively to corrections and we observed that different message tones made particular user groups more likely to react to the bot.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"227-245"},"PeriodicalIF":0.9,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial: AI and the news","authors":"Andreas L. Opdahl,&nbsp;Natali Helberger,&nbsp;Nicholas Diakopoulos","doi":"10.1002/aaai.12179","DOIUrl":"https://doi.org/10.1002/aaai.12179","url":null,"abstract":"&lt;p&gt;In a time of rising populism and strategic disinformation, quality journalism has become more important than ever. Trusted and high-quality media outlets are needed to provide accurate information to the public in order to protect public safety and wellbeing while supporting the information needs of citizens in order to promote healthy liberal democracies. But quality journalism is also under pressure due to competition for attention from new information channels, declining trust in institutions, and dwindling resources to support the information needs of local communities while there are simultaneously new resource demands to mitigate the impacts of mis- and disinformation. Given this challenging context, how can Artificial Intelligence (AI) support the provision of quality information for society?&lt;/p&gt;&lt;p&gt;This special issue therefore examines how ongoing advances in AI, including Machine Learning (ML), and generative AI such as Large Language Models (LLMs), can be harnessed to support efficient production and distribution of high-quality news. It takes a broad outlook on the area, including articles that deal with uses and implications of AI in all stages of news production and dissemination, from gathering and analyzing information to creating, presenting, or recommending news content, while also dealing with an onslaught of mis- and disinformation in the broader online information ecosystem. It also discusses AI on different levels, from individual news production tasks, through organizational transformations and ramifications, to societal and economic conditions and consequences. A common red thread throughout the articles is that AI has great transformational potential, also in the media sector, but the factors driving and enabling such transformations are not only technological. Such factors also very much pertain to the broader organizational, infrastructure and economic context, and successful alignment of the different actors along the value chain, including media users.&lt;/p&gt;&lt;p&gt;The articles presented here offer an optimistic picture of how quality information and the media ecosystem might evolve in positive ways in light of the technological change driven by AI. And while critical approaches and research are by all means warranted such that professional ethical commitments are maintained, we hope this collection at least provides some ideas and inspiration for technologists and other stakeholders to engage further with how to orient their work towards addressing problems, seeking fruitful cooperations with the different stakeholders along the value chain and providing benefits to support quality media production.&lt;/p&gt;&lt;p&gt;Next, we outline the six articles in the collection providing a brief summary of each to orient the reader.&lt;/p&gt;&lt;p&gt;LLMs and other generative AI technologies are ushering in a new phase of disruption in the news industry that may affect news production and consumption as well as distribution. David Caswell, in his paper &lt;i&gt;","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"172-173"},"PeriodicalIF":0.9,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming the value chain of local journalism with artificial intelligence","authors":"Bartosz Wilczek,&nbsp;Mario Haim,&nbsp;Neil Thurman","doi":"10.1002/aaai.12174","DOIUrl":"https://doi.org/10.1002/aaai.12174","url":null,"abstract":"<p>With their advertising and audience revenues in decline, local news organizations have been experiencing comparatively high degrees of disruption in recent years. Artificial Intelligence (AI) offers opportunities for local news organizations to better cope with the economic challenges they face. However, local news organizations need to carefully prioritize where AI will create the most value. After all, they serve customers in the audience and advertising markets, with external effects on society. At the same time, they are limited by scarce resources, which constrains the implementation of AI. Therefore, based on Porter's value chain, this article pursues two goals. First, drawing on previous research, we provide a systematic overview of activities for which local news organizations see the biggest potential of AI to create value. Moreover, we highlight promising AI use cases based on benchmarking with national news organizations. Second, we discuss local news organizations’ challenges in implementing AI and how they might overcome such obstacles.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"200-211"},"PeriodicalIF":0.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improve robustness of machine learning via efficient optimization and conformal prediction","authors":"Yan Yan","doi":"10.1002/aaai.12173","DOIUrl":"10.1002/aaai.12173","url":null,"abstract":"<p>The advance of machine learning (ML) systems in real-world scenarios usually expects safe deployment in high-stake applications (e.g., medical diagnosis) for critical decision-making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance-regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an <i>end-to-end</i> deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample-efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP-based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk-aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 2","pages":"270-279"},"PeriodicalIF":0.9,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}