Artificial Intelligence最新文献_第3页

Beyond incompatibility: Trade-offs between mutually exclusive fairness criteria in machine learning and law 超越不兼容性：机器学习和法律中互斥公平标准之间的权衡

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-01-07 DOI: 10.1016/j.artint.2024.104280

Meike Zehlike , Alex Loosley , Håkan Jonsson , Emil Wiedemann , Philipp Hacker

{"title":"Beyond incompatibility: Trade-offs between mutually exclusive fairness criteria in machine learning and law","authors":"Meike Zehlike , Alex Loosley , Håkan Jonsson , Emil Wiedemann , Philipp Hacker","doi":"10.1016/j.artint.2024.104280","DOIUrl":"10.1016/j.artint.2024.104280","url":null,"abstract":"<div><div>Fair and trustworthy AI is becoming ever more important in both machine learning and legal domains. One important consequence is that decision makers must seek to guarantee a ‘fair’, i.e., non-discriminatory, algorithmic decision procedure. However, there are several competing notions of algorithmic fairness that have been shown to be mutually incompatible under realistic factual assumptions. This concerns, for example, the widely used fairness measures of ‘calibration within groups’ and ‘balance for the positive/negative class,’ which relate to accuracy, false negative and false positive rates, respectively. In this paper, we present a novel algorithm (FAir Interpolation Method: FAIM) for continuously interpolating between these three fairness criteria. Thus, an initially unfair prediction can be remedied to meet, at least partially, a desired, weighted combination of the respective fairness conditions. We demonstrate the effectiveness of our algorithm when applied to synthetic data, the COMPAS data set, and a new, real-world data set from the e-commerce sector. We provide guidance on using our algorithm in different high-stakes contexts, and we discuss to what extent FAIM can be harnessed to comply with conflicting legal obligations. The analysis suggests that it may operationalize duties in traditional legal fields, such as credit scoring and criminal justice proceedings, but also for the latest AI regulations put forth in the EU, like the Digital Markets Act and the recently enacted AI Act.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104280"},"PeriodicalIF":5.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explain it as simple as possible, but no simpler – Explanation via model simplification for addressing inferential gap 尽可能简单地解释它，但不要更简单-通过模型简化来解决推理差距的解释

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-01-07 DOI: 10.1016/j.artint.2024.104279

Sarath Sreedharan , Siddharth Srivastava , Subbarao Kambhampati

{"title":"Explain it as simple as possible, but no simpler – Explanation via model simplification for addressing inferential gap","authors":"Sarath Sreedharan , Siddharth Srivastava , Subbarao Kambhampati","doi":"10.1016/j.artint.2024.104279","DOIUrl":"10.1016/j.artint.2024.104279","url":null,"abstract":"<div><div>One of the core challenges of explaining decisions made by modern AI systems is the need to address the potential gap in the inferential capabilities of the system generating the decision and the user trying to make sense of it. This <em>inferential capability gap</em> becomes even more critical when it comes to explaining sequential decisions. While there have been some isolated efforts at developing explanation methods suited for complex decision-making settings, most of these current efforts are limited in scope. In this paper, we introduce a general framework for generating explanations in the presence of inferential capability gaps. A framework that is grounded in the generation of simplified representations of the agent model through the application of a sequence of model simplifying transformations. This framework not only allows us to develop an extremely general explanation generation algorithm, but we see that many of the existing works in this direction could be seen as specific instantiations of our more general method. While the ideas presented in this paper are general enough to be applied to any decision-making framework, we will focus on instantiating the framework in the context of stochastic planning problems. As a part of this instantiation, we will also provide an exhaustive characterization of explanatory queries and an analysis of various classes of applicable transformations. We will evaluate the effectiveness of transformation-based explanations through both synthetic experiments and user studies.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104279"},"PeriodicalIF":5.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable AI and stakes in medicine: A user study

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-01-06 DOI: 10.1016/j.artint.2025.104282

Sam Baron , Andrew J. Latham , Somogy Varga

{"title":"Explainable AI and stakes in medicine: A user study","authors":"Sam Baron , Andrew J. Latham , Somogy Varga","doi":"10.1016/j.artint.2025.104282","DOIUrl":"10.1016/j.artint.2025.104282","url":null,"abstract":"<div><div>The apparent downsides of opaque algorithms have led to a demand for explainable AI (XAI) methods by which a user might come to understand why an algorithm produced the particular output it did, given its inputs. Patients, for example, might find that the lack of explanation of the process underlying the algorithmic recommendations for diagnosis and treatment hinders their ability to provide informed consent. This paper examines the impact of two factors on user perceptions of explanations for AI systems in medical contexts. The factors considered were the <em>stakes</em> of the decision—high versus low—and the decision source—human versus AI. 484 participants were presented with vignettes in which medical diagnosis and treatment plan recommendations were made by humans or by AI. Separate vignettes were used for <em>high stakes</em> scenarios involving life-threatening diseases, and <em>low stakes</em> scenarios involving mild diseases. In each vignette, an explanation for the decision was given. Four explanation types were tested across separate vignettes: no explanation, counterfactual, causal and a novel ‘narrative-based’ explanation, not previously considered. This yielded a total of 16 conditions, of which each participant saw only one. Individuals were asked to evaluate the explanations they received based on helpfulness, understanding, consent, reliability, trust, interests and likelihood of undergoing treatment. We observed a main effect for stakes on all factors and a main effect for decision source on all factors except for helpfulness and likelihood to undergo treatment. While we observed effects for explanation on helpfulness, understanding, consent, reliability, trust and interests, we by and large did not see any differences between the effects of explanation types. This suggests that the effectiveness of explanations may not depend on type of explanation but instead, on the stakes and decision source.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104282"},"PeriodicalIF":5.1,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143318245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

CureGraph: Contrastive multi-modal graph representation learning for urban living circle health profiling and prediction CureGraph：用于城市生活圈健康概况和预测的对比多模态图表示学习

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-01-02 DOI: 10.1016/j.artint.2024.104278

Jinlin Li, Xiao Zhou

{"title":"CureGraph: Contrastive multi-modal graph representation learning for urban living circle health profiling and prediction","authors":"Jinlin Li, Xiao Zhou","doi":"10.1016/j.artint.2024.104278","DOIUrl":"10.1016/j.artint.2024.104278","url":null,"abstract":"<div><div>The early detection and prediction of health status decline among the elderly at the neighborhood level are of great significance for urban planning and public health policymaking. While existing studies affirm the connection between living environments and health outcomes, most rely on single data modalities or simplistic feature concatenation of multi-modal information, limiting their ability to comprehensively profile the health-oriented urban environments. To fill this gap, we propose <strong>CureGraph</strong>, a <strong>c</strong>ontrastive m<strong>u</strong>lti-modal <strong>r</strong>epresentation learning framework for urban h<strong>e</strong>alth prediction that employs <strong>graph</strong>-based techniques to infer the prevalence of common chronic diseases among the elderly within the urban living circles of each neighborhood. CureGraph leverages rich multi-modal information, including photos and textual reviews of residential areas and their surrounding points of interest, to generate urban neighborhood embeddings. By integrating pre-trained visual and textual encoders with graph modeling techniques, CureGraph captures cross-modal spatial dependencies, offering a comprehensive understanding of urban environments tailored to elderly health considerations. Extensive experiments on real-world datasets demonstrate that CureGraph improves the best baseline by 28% on average in terms of <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> across elderly disease risk prediction tasks. Moreover, the model enables the identification of stage-wise chronic disease progression and supports comparative public health analysis across neighborhoods, offering actionable insights for sustainable urban development and enhanced quality of life. The code is publicly available at <span><span>https://github.com/jinlin2021/CureGraph</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104278"},"PeriodicalIF":5.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A semantic framework for neurosymbolic computation 神经符号计算的语义框架

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-12-31 DOI: 10.1016/j.artint.2024.104273

Simon Odense , Artur d'Avila Garcez

{"title":"A semantic framework for neurosymbolic computation","authors":"Simon Odense , Artur d'Avila Garcez","doi":"10.1016/j.artint.2024.104273","DOIUrl":"10.1016/j.artint.2024.104273","url":null,"abstract":"<div><div>The field of neurosymbolic AI aims to benefit from the combination of neural networks and symbolic systems. A cornerstone of the field is the translation or <em>encoding</em> of symbolic knowledge into neural networks. Although many neurosymbolic methods and approaches have been proposed, and with a large increase in recent years, no common definition of encoding exists that can enable a precise, theoretical comparison of neurosymbolic methods. This paper addresses this problem by introducing a semantic framework for neurosymbolic AI. We start by providing a formal definition of <em>semantic encoding</em>, specifying the components and conditions under which a knowledge-base can be encoded correctly by a neural network. We then show that many neurosymbolic approaches are accounted for by this definition. We provide a number of examples and correspondence proofs applying the proposed framework to the neural encoding of various forms of knowledge representation. Many, at first sight disparate, neurosymbolic methods, are shown to fall within the proposed formalization. This is expected to provide guidance to future neurosymbolic encodings by placing them in the broader context of semantic encodings of entire families of existing neurosymbolic systems. The paper hopes to help initiate a discussion around the provision of a theory for neurosymbolic AI and a semantics for deep learning.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104273"},"PeriodicalIF":5.1,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Athanor: Local search over abstract constraint specifications Athanor：抽象约束规范的局部搜索

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-12-27 DOI: 10.1016/j.artint.2024.104277

Saad Attieh , Nguyen Dang , Christopher Jefferson , Ian Miguel , Peter Nightingale

{"title":"Athanor: Local search over abstract constraint specifications","authors":"Saad Attieh , Nguyen Dang , Christopher Jefferson , Ian Miguel , Peter Nightingale","doi":"10.1016/j.artint.2024.104277","DOIUrl":"10.1016/j.artint.2024.104277","url":null,"abstract":"<div><div>Local search is a common method for solving combinatorial optimisation problems. We focus on general-purpose local search solvers that accept as input a constraint model — a declarative description of a problem consisting of a set of decision variables under a set of constraints. Existing approaches typically take as input models written in solver-independent constraint modelling languages like MiniZinc. The <span>Athanor</span> solver we describe herein differs in that it begins from a specification of a problem in the abstract constraint specification language <span>Essence</span>, which allows problems to be described without commitment to low-level modelling decisions through its support for a rich set of abstract types. The advantage of proceeding from <span>Essence</span> is that the structure apparent in a concise, abstract specification of a problem can be exploited to generate high quality neighbourhoods automatically, avoiding the difficult task of identifying that structure in an equivalent constraint model. Based on the twin benefits of neighbourhoods derived from high level types and the scalability derived by searching directly over those types, our empirical results demonstrate strong performance in practice relative to existing solution methods.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104277"},"PeriodicalIF":5.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

A simple proof-theoretic characterization of stable models: Reduction to difference logic and experiments 稳定模型的一个简单的证明理论表征：归约到差分逻辑和实验

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-12-24 DOI: 10.1016/j.artint.2024.104276

Martin Gebser , Enrico Giunchiglia , Marco Maratea , Marco Mochi

{"title":"A simple proof-theoretic characterization of stable models: Reduction to difference logic and experiments","authors":"Martin Gebser , Enrico Giunchiglia , Marco Maratea , Marco Mochi","doi":"10.1016/j.artint.2024.104276","DOIUrl":"10.1016/j.artint.2024.104276","url":null,"abstract":"<div><div>Stable models of logic programs have been studied and characterized in relation with other formalisms by many researchers. As already argued in previous papers, such characterizations are interesting for diverse reasons, including theoretical investigations and the possibility of leading to new algorithms for computing stable models of logic programs. At the theoretical level, complexity and expressiveness comparisons have brought about fundamental insights. Beyond that, practical implementations of the developed reductions enable the use of existing solvers for other logical formalisms to compute stable models. In this paper, we first provide a simple characterization of stable models that can be viewed as a proof-theoretic counterpart of the standard model-theoretic definition. We further show how it can be naturally encoded in difference logic. Such an encoding, compared to the existing reductions to classical logics, does not require Boolean variables. Then, we implement our novel translation to a Satisfiability Modulo Theories (SMT) formula. We finally compare our approach, employing the SMT solver <span>yices</span>, to the translation-based ASP solver <span>lp2diff</span> and to <span>clingo</span> on domains from the “Basic Decision” track of the 2017 Answer Set Programming competition. The results show that our approach is competitive to and often better than <span>lp2diff</span>, and that it can also be faster than <span>clingo</span> on non-tight domains.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104276"},"PeriodicalIF":5.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Defying catastrophic forgetting via influence function 通过影响函数对抗灾难性遗忘

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-11-27 DOI: 10.1016/j.artint.2024.104261

Rui Gao, Weiwei Liu

引用次数: 0

Integrating symbolic reasoning into neural generative models for design generation 将符号推理集成到神经生成模型中进行设计生成

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-11-19 DOI: 10.1016/j.artint.2024.104257

Maxwell J. Jacobson, Yexiang Xue

{"title":"Integrating symbolic reasoning into neural generative models for design generation","authors":"Maxwell J. Jacobson, Yexiang Xue","doi":"10.1016/j.artint.2024.104257","DOIUrl":"10.1016/j.artint.2024.104257","url":null,"abstract":"<div><div>Design generation requires tight integration of neural and symbolic reasoning, as good design must meet explicit user needs and honor implicit rules for aesthetics, utility, and convenience. Current automated design tools driven by neural networks produce appealing designs, but cannot satisfy user specifications and utility requirements. Symbolic reasoning tools, such as constraint programming, cannot perceive low-level visual information in images or capture subtle aspects such as aesthetics. We introduce the Spatial Reasoning Integrated Generator (SPRING) for design generation. SPRING embeds a neural and symbolic integrated spatial reasoning module inside the deep generative network. The spatial reasoning module samples the set of locations of objects to be generated from a backtrack-free distribution. This distribution modifies the implicit preference distribution, which is learned by a recurrent neural network to capture utility and aesthetics. The sampling from the backtrack-free distribution is accomplished by a symbolic reasoning approach, SampleSearch, which zeros out the probability of sampling spatial locations violating explicit user specifications. Embedding symbolic reasoning into neural generation guarantees that the output of SPRING satisfies user requirements. Furthermore, SPRING offers interpretability, allowing users to visualize and diagnose the generation process through the bounding boxes. SPRING is also adept at managing novel user specifications not encountered during its training, thanks to its proficiency in zero-shot constraint transfer. Quantitative evaluations and a human study reveal that SPRING outperforms baseline generative models, excelling in delivering high design quality and better meeting user specifications.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104257"},"PeriodicalIF":5.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lifted action models learning from partial traces 从部分轨迹学习的提升行动模型

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-11-15 DOI: 10.1016/j.artint.2024.104256

Leonardo Lamanna , Luciano Serafini , Alessandro Saetti , Alfonso Emilio Gerevini , Paolo Traverso

{"title":"Lifted action models learning from partial traces","authors":"Leonardo Lamanna , Luciano Serafini , Alessandro Saetti , Alfonso Emilio Gerevini , Paolo Traverso","doi":"10.1016/j.artint.2024.104256","DOIUrl":"10.1016/j.artint.2024.104256","url":null,"abstract":"<div><div>For applying symbolic planning, there is the necessity of providing the specification of a symbolic action model, which is usually manually specified by a domain expert. However, such an encoding may be faulty due to either human errors or lack of domain knowledge. Therefore, learning the symbolic action model in an automated way has been widely adopted as an alternative to its manual specification. In this paper, we focus on the problem of learning action models offline, from an input set of partially observable plan traces. In particular, we propose an approach to: <em>(i)</em> augment the observability of a given plan trace by applying predefined logical rules; <em>(ii)</em> learn the preconditions and effects of each action in a plan trace from partial observations before and after the action execution. We formally prove that our approach learns action models with fundamental theoretical properties, not provided by other methods. We experimentally show that our approach outperforms a state-of-the-art method on a large set of existing benchmark domains. Furthermore, we compare the effectiveness of the learned action models for solving planning problems and show that the action models learned by our approach are much more effective w.r.t. a state-of-the-art method.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104256"},"PeriodicalIF":5.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0