Imperial College London

ProfessorFrancescaToni

Faculty of EngineeringDepartment of Computing

Professor in Computational Logic
 
 
 
//

Contact

 

+44 (0)20 7594 8228f.toni Website

 
 
//

Location

 

430Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Publication Type
Year
to

367 results found

Gaskell A, Miao Y, Specia L, Toni Fet al., 2022, Logically Consistent Adversarial Attacks for Soft Theorem Provers, 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence

Conference paper

Rago A, Baroni P, Toni F, 2022, Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement, 19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022)

Conference paper

Irwin B, Rago A, Toni F, 2022, Forecasting Argumentation Frameworks, 19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022)

Conference paper

Ward F, Toni F, Belardinelli F, 2021, On agent incentives to manipulate human feedback in multi-agent reward learning scenarios, AAMAS 22, Publisher: ACM

In settings without well-defined goals, methods for reward learningallow reinforcement learning agents to infer goals from humanfeedback. Existing work has discussed the problem that such agentsmay manipulate humans, or the reward learning process, in orderto gain higher reward. We introduce the neglected problem that, inmulti-agent settings, agents may have incentives to manipulate oneanother’s reward functions in order to change each other’s behav-ioral policies. We focus on the setting with humans acting alongsideassistive (artificial) agents who must learn the reward function byinteracting with these humans. We propose a possible solution tomanipulation of human feedback in this setting: the Shared ValuePrior (SVP). The SVP equips agents with an assumption that thereward functions of all humans are similar. Given this assumption,the actions of any human provide information to an agent aboutits reward, and so the agent is incentivised to observe these actionsrather than to manipulate them. We present an expository examplein which the SVP prevents manipulation.

Conference paper

Irwin B, Rago A, Toni F, 2021, Argumentative forecasting, AAMAS 2022, Publisher: ACM

We introduce the Forecasting Argumentation Framework (FAF), anovel argumentation framework for forecasting informed by re-cent judgmental forecasting research. FAFs comprise update frame-works which empower (human or artificial) agents to argue overtime with and about probability of scenarios, whilst flagging per-ceived irrationality in their behaviour with a view to improvingtheir forecasting accuracy. FAFs include three argument types withfuture forecasts and aggregate the strength of these arguments toinform estimates of the likelihood of scenarios. We describe animplementation of FAFs for supporting forecasting agents.

Conference paper

Zhang K, Toni F, Williams M, 2021, A federated cox model with non-proportional hazards, The 6th International Workshop on ​Health Intelligence, Publisher: Springer, ISSN: 1860-949X

Recent research has shown the potential for neural networksto improve upon classical survival models such as the Cox model, whichis widely used in clinical practice. Neural networks, however, typicallyrely on data that are centrally available, whereas healthcare data arefrequently held in secure silos. We present a federated Cox model thataccommodates this data setting and also relaxes the proportional hazardsassumption, allowing time-varying covariate effects. In this latter respect,our model does not require explicit specification of the time-varying ef-fects, reducing upfront organisational costs compared to previous works.We experiment with publicly available clinical datasets and demonstratethat the federated model is able to perform as well as a standard model.

Conference paper

Sukpanichnant P, Rago A, Lertvittayakumjorn P, Toni Fet al., 2021, LRP-based argumentative explanations for neural networks, XAI.it 2021 - Italian Workshop on Explainable Artificial Intelligence, Pages: 71-84, ISSN: 1613-0073

In recent years, there have been many attempts to combine XAI with the field of symbolic AI in order to generate explanations for neural networks that are more interpretable and better align with human reasoning, with one prominent candidate for this synergy being the sub-field of computational argumentation. One method is to represent neural networks with quantitative bipolar argumentation frameworks (QBAFs) equipped with a particular semantics. The resulting QBAF can then be viewed as an explanation for the associated neural network. In this paper, we explore a novel LRP-based semantics under a new QBAF variant, namely neural QBAFs (nQBAFs). Since an nQBAF of a neural network is typically large, the nQBAF must be simplified before being used as an explanation. Our empirical evaluation indicates that the manner of this simplification is all important for the quality of the resulting explanation.

Conference paper

Rago A, Cocarascu O, Bechlivanidis C, Toni Fet al., 2021, Argumentation as a framework for interactive explanations for recommendations, KR 2020, 17th International Conference on Principles of Knowledge Representation and Reasoning, Publisher: IJCAI, Pages: 805-815, ISSN: 2334-1033

As AI systems become ever more intertwined in our personallives, the way in which they explain themselves to and inter-act with humans is an increasingly critical research area. Theexplanation of recommendations is, thus a pivotal function-ality in a user’s experience of a recommender system (RS),providing the possibility of enhancing many of its desirablefeatures in addition to itseffectiveness(accuracy wrt users’preferences). For an RS that we prove empirically is effective,we show how argumentative abstractions underpinning rec-ommendations can provide the structural scaffolding for (dif-ferent types of) interactive explanations (IEs), i.e. explana-tions empowering interactions with users. We prove formallythat these IEs empower feedback mechanisms that guaranteethat recommendations will improve with time, hence render-ing the RSscrutable. Finally, we prove experimentally thatthe various forms of IE (tabular, textual and conversational)inducetrustin the recommendations and provide a high de-gree oftransparencyin the RS’s functionality.

Conference paper

Kotonya N, Spooner T, Magazzeni D, Toni Fet al., 2021, Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification, FEVER 2021

Conference paper

Albini E, Rago A, Baroni P, Toni Fet al., 2021, Influence-driven explanations for bayesian network classifiers, PRICAI 2021, Publisher: Springer Verlag, ISSN: 0302-9743

We propose a novel approach to buildinginfluence-driven ex-planations(IDXs) for (discrete) Bayesian network classifiers (BCs). IDXsfeature two main advantages wrt other commonly adopted explanationmethods. First, IDXs may be generated using the (causal) influences between intermediate, in addition to merely input and output, variables within BCs, thus providing adeep, rather than shallow, account of theBCs’ behaviour. Second, IDXs are generated according to a configurable set of properties, specifying which influences between variables count to-wards explanations. Our approach is thusflexible and can be tailored to the requirements of particular contexts or users. Leveraging on this flexibility, we propose novel IDX instances as well as IDX instances cap-turing existing approaches. We demonstrate IDXs’ capability to explainvarious forms of BCs, and assess the advantages of our proposed IDX instances with both theoretical and empirical analyses.

Conference paper

Zylberajch H, Lertvittayakumjorn P, Toni F, 2021, HILDIF: interactive debugging of NLI models using influence functions, 1st Workshop on Interactive Learning for Natural Language Processing (InterNLP), Publisher: ASSOC COMPUTATIONAL LINGUISTICS-ACL, Pages: 1-6

Biases and artifacts in training data can cause unwelcome behavior in text classifiers (such as shallow pattern matching), leading to lack of generalizability. One solution to this problem is to include users in the loop and leverage their feedback to improve models. We propose a novel explanatory debugging pipeline called HILDIF, enabling humans to improve deep text classifiers using influence functions as an explanation method. We experiment on the Natural Language Inference (NLI) task, showing that HILDIF can effectively alleviate artifact problems in fine-tuned BERT models and result in increased model generalizability.

Conference paper

Albini E, Baroni P, Rago A, Toni Fet al., 2021, Interpreting and explaining pagerank through argumentation semantics, Intelligenza Artificiale, Vol: 15, Pages: 17-34, ISSN: 1724-8035

In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results.

Journal article

Paulino-Passos G, Toni F, 2021, Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)

Recently, abstract argumentation-based models of case-based reasoning($AA{\text -} CBR$ in short) have been proposed, originally inspired by thelegal domain, but also applicable as classifiers in different scenarios.However, the formal properties of $AA{\text -} CBR$ as a reasoning systemremain largely unexplored. In this paper, we focus on analysing thenon-monotonicity properties of a regular version of $AA{\text -} CBR$ (that wecall $AA{\text -} CBR_{\succeq}$). Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiously monotonic, a property frequently considereddesirable in the literature. We then define a variation of $AA{\text -}CBR_{\succeq}$ which is cautiously monotonic. Further, we prove that suchvariation is equivalent to using $AA{\text -} CBR_{\succeq}$ with a restrictedcasebase consisting of all "surprising" and "sufficient" cases in the originalcasebase. As a by-product, we prove that this variation of $AA{\text -}CBR_{\succeq}$ is cumulative, rationally monotonic, and empowers a principledtreatment of noise in "incoherent" casebases. Finally, we illustrate $AA{\text-} CBR$ and cautious monotonicity questions on a case study on the U.S. TradeSecrets domain, a legal casebase.

Report

Rago A, Cocarascu O, Bechlivanidis C, Lagnado D, Toni Fet al., 2021, Argumentative explanations for interactive recommendations, Artificial Intelligence, Vol: 296, Pages: 1-22, ISSN: 0004-3702

A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the systematic definition of explanations for outputs in such a way that both the explanations and the systems themselves are able to adapt to their human users' needs. In this paper we propose an RS hosting a vast repertoire of explanations, which are customisable to users in their content and format, and thus able to adapt to users' explanatory requirements, while being reasonably effective (proven empirically). Our RS is built on a graphical chassis, allowing the extraction of argumentation scaffolding, from which diverse and varied argumentative explanations for recommendations can be obtained. These recommendations are interactive because they can be questioned by users and they support adaptive feedback mechanisms designed to allow the RS to self-improve (proven theoretically). Finally, we undertake user studies in which we vary the characteristics of the argumentative explanations, showing users' general preferences for more information, but also that their tastes are diverse, thus highlighting the need for our adaptable RS.

Journal article

Cyras K, Oliveira T, Karamlou M, Toni Fet al., 2021, Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines, Argument and Computation, Vol: 12, Pages: 149-189, ISSN: 1946-2166

A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realis

Journal article

Santos M, Toni F, 2021, Artificial intelligence-empowered technology in the home, The Home in the Digital Age, Pages: 38-55, ISBN: 9780367530174

Book chapter

Dejl A, He P, Mangal P, Mohsin H, Surdu B, Voinea E, Albini E, Lertvittayakumjorn P, Rago A, Toni Fet al., 2021, Argflow: a toolkit for deep argumentative explanations for neural networks., Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems, Publisher: ACM, Pages: 1761-1763

In recent years, machine learning (ML) models have been successfully applied in a variety of real-world applications. However, theyare often complex and incomprehensible to human users. This candecrease trust in their outputs and render their usage in criticalsettings ethically problematic. As a result, several methods for explaining such ML models have been proposed recently, in particularfor black-box models such as deep neural networks (NNs). Nevertheless, these methods predominantly explain outputs in termsof inputs, disregarding the inner workings of the ML model computing those outputs. We present Argflow, a toolkit enabling thegeneration of a variety of ‘deep’ argumentative explanations (DAXs)for outputs of NNs on classification tasks.

Conference paper

Dejl A, He P, Mangal P, Mohsin H, Surdu B, Voinea E, Albini E, Lertvittayakumjorn P, Rago A, Toni Fet al., 2021, Argflow: A toolkit for deep argumentative explanations for neural networks, AAMAS, Pages: 1749-1751, ISSN: 1548-8403

In recent years, machine learning (ML) models have been successfully applied in a variety of real-world applications. However, they are often complex and incomprehensible to human users. This can decrease trust in their outputs and render their usage in critical settings ethically problematic. As a result, several methods for explaining such ML models have been proposed recently, in particular for black-box models such as deep neural networks (NNs). Nevertheless, these methods predominantly explain outputs in terms of inputs, disregarding the inner workings of the ML model computing those outputs. We present Argflow, a toolkit enabling the generation of a variety of 'deep' argumentative explanations (DAXs) for outputs of NNs on classification tasks.

Conference paper

Cyras K, Rago A, Emanuele A, Baroni P, Toni Fet al., 2021, Argumentative XAI: A Survey, The 30th International Joint Conference on Artificial Intelligence (IJCAI-21)

Conference paper

Cyras K, Heinrich Q, Toni F, 2021, Computational complexity of flat and generic assumption-based argumentation, with and without probabilities, Artificial Intelligence, Vol: 293, Pages: 1-36, ISSN: 0004-3702

Reasoning with probabilistic information has recently attracted considerable attention in argumentation, and formalisms of Probabilistic Abstract Argumentation (PAA), Probabilistic Bipolar Argumentation (PBA) and Probabilistic Structured Argumentation (PSA) have been proposed. These foundational advances have been complemented with investigations on the complexity of some approaches to PAA and PBA, but not to PSA. We study the complexity of an existing form of PSA, namely Probabilistic Assumption-Based Argumentation (PABA), a powerful, implemented formalism which subsumes several forms of PAA and other forms of PSA. Specifically, we establish membership (general upper bounds) and completeness (instantiated lower bounds) of reasoning in PABA for the class FP#P (of functions with a #P-oracle for counting the solutions of an NP problem) with respect to newly introduced probabilistic verification, credulous and sceptical acceptance function problems under several ABA semantics. As a by-product necessary to establish PABA complexity results, we provide a comprehensive picture of the ABA complexity landscape (for both flat and generic, possibly non-flat ABA) for the classical decision problems of verification, existence, credulous and sceptical acceptance under those ABA semantics.

Journal article

Rago A, Albini E, Baroni P, Toni Fet al., 2021, Influence-driven explanations for bayesian network classifiers, Publisher: arXiv

One of the most pressing issues in AI in recent years has been the need toaddress the lack of explainability of many of its models. We focus onexplanations for discrete Bayesian network classifiers (BCs), targeting greatertransparency of their inner workings by including intermediate variables inexplanations, rather than just the input and output variables as is standardpractice. The proposed influence-driven explanations (IDXs) for BCs aresystematically generated using the causal relationships between variableswithin the BC, called influences, which are then categorised by logicalrequirements, called relation properties, according to their behaviour. Theserelation properties both provide guarantees beyond heuristic explanationmethods and allow the information underpinning an explanation to be tailored toa particular context's and user's requirements, e.g., IDXs may be dialecticalor counterfactual. We demonstrate IDXs' capability to explain various forms ofBCs, e.g., naive or multi-label, binary or categorical, and also integraterecent approaches to explanations for BCs from the literature. We evaluate IDXswith theoretical and empirical analyses, demonstrating their considerableadvantages when compared with existing explanation methods.

Working paper

Albini E, Lertvittayakumjorn P, Rago A, Toni Fet al., 2021, DAX: deep argumentative eXplanation for neural networks, Publisher: arXiv

Despite the rapid growth in attention on eXplainable AI (XAI) of late,explanations in the literature provide little insight into the actualfunctioning of Neural Networks (NNs), significantly limiting theirtransparency. We propose a methodology for explaining NNs, providingtransparency about their inner workings, by utilising computationalargumentation (a form of symbolic AI offering reasoning abstractions for avariety of settings where opinions matter) as the scaffolding underpinning DeepArgumentative eXplanations (DAXs). We define three DAX instantiations (forvarious neural architectures and tasks) and evaluate them empirically in termsof stability, computational cost, and importance of depth. We also conducthuman experiments with DAXs for text classification models, indicating thatthey are comprehensible to humans and align with their judgement, while alsobeing competitive, in terms of user acceptance, with existing approaches to XAIthat also have an argumentative spirit.

Working paper

Rago A, Russo F, Albini E, Baroni P, Toni Fet al., 2021, Forging Argumentative Explanations from Causal Models, ISSN: 1613-0073

We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for models' outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the extracted bipolar AFs may be used as relation-based explanations for the outputs of causal models.

Conference paper

Lertvittayakumjorn P, Toni F, 2021, Explanation-Based Human Debugging of NLP Models: A Survey, TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, Vol: 9, Pages: 1508-1528

Journal article

Lauren S, Belardinelli F, Toni F, 2020, Aggregating Bipolar Opinions, 20th International Conference on Autonomous Agents and Multiagent Systems

Conference paper

Kotonya N, Toni F, 2020, Explainable Automated Fact-Checking: A Survey, Barcelona. Spain, 28th International Conference on Computational Linguistics (COLING 2020), Publisher: International Committee on Computational Linguistics, Pages: 5430-5443

A number of exciting advances have been made in automated fact-checkingthanks to increasingly larger datasets and more powerful systems, leading toimprovements in the complexity of claims which can be accurately fact-checked.However, despite these advances, there are still desirable functionalitiesmissing from the fact-checking pipeline. In this survey, we focus on theexplanation functionality -- that is fact-checking systems providing reasonsfor their predictions. We summarize existing methods for explaining thepredictions of fact-checking systems and we explore trends in this topic.Further, we consider what makes for good explanations in this specific domainthrough a comparative analysis of existing fact-checking explanations againstsome desirable properties. Finally, we propose further research directions forgenerating fact-checking explanations, and describe how these may lead toimprovements in the research area.v

Conference paper

Kotonya N, Toni F, 2020, Explainable Automated Fact-Checking for Public Health Claims, 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP(1) 2020), Publisher: ACL, Pages: 7740-7754

Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast major-ity of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new datasetPUBHEALTHof 11.8K claims accompanied by journalist crafted, gold standard explanations(i.e., judgments) to support the fact-check la-bels for claims1. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that,by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.

Conference paper

Lertvittayakumjorn P, Specia L, Toni F, 2020, FIND: human-in-the-loop debugging deep text classifiers

Since obtaining a perfect training dataset (i.e., a dataset which isconsiderably large, unbiased, and well-representative of unseen cases) ishardly possible, many real-world text classifiers are trained on the available,yet imperfect, datasets. These classifiers are thus likely to have undesirableproperties. For instance, they may have biases against some sub-populations ormay not work effectively in the wild due to overfitting. In this paper, wepropose FIND -- a framework which enables humans to debug deep learning textclassifiers by disabling irrelevant hidden features. Experiments show that byusing FIND, humans can improve CNN text classifiers which were trained underdifferent types of imperfect datasets (including datasets with biases anddatasets with dissimilar train-test distributions).

Working paper

Lertvittayakumjorn P, Specia L, Toni F, 2020, FIND: Human-in-the-loop debugging deep text classifiers, 2020 Conference on Empirical Methods in Natural Language Processing, Publisher: ACL

Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases)is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets. These classifiers are thus likely to have undesirable properties. For instance, they may have biases against some sub-populations or may not work effectively in the wild due to overfitting. In this paper, we propose FIND–a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features. Experiments show that by using FIND, humans can improve CNN text classifiers which were trained under different types of imperfect datasets (including datasets with biases and datasets with dissimilar train-test distributions).

Conference paper

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00154121&limit=30&person=true