Trust Indicators and Explainable AI: A Study on User Perceptions

Nowadays, search engines, social media or news aggregators are the preferred services for news access. Aggregation is mostly based on artificial intelligence technologies raising a new challenge: Trust has been ranked as the most important factor for media business. This paper reports findings of a study evaluating the influence of manipulations of interface design and information provided in the context of eXplainable Artificial Intelligence (XAI) on user perception and in the context of news content aggregators. In an experimental online study, various layouts and scenarios have been developed, implemented and tested with 266 participants. Measures of trust, understanding and preference were recorded. Results showed no influence of the factors on trust. However, data indicates that the influence of the layout, for example implicit integration of media source through layout structuration has a significant effect on perceived importance to cite the source of a media. Moreover, the amount of information presented to explain the AI showed a negative influence on user understanding. This highlights the importance and difficulty of making XAI understandable for its users.

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

Plamen Prodanov

Mobile service robots are going to play an increasing role in the society of humans. Voice-enabled interaction with service robots becomes very important, if such robots are to be deployed in real-world environments and accepted by the vast majority of potential human users. The research presented in this thesis addresses the problem of speech recognition integration in an interactive voice-enabled interface of a service robot, in particular a tour-guide robot. The task of a tour-guide robot is to engage visitors to mass exhibitions (users) in dialogue providing the services it is designed for (e.g. exhibit presentations) within a limited time. In managing tour-guide dialogues, extracting the user goal (intention) for requesting a particular service at each dialogue state is the key issue. In mass exhibition conditions speech recognition errors are inevitable because of noisy speech and uncooperative users of robots with no prior experience in robotics. They can jeopardize the user goal identification. Wrongly identified user goals can lead to communication failures. Therefore, to reduce the risk of such failures, methods for detecting and compensating for communication failures in human-robot dialogue are needed. During the short-term interaction with visitors, the interpretation of the user goal at each dialogue state can be improved by combining speech recognition in the speech modality with information from other available robot modalities. The methods presented in this thesis exploit probabilistic models for fusing information from speech and auxiliary modalities of the robot for user goal identification and communication failure detection. To compensate for the detected communication failures we investigate multimodal methods for recovery from communication failures. To model the process of modality fusion, taking into account the uncertainties in the information extracted from each input modality during human-robot interaction, we use the probabilistic framework of Bayesian networks. Bayesian networks are graphical models that represent a joint probability function over a set of random variables. They are used to model the dependencies among variables associated with the user goals, modality related events (e.g. the event of user presence that is inferred from the laser scanner modality of the robot), and observed modality features providing evidence in favor of these modality events. Bayesian networks are used to calculate posterior probabilities over the possible user goals at each dialogue state. These probabilities serve as a base in deciding if the user goal is valid, i.e. if it can be mapped into a tour-guide service (e.g. exhibit presentation) or is undefined – signaling a possible communication failure. The Bayesian network can be also used to elicit probabilities over the modality events revealing information about the possible cause for a communication failure. Introducing new user goal aspects (e.g. new modality events and related features) that provide auxiliary information for detecting communication failures makes the design process cumbersome, calling for a systematic approach in the Bayesian network modelling. Generally, introducing new variables for user goal identification in the Bayesian networks can lead to complex and computationally expensive models. In order to make the design process more systematic and modular, we adapt principles from the theory of grounding in human communication. When people communicate, they resolve understanding problems in a collaborative joint effort of providing evidence of common shared knowledge (grounding). We use Bayesian network topologies, tailored to limited computational resources, to model a state-based grounding model fusing information from three different input modalities (laser, video and speech) to infer possible grounding states. These grounding states are associated with modality events showing if the user is present in range for communication, if the user is attending to the interaction, whether the speech modality is reliable, and if the user goal is valid. The state-based grounding model is used to compute probabilities that intermediary grounding states have been reached. This serves as a base for detecting if the the user has reached the final grounding state, or wether a repair dialogue sequence is needed. In the case of a repair dialogue sequence, the tour-guide robot can exploit the multiple available modalities along with speech. For example, if the user has failed to reach the grounding state related to her/his presence in range for communication, the robot can use its move modality to search and attract the attention of the visitors. In the case when speech recognition is detected to be unreliable, the robot can offer the alternative use of the buttons modality in the repair sequence. Given the probability of each grounding state, and the dialogue sequence that can be executed in the next dialogue state, a tour-guide robot has different preferences on the possible dialogue continuation. If the possible dialogue sequences at each dialogue state are defined as actions, the introduced principle of maximum expected utility (MEU) provides an explicit way of action selection, based on the action utility, given the evidence about the user goal at each dialogue state. Decision networks, constructed as graphical models based on Bayesian networks are proposed to perform MEU-based decisions, incorporating the utility of the actions to be chosen at each dialogue state by the tour-guide robot. These action utilities are defined taking into account the tour-guide task requirements. The proposed graphical models for user goal identification and dialogue error handling in human-robot dialogue are evaluated in experiments with multimodal data. These data were collected during the operation of the tour-guide robot RoboX at the Autonomous System Lab of EPFL and at the Swiss National Exhibition in 2002 (Expo.02). The evaluation experiments use component and system level metrics for technical (objective) and user-based (subjective) evaluation. On the component level, the technical evaluation is done by calculating accuracies, as objective measures of the performance of the grounding model, and the resulting performance of the user goal identification in dialogue. The benefit of the proposed error handling framework is demonstrated comparing the accuracy of a baseline interactive system, employing only speech recognition for user goal identification, and a system equipped with multimodal grounding models for error handling.

EPFL2006

Selection and Aggregation of Ranking Criteria for Retrieval from Scientific Publication Databases

Martin Veselý

Selection and aggregation of ranking criteria became an important topic in information retrieval as search is getting more specialized and as volume of electronically available information grows. In this context, document ranking has undergone a shift from purely content-based ranking criteria to combined ranking schemes, integrating additional fea- tures, such as document popularity or impact, or link-based techniques that were widely applied after the success of the PageRank algorithm. In this thesis we experiment with selection and aggregation of ranking criteria, aiming at increasing performance of a specialized scientific search engine. Based on theoretical foundations across several research fields including social choice theory, information re- trieval and digital libraries, we focus on ranking in databases of scientific publications in the field of High Energy Physics, identifying criteria that are pertinent for ranking scientific publications and selecting and aggregating them within a unified framework. The first issue that we address is thus identification and selection of ranking criteria for scientific documents in High Energy Physics. The criteria include the traditional information retrieval relevance that is based on the word similarity, but also the document usage, citation counts and links. In this context we present a novel ranking criterion combining the Hirsch index and the download counts, that we call the d-Hirsch index, taking into account counts of document downloads and assigning the corresponding Hirsch index directly to a document. Criteria selection is then based on correlation analysis between ranked lists of documents. We propose that correlations of entire document listings should be replaced by measuring an overlap on the top-k of the resulting list that should better reflect the independence in terms of ranking. To this end we proposed a new measure for the overlap, the Mean Average Overlap (MeanO). The second issue that we address is the aggregation framework for ranked lists of documents, where we focus on applying linear combination and models trained with ma- chine learning techniques based on logistic regression. As individual scores that are used for ranking are not necessarily comparable to each other, we describe a unified model for normalizing ranking scores before their aggregation, based on statistical properties of the underlying ranking criteria. Another contribution of our work is related to creation of a referential of relevance judgments for information retrieval experimentation in databases of scientific publications in the domain of High Energy Physics. Until now there has been no such resource avail- able that would allow to carry out evaluation of specialized information retrieval in this domain. We propose a method for automated generation of referentials, assuming that document relevance is determined by the document usage. Our approach corresponds to a modification of the pooling method, where validating of relevance judgments is not done by experts in the domain, but is inferred from previous document usage as observed in past user behaviors. The developed ranking models, methods and algorithms were validated in the frame- work of the d-Rank prototype developed in collaboration between EPFL and CERN, aiming at the design of document retrieval mechanisms and interfaces combining a multi- tude of individual specialized ranking criteria. This prototype has been integrated within the CERN institutional repository of scientific publications. We carried out several experiments to support the thesis that rank aggregation pro- vides a superior performance for specialized information retrieval than ranking with single criteria. We experimented with the CERN document server document repository, as well as with artificially generated data showing that rank aggregation can contribute to increased performance of a specialized information retrieval system, as compared to a baseline of the best individually performing ranking criteria. We showed on an experiment done with artificially generated data that if scores of individual ranking criteria follow a comparable distribution skewed to the right, then ag- gregating such ranking criteria can lead to an improvement of the ranking performance in terms of the Mean Average Precision. We reproduced this result on an experiment carried out on a real-world data set created from the CERN database of scientific publications showing that aggregating freshness and the download frequency yielded better ranking results as compared to rankings done with the download frequency and freshness sepa- rately, based on the Mean Reciprocal Rank and S@10 evaluation measures. We observed that local aggregates obtained from documents that were part of the result set performed significantly better than global aggregates, obtained through involving all documents in a collection. These results were confirmed by a 10-fold randomized cross-validation test for both the Mean Reciprocal Rank and the S@10 evaluation measure. We compared performance of the d-Hirsch index with existing ranking criteria at the CERN document server and with aggregates obtained through linear combination of individual scores and through logistic regression. We did not observe significantly better ranking results when ranking documents with the d-Hirsch index or its aggregates as compared to other ranking criteria in the test set. We suggested that selecting ranking criteria based on a novel selection criterion, the mean average overlaps between ranked lists, provides a justified basis for criteria selection.

EPFL2012

Combating Online Scientific Misinformation

Panagiotis Smeros

The drastic shift towards digital communication in our mediasphere has caused a profound change in the production and consumption of information, which in turn has substantial implications on the social and political landscape. Misinformation, as a side effect of mass information diffusion, has become a fundamental problem for governments, platforms, and the general public in light of critical events such as elections, pandemics, and wars. In this thesis, we focus on the problem of online scientific misinformation. As a starting point, we survey the evolution of misinformation and present the main characteristics and approaches against it, framing the high-level positioning of this thesis with respect to related literature. Then, we discuss three major scientific contributions of this thesis: our methods for combating claim-based, article-based, and source-based scientific misinformation.For combating claim-based scientific misinformation, we introduce SciClops, a method for detecting and contextualizing scientific claims for assisting manual fact-checking. Our method involves three steps: (1) extracting scientific claims using a domain-specific, fine-tuned transformer model, (2) clustering similar claims together with related scientific literature using a method that exploits their content and the connections among them, and (3) highlighting check-worthy claims broadcasted by popular yet unreliable sources. Our experiments show that SciClops effectively assists non-expert fact-checkers in verifying complex scientific claims, facilitating them to outperform commercial fact-checking systems.For combating article-based scientific misinformation, we introduce SciLens, a method for evaluating the quality of scientific news articles. Our method involves a series of quality indicators for news articles that derive from: (1) their content, including the use of attributed quotes, (2) their scientific context, including their semantic similarity and web proximity to the scientific literature, and (3) their social context, including their social media reach and stance. Our experiments show that these indicators help non-experts evaluate the quality of articles more accurately compared to non-experts that do not have access to these indicators. Moreover, SciLens can also produce completely automated quality scores for articles, which agree more with expert evaluators than manual evaluations done by non-experts.For combating source-based scientific misinformation, we introduce SciLander, a method for learning representations of news sources reporting on scientific topics. Our method involves heterogeneous source indicators that capture: (1) the copying of news stories between sources, (2) the semantic shift of terms across sources, (3) the usage of jargon, and (4) the stance towards specific citations. SciLander uses these indicators as signals of source agreement to train unsupervised source embeddings. Our experiments show that the learned source representations outperform state-of-the-art baselines on the task of news veracity classification while encoding information about the reliability, political leaning, and partisanship bias of these sources.In the last part of this thesis, we introduce NewsTeller, a real-time news analytics platform that runs operationally, handling daily thousands of news articles, social media reactions, and references.

EPFL2022