Interpretable Instance-Based Text Classification for Social Science Research Projects

In this study, two groups of respondents have evaluated explanations generated from an instance-based explanation method called WITE (Weighted Instance-based Text Explanations). One group consisted of 24 non-experts who answered a web survey about the words characterising the concepts of the classes and the other group consisted of three senior researchers and three respondents from a media house in Sweden who answered a questionnaire with open questions. The data used originates from one of the researchers’ project on media consumption in Sweden. The results from the non-experts indicate that WITE identified many words that corresponded to the human understanding but also included some insignificant or contrary words as important. In the results from the expert evaluation, there were indications that there is a risk that the explanations could persuade the users of the correctness of a prediction, even if it is incorrect. Consequently, the study indicates that an explanation method could be seen as a new actor which is able to persuade and interact with the humans and cause a change in the results of the classification of a text

Calibrated Explanations for Regression

Artificial Intelligence (AI) is often an integral part of modern decision support systems (DSSs). The best-performing predictive models used in AI-based DSSs lack transparency. Explainable Artificial Intelligence (XAI) aims to create AI systems that can explain their rationale to human users. Local explanations in XAI can provide information about the causes of individual predictions in terms of feature importance. However, a critical drawback of existing local explanation methods is their inability to quantify the uncertainty associated with a feature's importance. This paper introduces an extension of a feature importance explanation method, Calibrated Explanations (CE), previously only supporting classification, with support for standard regression and probabilistic regression, i.e., the probability that the target is above an arbitrary threshold. The extension for regression keeps all the benefits of CE, such as calibration of the prediction from the underlying model with confidence intervals, uncertainty quantification of feature importance, and allows both factual and counterfactual explanations. CE for standard regression provides fast, reliable, stable, and robust explanations. CE for probabilistic regression provides an entirely new way of creating probabilistic explanations from any ordinary regression model and with a dynamic selection of thresholds. The performance of CE for probabilistic regression regarding stability and speed is comparable to LIME. The method is model agnostic with easily understood conditional rules. An implementation in Python is freely available on GitHub and for installation using pip making the results in this paper easily replicable.Comment: 30 pages, 11 figures (replaced due to omitted author, which is the only change made

Trustworthy explanations : Improved decision support through well-calibrated uncertainty quantification

The use of Artificial Intelligence (AI) has transformed fields like disease diagnosis and defence. Utilising sophisticated Machine Learning (ML) models, AI predicts future events based on historical data, introducing complexity that challenges understanding and decision-making. Previous research emphasizes users’ difficulty discerning when to trust predictions due to model complexity, underscoring addressing model complexity and providing transparent explanations as pivotal for facilitating high-quality decisions. Many ML models offer probability estimates for predictions, commonly used in methods providing explanations to guide users on prediction confidence. However, these probabilities often do not accurately reflect the actual distribution in the data, leading to potential user misinterpretation of prediction trustworthiness. Additionally, most explanation methods fail to convey whether the model’s probability is linked to any uncertainty, further diminishing the reliability of the explanations. Evaluating the quality of explanations for decision support is challenging, and although highlighted as essential in research, there are no benchmark criteria for comparative evaluations. This thesis introduces an innovative explanation method that generates reliable explanations, incorporating uncertainty information supporting users in determining when to trust the model’s predictions. The thesis also outlines strategies for evaluating explanation quality and facilitating comparative evaluations. Through empirical evaluations and user studies, the thesis provides practical insights to support decision-making utilising complex ML models.Användningen av Artificiell intelligens (AI) har förändrat områden som diagnosticering av sjukdomar och försvar. Genom att använda sofistikerade maskininlärningsmodeller predicerar AI framtida händelser baserat på historisk data. Modellernas komplexitet resulterar samtidigt i utmanande beslutsprocesser när orsakerna till prediktionerna är svårbegripliga. Tidigare forskning pekar på användares problem att avgöra prediktioners tillförlitlighet på grund av modellkomplexitet och belyser vikten av att tillhandahålla transparenta förklaringar för att underlätta högkvalitativa beslut. Många maskininlärningsmodeller erbjuder sannolikhetsuppskattningar för prediktionerna, vilket vanligtvis används i metoder som ger förklaringar för att vägleda användare om prediktionernas tillförlitlighet. Dessa sannolikheter återspeglar dock ofta inte de faktiska fördelningarna i datat, vilket kan leda till att användare felaktigt tolkar prediktioner som tillförlitliga. Därutöver förmedlar de flesta förklaringsmetoder inte om prediktionernas sannolikheter är kopplade till någon osäkerhet, vilket minskar tillförlitligheten hos förklaringarna. Att utvärdera kvaliteten på förklaringar för beslutsstöd är utmanande, och även om det har betonats som avgörande i forskning finns det inga benchmark-kriterier för jämförande utvärderingar. Denna avhandling introducerar en innovativ förklaringsmetod som genererar tillförlitliga förklaringar vilka inkluderar osäkerhetsinformation för att stödja användare att avgöra när man kan lita på modellens prediktioner. Avhandlingen ger också förslag på strategier för att utvärdera kvaliteten på förklaringar och underlätta jämförande utvärderingar. Genom empiriska utvärderingar och användarstudier ger avhandlingen praktiska insikter för att stödja beslutsfattande användande komplexa maskininlärningsmodeller

Trustworthy explanations : Improved decision support through well-calibrated uncertainty quantification

The use of Artificial Intelligence (AI) has transformed fields like disease diagnosis and defence. Utilising sophisticated Machine Learning (ML) models, AI predicts future events based on historical data, introducing complexity that challenges understanding and decision-making. Previous research emphasizes users’ difficulty discerning when to trust predictions due to model complexity, underscoring addressing model complexity and providing transparent explanations as pivotal for facilitating high-quality decisions. Many ML models offer probability estimates for predictions, commonly used in methods providing explanations to guide users on prediction confidence. However, these probabilities often do not accurately reflect the actual distribution in the data, leading to potential user misinterpretation of prediction trustworthiness. Additionally, most explanation methods fail to convey whether the model’s probability is linked to any uncertainty, further diminishing the reliability of the explanations. Evaluating the quality of explanations for decision support is challenging, and although highlighted as essential in research, there are no benchmark criteria for comparative evaluations. This thesis introduces an innovative explanation method that generates reliable explanations, incorporating uncertainty information supporting users in determining when to trust the model’s predictions. The thesis also outlines strategies for evaluating explanation quality and facilitating comparative evaluations. Through empirical evaluations and user studies, the thesis provides practical insights to support decision-making utilising complex ML models.Användningen av Artificiell intelligens (AI) har förändrat områden som diagnosticering av sjukdomar och försvar. Genom att använda sofistikerade maskininlärningsmodeller predicerar AI framtida händelser baserat på historisk data. Modellernas komplexitet resulterar samtidigt i utmanande beslutsprocesser när orsakerna till prediktionerna är svårbegripliga. Tidigare forskning pekar på användares problem att avgöra prediktioners tillförlitlighet på grund av modellkomplexitet och belyser vikten av att tillhandahålla transparenta förklaringar för att underlätta högkvalitativa beslut. Många maskininlärningsmodeller erbjuder sannolikhetsuppskattningar för prediktionerna, vilket vanligtvis används i metoder som ger förklaringar för att vägleda användare om prediktionernas tillförlitlighet. Dessa sannolikheter återspeglar dock ofta inte de faktiska fördelningarna i datat, vilket kan leda till att användare felaktigt tolkar prediktioner som tillförlitliga. Därutöver förmedlar de flesta förklaringsmetoder inte om prediktionernas sannolikheter är kopplade till någon osäkerhet, vilket minskar tillförlitligheten hos förklaringarna. Att utvärdera kvaliteten på förklaringar för beslutsstöd är utmanande, och även om det har betonats som avgörande i forskning finns det inga benchmark-kriterier för jämförande utvärderingar. Denna avhandling introducerar en innovativ förklaringsmetod som genererar tillförlitliga förklaringar vilka inkluderar osäkerhetsinformation för att stödja användare att avgöra när man kan lita på modellens prediktioner. Avhandlingen ger också förslag på strategier för att utvärdera kvaliteten på förklaringar och underlätta jämförande utvärderingar. Genom empiriska utvärderingar och användarstudier ger avhandlingen praktiska insikter för att stödja beslutsfattande användande komplexa maskininlärningsmodeller

Time to Open the Black Box : Explaining the Predictions of Text Classification

The purpose of this thesis has been to evaluate if a new instance based explanation method, called Automatic Instance Text Classification Explanator (AITCE), could provide researchers with insights about the predictions of automatic text classification and decision support about documents requiring human classification. Making it possible for researchers, that normally use manual classification, to cut time and money in their research, with the maintained quality. In the study, AITCE was implemented and applied to the predictions of a black box classifier. The evaluation was performed at two levels: at instance level, where a group of 3 senior researchers, that use human classification in their research, evaluated the results from AITCE from an expert view; and at model level, where a group of 24 non experts evaluated the characteristics of the classes. The evaluations indicate that AITCE produces insights about which words that most strongly affect the prediction. The research also suggests that the quality of an automatic text classification may increase through an interaction between the user and the classifier in situations with unsure predictions

Investigating the impact of calibration on the quality of explanations

Predictive models used in Decision Support Systems (DSS) are often requested to explain the reasoning to users. Explanations of instances consist of two parts; the predicted label with an associated certainty and a set of weights, one per feature, describing how each feature contributes to the prediction for the particular instance. In techniques like Local Interpretable Model-agnostic Explanations (LIME), the probability estimate from the underlying model is used as a measurement of certainty; consequently, the feature weights represent how each feature contributes to the probability estimate. It is, however, well-known that probability estimates from classifiers are often poorly calibrated, i.e., the probability estimates do not correspond to the actual probabilities of being correct. With this in mind, explanations from techniques like LIME risk becoming misleading since the feature weights will only describe how each feature contributes to the possibly inaccurate probability estimate. This paper investigates the impact of calibrating predictive models before applying LIME. The study includes 25 benchmark data sets, using Random forest and Extreme Gradient Boosting (xGBoost) as learners and Venn-Abers and Platt scaling as calibration methods. Results from the study show that explanations of better calibrated models are themselves better calibrated, with ECE and log loss for the explanations after calibration becoming more conformed to the model ECE and log loss. The conclusion is that calibration makes the models and the explanations better by accurately representing reality.

Utvärdering av de virtuella träningsprogrammen SeQualia och Vizendo

The automotive industry is constantly developing and manufacturing industry is facing modernisation and need for increased efficiency which implies that the automotive industry is facing changes for assembly training. To minimise quality errors and save time computerbased training virtual training can be used to practice on product knowledge, variants and sequences. Currently in traditional training in automotive industries, the operators practice on psychical products. The purpose of this bachelor thesis is to obtain knowledge of the effects of virtual training with cycle times longer than five minutes by evaluate the virtual training methods at Scania in Södertälje, Sweden. To achieve the purpose of the thesis the objective is to create a basis of the advantages and disadvantages. Scania is part of the Volkswagen Group and is a world leading supplier in the automotive industry which develops custom made and high-quality products with short lead times. Scania’s core products are heavy trucks, buses and engines for marine and industrial applications. Scania has 46 000 employees in 100 countries. Today Scania use standardised work and Job Instructions Training tools for learning the assembling processes which could be time consuming, ineffective and resource-intensive. Therefore, Scania’s vision with virtual training is to shorten the learning time on-line by implementing virtual training to practice off-line. The main objectives are to reduce the time for training simultaneously as the product quality increases. To obtain reliable information to reach the purpose a qualitative approach has been used in the study. To obtain primary data the methods have been semi-structured interviews, observation and questionnaires that have been performed and literature studies have been used to collect secondary data to obtain a broad knowledge of the subject. Through this the bachelor thesis resulted in recommendations that will contribute to Scania’s decision of future work with virtual training. The recommendations have been based on the previous accomplished studies, results and obtained comments which together were connected in the discussion and by the recommendations the authors believe that Scania can use virtual training with success.  

Samordningsförbundet FinsamGotland : en studie av hur samverkan implementeras genom samordningsförbund.

Samordningsförbundet FinsamGotland bildades år 2007 och har varit formelltverksamt sedan 2008. Inom ramen för förbundet samarbetar Arbetsförmedlin-gen, Försäkringskassan och Region Gotland. Som samordningsförbund är deten egen juridisk person och utgår från lagen om finansiell samordningav reha-biliteringsinsatsersom funnits sedan 1 januari 2004 (SFS 2003: 1210).Samordningsförbundet har som syfte att gentemot målgrupper som harbehov av insatser från två eller flera myndigheter samordna sina insatser.Utöver att olika effektivare insatser prövas för människor som hamnat utan-för arbetslivet skapas möjligheter att utveckla nya arbetssätt och nya formerför organisering av samverkan.Samordningsförbundet har således som främsta syfte att öka nyttan förbrukare av de olika välfärdstjänsterna genom att främja samverkan mellan deolika myndigheternas verksamhet i det geografiska område som utgör Sam-ordningsförbundet FinsamGotland. Det medför att man genom samverkanetablerar specifika verksamheter och forum för att mötas såväl i den operativaverksamheten som mellan ledningsstrukturer

Samordningsförbundet FinsamGotland : en studie av hur samverkan implementeras genom samordningsförbund.

Samordningsförbundet FinsamGotland bildades år 2007 och har varit formelltverksamt sedan 2008. Inom ramen för förbundet samarbetar Arbetsförmedlin-gen, Försäkringskassan och Region Gotland. Som samordningsförbund är deten egen juridisk person och utgår från lagen om finansiell samordningav reha-biliteringsinsatsersom funnits sedan 1 januari 2004 (SFS 2003: 1210).Samordningsförbundet har som syfte att gentemot målgrupper som harbehov av insatser från två eller flera myndigheter samordna sina insatser.Utöver att olika effektivare insatser prövas för människor som hamnat utan-för arbetslivet skapas möjligheter att utveckla nya arbetssätt och nya formerför organisering av samverkan.Samordningsförbundet har således som främsta syfte att öka nyttan förbrukare av de olika välfärdstjänsterna genom att främja samverkan mellan deolika myndigheternas verksamhet i det geografiska område som utgör Sam-ordningsförbundet FinsamGotland. Det medför att man genom samverkanetablerar specifika verksamheter och forum för att mötas såväl i den operativaverksamheten som mellan ledningsstrukturer

Risk stratification score screening for infective endocarditis in patients with Gram-positive bacteraemia

Background: A feared cause of bacteraemia with Gram-positives is infective endocarditis. Risk stratification scores can aid clinicians in determining the risk of endocarditis. Six proposed scores for the use in bacteraemia; Staphylococcus aureus (PREDICT, VIRSTA, POSITIVE), non-β-haemolytic streptococci (HANDOC) and Enterococcus faecalis (NOVA, DENOVA) were validated for predictive ability and the utilization of echocardiography was investigated. Methods: Hospitalized adult patients with Gram-positive bacteraemia during 2017–2019 were evaluated retrospectively through medical records and the Swedish Death Registry. Baseline and score-specific data, definite endocarditis and echocardiographies performed were recorded. Sensitivity, specificity, negative and positive predictive values and echocardiography utilization were determined. Results: 480 patients with bacteraemia were included and definite endocarditis was diagnosed in 20 (7.5%), 10 (6.6%), and 2 (3.2%) patients with S. aureus, non-β-haemolytic streptococci and E. faecalis, respectively. The sensitivities of the scores were 80–100% and specificities 8–77%. Negative predictive values of the six scores were 98–100%. VIRSTA, HANDOC, NOVA and DENOVA identified all, the PREDICT5 score missed 1/20 and the POSITIVE score missed 4/20 cases of endocarditis. Transoesophageal echocardiography was performed in 141 patients (29%). Thus, the risk stratification scores suggested an increase of 3–63 (7–77%) investigations with echocardiography. Conclusions: All scores had negative-predictive values over 98%, therefore it can be concluded that PREDICT5, VIRSTA, POSITIVE, HANDOC and DENOVA are reasonable screening tools for endocarditis early on in Gram-positive bacteraemia. The use of risk stratification scores will lead to more echocardiographies