1,720,974 research outputs found
Model monitoring in the absence of labeled data via feature attributions distributions
No full textModel monitoring involves analyzing AI algorithms once they have been deployed and detecting changes in their behaviour.This thesis explores machine learning model monitoring ML before the predictions impact real-world decisions or users. This step is characterized by one particular condition: the absence of labelled data at test time, which makes it challenging, even often impossible, to calculate performance metrics.The thesis is structured around two main themes: \emph{(i) AI alignment}, measuring if AI models behave in a manner consistent with human values and \emph{(ii) performance monitoring}, measuring if the models achieve specific accuracy goals or desires. The thesis uses a common methodology that unifies all its sections. It explores feature attribution distributions for both monitoring dimensions. Using these feature attribution explanations, we can exploit their theoretical properties to derive and establish certain guarantees and insights into model monitoring.For AI Alignment, we explore whether the distributions of feature attributions are distinct for different social groups and propose a new formalization of equal treatment. This novel metric assesses how well AI decisions adhere to ethical standards and political-philosophical values. Our notion of Equal Treatment tests for statistical independence of the explanation distributions over populations with different protected characteristics. We show the theoretical properties of our formalization of equal treatment and devise an equal treatment inspector based on the AUC of a classifier two-sample test. For performance monitoring, we define \emph{explanation shift} as the statistical comparison between how predictions from training data are explained and how predictions on new data are explained. We propose explanation shift as a key indicator to investigate the interaction between distribution shifts and learned models. We introduce an Explanation Shift Detector that operates on the explanation distributions, providing more sensitive and explainable changes in interactions between distribution shifts and learned models. We compare explanation shifts with other methods that are based on distribution shifts, showing that monitoring for explanation shifts results in more sensitive indicators for varying model behavior. We provide theoretical and experimental evidence and demonstrate the effectiveness of our approach on synthetic and real data.Finally, to explain model degradation we use a second model, that predicts the uncertainty estimates of the firstAdditionally, we release two open-source Python packages, \texttt{skshift} and \texttt{explanationspace}, which implement our methods and provide usage tutorials for further reproducibility.<br/
Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap
Full text linkMonitoring machine learning models once they are deployed is challenging. It
is even more challenging to decide when to retrain models in real-case
scenarios when labeled data is beyond reach, and monitoring performance metrics
becomes unfeasible. In this work, we use non-parametric bootstrapped
uncertainty estimates and SHAP values to provide explainable uncertainty
estimation as a technique that aims to monitor the deterioration of machine
learning models in deployment environments, as well as determine the source of
model deterioration when target labels are not available. Classical methods are
purely aimed at detecting distribution shift, which can lead to false positives
in the sense that the model has not deteriorated despite a shift in the data
distribution. To estimate model uncertainty we construct prediction intervals
using a novel bootstrap method, which improves upon the work of Kumar &
Srivastava (2012). We show that both our model deterioration detection system
as well as our uncertainty estimation method achieve better performance than
the current state-of-the-art. Finally, we use explainable AI techniques to gain
an understanding of the drivers of model deterioration. We release an open
source Python package, doubt, which implements our proposed methods, as well as
the code used to reproduce our experiments.Comment: 7+6 pages. Accepted at AAAI'23 Safe and Robust AI trac
Explanation shift: how did the distribution shift impact the model?
No full textThe performance of machine learning models on new data is critical for their success in real-world applications. Current methods to detect shifts in the input or output data distributions have limitations in identifying model behaviour changes when no labelled data is available. In this paper, we define \emph{explanation shift} as the statistical comparison between how predictions from training data are explained and how predictions on new data are explained. We propose explanation shift as a key indicator to investigate the interaction between distribution shifts and learned models. We introduce an Explanation Shift Detector that operates on the explanation distributions, providing more sensitive and explainable changes in interactions between distribution shifts and learned models. We compare explanation shifts with other methods that are based on distribution shifts, showing that monitoring for explanation shifts results in more sensitive indicators for varying model behavior. We provide theoretical and experimental evidence and demonstrate the effectiveness of our approach on synthetic and real data. Additionally, we release an open-source Python package, \texttt{skshift}, which implements our method and provides usage tutorials for further reproducibility
Fairness implications of encoding protected categorical attributes
No full textPast research has demonstrated that the explicit use of protected attributes in machine learning can improve both performance and fairness. Many machine learning algorithms, however, cannot directly process categorical attributes, such as country of birth or ethnicity. Because protected attributes frequently are categorical, they must be encoded as features that can be input to a chosen machine learning algorithm, e.g. support vector machines, gradient boosting decision trees or linear models. Thereby, encoding methods influence how and what the machine learning algorithm will learn, affecting model performance and fairness. This work compares the accuracy and fairness implications of the two most well-known encoding methods:one-hot encoding and target encoding. We distinguish between two types of induced bias that may arise from these encoding methods and may lead to unfair models. The first type, irreducible bias, is due to direct group category discrimination and the second type, reducible bias, is due to the large variance in statistically underrepresented groups. We investigate the interaction between categorical encodings and target encoding regularization methods that reduce unfairness. Furthermore, we consider the problem of intersectional unfairness that may arise when machine learning best practices improve performance measures by encoding several categorical attributes into a high-cardinality feature
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
No full textAs input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In the past, predictive performance was considered the key indicator to monitor. However, explanation aspects have come to attention within the last years. In this work, we investigate how model predictive performance and model explanation characteristics are affected under distribution shifts and how these key indicators are related to each other for tabular data. We find that the modeling of explanation shifts can be a better indicator for the detection of predictive performance changes than state-of-the-art techniques based on representations of distribution shifts. We provide a mathematical analysis of different types of distribution shifts as well as synthetic experimental examples
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
No full textRegression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem of how to incorporate categorical features to regression problems. Usually, categorical feature encoders are general enough to cover both classification and regression problems. This lack of specificity results in underperforming regression models. In this paper,we provide an in-depth analysis of how to tackle high cardinality categor-ical features with the quantile. Our proposal outperforms state-of-the-encoders, including the traditional statistical mean target encoder, when considering the Mean Absolute Error, especially in the presence of long-tailed or skewed distributions. Besides, to deal with possible overfitting when there are categories with small support, our encoder benefits from additive smoothing. Finally, we describe how to expand the encoded values by creating a set of features with different quantiles. This expanded encoder provides a more informative output about the categorical feature in question, further boosting the performance of the regression model
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
