Can metafeatures help improve explanations of prediction models when using behavioral and textual data?

Ramon, Yanou; Martens, David; Evgeniou, Theodoros; Praet, Stiene

doi:10.1007/s10994-021-05981-0

Cited by 6 publications

(13 citation statements)

References 54 publications

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“…Consequently, it is often difficult-if not impossible-to understand how classifications were made when using nonlinear models without relying on interpretation techniques like the ones we use in this study. Even for linear models or decision trees, it can be challenging to gain meaningful insights into how classifications are made, because of the high dimensional and sparse nature of behavioral data [14][15][16][17]. For example, if we want to predict people's personality based on the Facebook pages they 'like', a user is represented by a binary feature for every page, which results in an enormous feature space.…”

Section: Ai As a Black Boxmentioning

confidence: 99%

“…We use rule extraction as a global method to gain insight into the classification models. Rule extraction has been proposed in the literature to generate explanations by distilling a comprehensible set of rules (hereafter 'explanation rules') from a complex classification model C M [15,[37][38][39]. Rule extraction is based on surrogate modeling of which the goal is to use an interpretable model to approximate the predictions of a more complex model Ŷ.…”

Section: Rules As Global Explanationsmentioning

confidence: 99%

“…(Rule extraction can be challenging for high-dimensional, sparse data, as the black box model needs to be replaced by many rules to explain a substantial fraction of the classifications, which leaves the user again with an incomprehensible explanation. To address this, Ramon et al [15] proposed a technique based on metafeatures (i.e., clusters of the original features) to extract a concise set of rules that more accurately approximates the model's behavior. In this study, however, we apply rule extraction on the original data, because the dimensionality and sparsity of the data used in the case study are still manageable).…”

Section: Rules As Global Explanationsmentioning

confidence: 99%

“…If the most important features in a model are extremely sparse, an explanation with few rules and/or few conditions per rule will fail to make accurate predictions for most people, as reflected by a low Fidelity or Fscore f . When this is the case, novel rule extraction approaches can be used to replace features with metafeatures (groups of individual behavioral features-e.g., 'fast food' purchases that are made up of individual merchants) to increase the Fidelity of the extracted rules [15].…”

Section: Importance Of Global Explanations and Implicationsmentioning

confidence: 99%

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Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Ramon

Farrokhnia²,

Matz

et al. 2021

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Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.

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Section: Ai As a Black Boxmentioning

confidence: 99%

Section: Rules As Global Explanationsmentioning

confidence: 99%

Section: Rules As Global Explanationsmentioning

confidence: 99%

Section: Importance Of Global Explanations and Implicationsmentioning

confidence: 99%

See 2 more Smart Citations

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Ramon

Farrokhnia²,

Matz

et al. 2021

Information

Self Cite

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show abstract

“…We find that counterfactual explanations, despite being often favored by XAI researchers [35,36,13,40], are in general less popular. Consistent with our predictions, we find that aversion to counterfactual explanations is reduced when these explanations are provided following a negative decision (e.g., loan application was denied).…”

Section: Introductionmentioning

confidence: 62%

Understanding Consumer Preferences for Explanations Generated by XAI Algorithms

Ramon,

Vermeire,

Toubia

et al. 2021

Preprint

Self Cite

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Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers' preferences for various types and formats of explanations. We discuss theoretically and study empirically people's preferences for explanations of algorithmic decisions. We focus on three main attributes that describe automatically-generated explanations from existing XAI algorithms (format, complexity, and specificity), and capture differences across contexts (online targeted advertising vs. loan applications) as well as heterogeneity in users' cognitive styles. Despite their popularity among academics, we find that counterfactual explanations are not popular among users, unless they follow a negative outcome (e.g., loan application was denied). We also find that users are willing to tolerate some complexity in explanations. Finally, our results suggest that preferences for specific (vs. more abstract) explanations are related to the level at which the decision is construed by the user, and to the deliberateness of the user's cognitive style. * Yanou Ramon and Tom Vermeire contributed equally.

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Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with $${{\ell}}_{0}$$-Constraints

Yang,

Min

2024

Lecture Notes in Computer Science

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Can metafeatures help improve explanations of prediction models when using behavioral and textual data?

Cited by 6 publications

References 54 publications

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

Understanding Consumer Preferences for Explanations Generated by XAI Algorithms

Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with $${{\ell}}_{0}$$-Constraints

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