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Jorge Pérez

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7 papers

1 author row

JMLR Journal 2025 Journal Article

WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings

Pablo Badilla
Felipe Bravo-Marquez
María José Zambrano
Jorge Pérez

Word embeddings, which are a mapping of words into continuous vectors, are widely used in modern Natural Language Processing (NLP) systems. However, they are prone to inherit stereotypical social biases from the corpus on which they are built. The research community has focused on two main tasks to address this problem: 1) how to measure these biases, and 2) how to mitigate them. Word Embedding Fairness Evaluation (WEFE) is an open source library that implements many fairness metrics and mitigation methods in a unified framework. It also provides a standard interface for designing new ones. The software follows the object-oriented paradigm with a strong focus on extensibility. Each of its methods is appropriately documented, verified and tested. WEFE is not limited to just a library: it also contains several replications of previous studies as well as tutorials that serve as educational material for newcomers to the field. It is licensed under BSD-3 and can be easily installed through pip and conda package managers. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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TCS Journal 2022 Journal Article

On the expressiveness of Lara: A proposal for unifying linear and relational algebra

Pablo Barceló
Nelson Higuera
Jorge Pérez
Bernardo Subercaseaux

We study the expressive power of the Lara language – a recently proposed unified model for expressing relational and linear algebra operations – both in terms of traditional database query languages and some analytic tasks often performed in machine learning pipelines. Since Lara is parameterized by a set of user-defined functions which allow to transform values in tables, known as extension functions, the exact expressive power of the language depends on how these functions are defined. We start by showing Lara to be expressive complete with respect to a syntactic fragment of relational algebra with aggregation (under the mild assumption that extension functions in Lara can cope with traditional relational algebra operations such as selection and renaming). We then look further into the expressiveness of Lara based on different classes of extension functions, and distinguish two main cases depending on the level of genericity that queries are enforced to satisfy. Under strong genericity assumptions the language cannot express matrix convolution, a very important operation in current machine learning pipelines. This language is also local, and thus cannot express operations such as matrix inverse that exhibit a recursive behavior. For expressing convolution, one can relax the genericity requirement by adding an underlying linear order on the domain. This, however, destroys locality and turns the expressive power of the language much more difficult to understand. In particular, although under complexity assumptions some versions of the resulting language can still not express matrix inverse, a proof of this fact without such assumptions seems challenging to obtain.

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JMLR Journal 2021 Journal Article

Attention is Turing-Complete

Jorge Pérez
Pablo Barceló
Javier Marinkovic

Alternatives to recurrent neural networks, in particular, architectures based on self-attention, are gaining momentum for processing input sequences. In spite of their relevance, the computational properties of such networks have not yet been fully explored.We study the computational power of the Transformer, one of the most paradigmatic architectures exemplifying self-attention. We show that the Transformer with hard-attention is Turing complete exclusively based on their capacity to compute and access internal dense representations of the data.Our study also reveals some minimal sets of elements needed to obtain this completeness result. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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NeurIPS Conference 2021 Conference Paper

Foundations of Symbolic Languages for Model Interpretability

Marcelo Arenas
Daniel Báez
Pablo Barceló
Jorge Pérez
Bernardo Subercaseaux

Several queries and scores have recently been proposed to explain individual predictions over ML models. Examples include queries based on “anchors”, which are parts of an instance that are sufficient to justify its classification, and “feature-perturbation” scores such as SHAP. Given the need for flexible, reliable, and easy-to-apply interpretability methods for ML models, we foresee the need for developing declarative languages to naturally specify different explainability queries. We do this in a principled way by rooting such a language in a logic called FOIL, which allows for expressing many simple but important explainability queries, and might serve as a core for more expressive interpretability languages. We study the computational complexity of FOIL queries over two classes of ML models often deemed to be easily interpretable: decision trees and more general decision diagrams. Since the number of possible inputs for an ML model is exponential in its dimension, tractability of the FOIL evaluation problem is delicate but can be achieved by either restricting the structure of the models, or the fragment of FOIL being evaluated. We also present a prototype implementation of FOIL wrapped in a high-level declarative language and perform experiments showing that such a language can be used in practice.

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NeurIPS Conference 2020 Conference Paper

Model Interpretability through the lens of Computational Complexity

Pablo Barceló
Mikaël Monet
Jorge Pérez
Bernardo Subercaseaux

In spite of several claims stating that some models are more interpretable than others --e. g. , "linear models are more interpretable than deep neural networks"-- we still lack a principled notion of interpretability that allows us to formally compare among different classes of models. We make a step towards such a theory by studying whether folklore interpretability claims have a correlate in terms of computational complexity theory. We focus on post-hoc explainability queries that, intuitively, attempt to answer why individual inputs are classified in a certain way by a given model. In a nutshell, we say that a class C1 of models is more interpretable than another class C2, if the computational complexity of answering post-hoc queries for models in C2 is higher than for C1. We prove that this notion provides a good theoretical counterpart to current beliefs on the interpretability of models; in particular, we show that under our definition and assuming standard complexity-theoretical assumptions (such as P! =NP), both linear and tree-based models are strictly more interpretable than neural networks. Our complexity analysis, however, does not provide a clear-cut difference between linear and tree-based models, as we obtain different results depending on the particular {post-hoc explanations} considered. Finally, by applying a finer complexity analysis based on parameterized complexity, we are able to prove a theoretical result suggesting that shallow neural networks are more interpretable than deeper ones.

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IJCAI Conference 2020 Conference Paper

WEFE: The Word Embeddings Fairness Evaluation Framework

Pablo Badilla
Felipe Bravo-Marquez
Jorge Pérez

Word embeddings are known to exhibit stereotypical biases towards gender, race, religion, among other criteria. Severa fairness metrics have been proposed in order to automatically quantify these biases. Although all metrics have a similar objective, the relationship between them is by no means clear. Two issues that prevent a clean comparison is that they operate with different inputs, and that their outputs are incompatible with each other. In this paper we propose WEFE, the word embeddings fairness evaluation framework, to encapsulate, evaluate and compare fairness metrics. Our framework needs a list of pre-trained embeddings and a set of fairness criteria, and it is based on checking correlations between fairness rankings induced by these criteria. We conduct a case study showing that rankings produced by existing fairness methods tend to correlate when measuring gender bias. This correlation is considerably less for other biases like race or religion. We also compare the fairness rankings with an embedding benchmark showing that there is no clear correlation between fairness and good performance in downstream tasks.

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IJCAI Conference 2018 Conference Paper

A Model of Distributed Query Computation in Client-Server Scenarios on the Semantic Web

Olaf Hartig
Ian Letter
Jorge Pérez

This paper provides an overview of a model for capturing properties of client-server-based query computation setups. This model can be used to formally analyze different combinations of client and server capabilities, and compare them in terms of various fine-grain complexity measures. While the motivations and the focus of the presented work are related to querying the Semantic Web, the main concepts of the model are general enough to be applied in other contexts as well.

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