FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

Sokol, Kacper; Santos-Rodriguez, Raul; Flach, Peter

doi:10.48550/arxiv.1909.05167

Cited by 8 publications

(15 citation statements)

References 12 publications

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“…To show the importance of selecting a good surrogate model and the difference in explanations that it can produce we explain a carefully selected data point from the two moons data set. The two moons data set -shown in Figure 3 and generated with scikit-learn 11 -is a synthetic 2-dimensional, binary classification data set with a complex decision boundary. It is suitable for this type of experiments as depending on which data point is chosen the resulting explanations can be quite diverse.…”

Section: A3: Decision Tree-based Surrogate Explainer For Tabular Datamentioning

confidence: 99%

bLIMEy: Surrogate Prediction Explanations Beyond LIME

Sokol,

Hepburn,

Santos-Rodriguez

et al. 2019

Preprint

Self Cite

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Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type of data (images, text and tabular), are model-agnostic and are post-hoc (i.e., can be retrofitted). The Local Interpretable Model-agnostic Explanations (LIME) algorithm is often mistakenly unified with a more general framework of surrogate explainers, which may lead to a belief that it is the solution to surrogate explainability. In this paper we empower the community to "build LIME yourself" (bLIMEy) by proposing a principled algorithmic framework for building custom local surrogate explainers of black-box model predictions, including LIME itself. To this end, we demonstrate how to decompose the surrogate explainers family into algorithmically independent and interoperable modules and discuss the influence of these component choices on the functional capabilities of the resulting explainer, using the example of LIME.

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Section: A3: Decision Tree-based Surrogate Explainer For Tabular Datamentioning

confidence: 99%

bLIMEy: Surrogate Prediction Explanations Beyond LIME

Sokol,

Hepburn,

Santos-Rodriguez

et al. 2019

Preprint

Self Cite

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“…Sokol, Santos-Rodriguez, and Flach (2019) already showed how counterfactual explanations can be used to check individual fairness. They consider an instance to be treated unfairly if that instance received the undesirable label and there exists a counterfactual explanation for that instance that includes at least one protected attribute change (Sokol et al (2019)). We follow this approach when we use counterfactual explanations to identify explicit bias for an individual.…”

Section: Counterfactual Fairness (Bis)mentioning

confidence: 99%

“…However, our metric will be different as it does not need to assume a causal graph (Kusner et al ( 2017)), and does not use the distance to the counterfactual like Sharma et al (2019), but will look at the actual explanations of decisions instead. Furthermore, we will use counterfactual explanations not only to show explicit bias, as done by Sokol et al (2019), but also to get insights into the implicit bias, which is arguably the more challenging problem.…”

Section: (X) ∈ {+ −}mentioning

confidence: 99%

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PreCoF: Counterfactual Explanations for Fairness

Goethals

Martens

Calders

2022

Preprint

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This paper studies how counterfactual explanations can be used to assess the fairness of a model. Using machine learning for high-stakes decisions is a threat to fairness as these models can amplify bias present in the dataset, and there is no consensus on a universal metric to detect this. The appropriate metric and method to tackle the bias in a dataset will be case-dependent, and it requires insight into the nature of the bias first. We aim to provide this insight by integrating explainable AI (XAI) research with the fairness domain. More specifically, apart from being able to use (Predictive) Counterfactual Explanations to detect explicit bias when the model is directly using the sensitive attribute, we show that it can also be used to detect implicit bias when the model does not use the sensitive attribute directly but does use other correlated attributes leading to a substantial disadvantage for a protected group. We call this metric $PreCoF$, or Predictive Counterfactual Fairness. Our experimental results show that our metric succeeds in detecting occurrences of implicit bias in the model by assessing which attributes are more present in the explanations of the protected group compared to the unprotected group. These results could help policymakers decide on whether this discrimination is justified or not.

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“…These works propose methods to generate appropriate test data inputs for the model and prediction on those inputs characterizes fairness. Some research has been conducted to build automated tools [2,64,67] and libraries [8] for fairness. In addition, empirical studies have been conducted to compare, contrast between fairness aspects, interventions, tradeoffs, developers concerns, and human aspects of fairness [10,26,33,35,77].…”

Section: Related Workmentioning

confidence: 99%

Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline

Biswas

Rajan

2021

Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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In recent years, many incidents have been reported where machine learning models exhibited discrimination among people based on race, sex, age, etc. Research has been conducted to measure and mitigate unfairness in machine learning models. For a machine learning task, it is a common practice to build a pipeline that includes an ordered set of data preprocessing stages followed by a classifier. However, most of the research on fairness has considered a single classifier based prediction task. What are the fairness impacts of the preprocessing stages in machine learning pipeline? Furthermore, studies showed that often the root cause of unfairness is ingrained in the data itself, rather than the model. But no research has been conducted to measure the unfairness caused by a specific transformation made in the data preprocessing stage. In this paper, we introduced the causal method of fairness to reason about the fairness impact of data preprocessing stages in ML pipeline. We leveraged existing metrics to define the fairness measures of the stages. Then we conducted a detailed fairness evaluation of the preprocessing stages in 37 pipelines collected from three different sources. Our results show that certain data transformers are causing the model to exhibit unfairness. We identified a number of fairness patterns in several categories of data transformers. Finally, we showed how the local fairness of a preprocessing stage composes in the global fairness of the pipeline. We used the fairness composition to choose appropriate downstream transformer that mitigates unfairness in the machine learning pipeline. CCS CONCEPTS• Software and its engineering → Software creation and management; • Computing methodologies → Machine learning.

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FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

Cited by 8 publications

References 12 publications

bLIMEy: Surrogate Prediction Explanations Beyond LIME

bLIMEy: Surrogate Prediction Explanations Beyond LIME

PreCoF: Counterfactual Explanations for Fairness

Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline

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