Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning
Abstract
:1. Introduction
- In Section 2, we rigorously define which types of algorithmic biases are commonly observed in machine learning applications based on images, and what their causes are.
- In Section 3, Section 4 and Section 5, we then give a comprehensive overview of various methods to measure, to detect and to mitigate algorithmic biases. Note that Section 4 distinguishes the cases where the potential algorithmic biases are either due to suspected or to unsuspected sensitive variables, the second case being of particular interest in our paper.
2. Algorithmic Biases in Machine Learning
2.1. Definitions
2.2. Potential Causes of Bias in Computer Vision
2.2.1. Improperly Sampled Training Data
2.2.2. Spurious Correlations and External Factors
2.2.3. Unreliable Labels
2.3. From Determined Bias to Unknown Bias in Image Analysis
- Full information: images, targets, metadata and sensitive variables, i.e., are available. The bias may then come from the meta-observations , the image itself, the labels or all three.
- Partial information: the sensitive variable is not observed, so we only observe . The sensitive variable may be included in the meta variables , or may be estimated using the meta-variables .
- Scarce information: only the images are observed along with their target, i.e., we only observe . The sensitive variable A is, therefore, hidden. The bias it induces is contained inside the images and has to be inferred from the available data X and used to estimate A.
2.4. Current Regulation of AI
3. Measuring Algorithmic Biases
- -
- Statistical Parity One of the most standard measures of algorithmic bias is the so-called Statistical Parity. Balanced decisions in the sense of Statistical Parity are then reached when the model outputs are not influenced by the sensitive variable value—i.e., . For a binary decision, it is often quantified using the Disparate Impact (DI) metric. Introduced in the US legislation in 1971 (https://www.govinfo.gov/content/pkg/CFR-2017-title29-vol4/xml/CFR-2017-title29-vol4-part1607.xml, accessed on 30 October 2023) it measures how the outcome of the algorithm depends on A.It is computed for a binary decision as
- -
- Equal performance metrics familyTaking into account the input observations X or the prediction errors can be more proper in various applications than imposing the same decisions for all. To address this, the notions of equal performance, status-quo preserving, or error parity measure whether a model is equally accurate for individuals in the sensitive and non-sensitive groups. As discussed in [27], it is often measured by using three common metrics: equal sensitivity or equal opportunity [64], equal sensitivity and specificity or equalised odds, and equal positive predictive value or predictive parity [71]. In the case of a binary decision, common metrics usually compute the difference between True Positive Rate and/or False Positive Rate for majority and minority groups. Therefore, algorithmically unbiased decisions in the sense of equal performance are reached when this difference is zero. Specifically, an equal opportunity metric is given byNote finally that, predictive parity refers to Equal accuracy (or error) in the two groups also corresponds to refered by.
- -
- Calibration Previous notions can be written using the notion of calibration in fair machine learning. When the algorithm’s decision is based on a score , as in [72], a calibration metric is defined asCalibration measures the proportion of individuals that experience a situation compared to the proportion of individuals forecast to experience this outcome. It is a measure of efficiency of the algorithm and of the validity of its outcome. Yet, studying the difference between the groups enables one to point out a difference in behaviours that would let the user trust the outcome of an algorithm less for one group than another. This definition extends in this sense previous notions to the multivalued settings as pointed in [73]. Calibration is similar to the definition of fairness using quantiles, as shown in in [74]. Note that previous definitions can also easily be extended to the case where the variables are not binary but discrete.
- -
- Advanced metrics First, for algorithms with continuous values, previous metrics can be understood as quantification of the variability of a mean characteristic of the algorithm, with respect to the sensitive value. So natural metrics as in [75,76] are given byNote that, as pointed out in [75], these two metrics are not normalised Sobol indices. Hence, sensitivity analysis metrics can also be used to measure bias of algorithmic decisions. As a natural extension, sensitivity analysis tools provide new ways to describe the dependency relationships between a well-chosen function of the algorithm, focusing on particular features of the algorithm. They are well-adapted to studying bias in image analysis.Previous measures focus on computing a measure of dependency. Yet, many authors used different ways to compute covariance-like operators, directly as in [69], or based on information theory [77], or using more advanced notions of covariance based on embedding, possibly with kernels. We refer, for instance, to [66] for a review. Each method chooses a measure of dependency and computes an algorithmic bias measure of either the outcome of the algorithmic model or its residuals (or any appropriate transformation) with the sensitive parameter.Other measures of algorithmic biases do not focus on the mean behaviour of the algorithm, but other properties that may be the quantiles or the whole distribution. Hence, algorithmic bias measures can compare the distance between the conditional distribution for two different values of the sensitive attribute of either the decisionsDifferent distances between probability distributions can be used. We refer for instance to [78] and references therein, where Monge–Kantorovich distance (or Wasserstein distance) is used. Embedding of distributions using kernels can also be used, as pointed out in [79], together with well adapted notions of dependency in this setting.
4. Detecting Algorithmic Biases
4.1. With Suspected Sensitive Variables
4.2. Without Suspected Sensitive Variables
5. Algorithmic Bias Mitigation
- Firstly, it is critical to obtain robust algorithms that generalise to the test domain with a certified level of performance, and that do not depend on specific working conditions or types of sensors to work as intended. The property which is expected is the robustness of the algorithm.
- Secondly, the second goal is to learn representations independent of non-informative variables that can correlate with actual predictive information and play the role of confounding variables. The link between algorithmic biases and these representations constitutes an open challenge. In many cases, representations are affected by spurious correlations between subjects and backgrounds (Waterbirds, Benchmarking Attribution Methods), or gender and occupation (Athletes and health professionals, political person) that influence too much the selection of the features, and hence, the algorithmic decision. One way to study it is through disentangled representations [55], i.e., by isolating each factor of variation into a specific dimension of the latent space, it is possible to ensure the independence with respect to sensitive variables.
6. A Generic Pipeline to Detect and to Treat Unsuspected Sensitive Variables
Algorithm 1 Pipeline to detect unsuspected sensitive variables and to mitigate their biases |
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7. A Use-Case for EuroSAT
7.1. The Blue Veil Effect in the EuroSAT Dataset
7.2. Detecting Sensitive Variables without Additional Metadata
7.3. Measuring the Effect of the Sensitive Variable
7.4. Bias Mitigation
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Risser, L.; Picard, A.M.; Hervier, L.; Loubes, J.-M. Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning. Algorithms 2023, 16, 510. https://doi.org/10.3390/a16110510
Risser L, Picard AM, Hervier L, Loubes J-M. Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning. Algorithms. 2023; 16(11):510. https://doi.org/10.3390/a16110510
Chicago/Turabian StyleRisser, Laurent, Agustin Martin Picard, Lucas Hervier, and Jean-Michel Loubes. 2023. "Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning" Algorithms 16, no. 11: 510. https://doi.org/10.3390/a16110510