A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
- URL: http://arxiv.org/abs/2204.10233v2
- Date: Tue, 13 Dec 2022 18:00:46 GMT
- Title: A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
- Authors: Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao-Fei Cheng,
Haiyi Zhu, Steven Wu, Hoda Heidari
- Abstract summary: We present the conceptual idea and a first implementation of a bias-injection sandbox tool to investigate fairness consequences of various biases.
Unlike existing toolkits, ours provides a controlled environment to counterfactually inject biases in the ML pipeline.
In particular, we can test whether a given remedy can alleviate the injected bias by comparing the predictions resulting after the intervention with true labels in the unbiased regime-that is, before any bias injection.
- Score: 19.86635585740634
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motivated by the growing importance of reducing unfairness in ML predictions,
Fair-ML researchers have presented an extensive suite of algorithmic
'fairness-enhancing' remedies. Most existing algorithms, however, are agnostic
to the sources of the observed unfairness. As a result, the literature
currently lacks guiding frameworks to specify conditions under which each
algorithmic intervention can potentially alleviate the underpinning cause of
unfairness. To close this gap, we scrutinize the underlying biases (e.g., in
the training data or design choices) that cause observational unfairness. We
present the conceptual idea and a first implementation of a bias-injection
sandbox tool to investigate fairness consequences of various biases and assess
the effectiveness of algorithmic remedies in the presence of specific types of
bias. We call this process the bias(stress)-testing of algorithmic
interventions. Unlike existing toolkits, ours provides a controlled environment
to counterfactually inject biases in the ML pipeline. This stylized setup
offers the distinct capability of testing fairness interventions beyond
observational data and against an unbiased benchmark. In particular, we can
test whether a given remedy can alleviate the injected bias by comparing the
predictions resulting after the intervention in the biased setting with true
labels in the unbiased regime-that is, before any bias injection. We illustrate
the utility of our toolkit via a proof-of-concept case study on synthetic data.
Our empirical analysis showcases the type of insights that can be obtained
through our simulations.
Related papers
- How to be fair? A study of label and selection bias [3.018638214344819]
It is widely accepted that biased data leads to biased and potentially unfair models.
Several measures for bias in data and model predictions have been proposed, as well as bias mitigation techniques.
Despite the myriad of mitigation techniques developed in the past decade, it is still poorly understood under what circumstances which methods work.
arXiv Detail & Related papers (2024-03-21T10:43:55Z) - Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy.
We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples.
Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
arXiv Detail & Related papers (2023-02-22T14:50:24Z) - Fairness and Explainability: Bridging the Gap Towards Fair Model
Explanations [12.248793742165278]
We bridge the gap between fairness and explainability by presenting a novel perspective of procedure-oriented fairness based on explanations.
We propose a Comprehensive Fairness Algorithm (CFA), which simultaneously fulfills multiple objectives - improving traditional fairness, satisfying explanation fairness, and maintaining the utility performance.
arXiv Detail & Related papers (2022-12-07T18:35:54Z) - Systematic Evaluation of Predictive Fairness [60.0947291284978]
Mitigating bias in training on biased datasets is an important open problem.
We examine the performance of various debiasing methods across multiple tasks.
We find that data conditions have a strong influence on relative model performance.
arXiv Detail & Related papers (2022-10-17T05:40:13Z) - D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling
Algorithmic Bias [57.87117733071416]
We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases.
A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network.
For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
arXiv Detail & Related papers (2022-08-10T03:41:48Z) - Understanding Unfairness in Fraud Detection through Model and Data Bias
Interactions [4.159343412286401]
We argue that algorithmic unfairness stems from interactions between models and biases in the data.
We study a set of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML algorithms exhibit under different data bias settings.
arXiv Detail & Related papers (2022-07-13T15:18:30Z) - Information-Theoretic Bias Reduction via Causal View of Spurious
Correlation [71.9123886505321]
We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation.
We present a novel debiasing framework against the algorithmic bias, which incorporates a bias regularization loss.
The proposed bias measurement and debiasing approaches are validated in diverse realistic scenarios.
arXiv Detail & Related papers (2022-01-10T01:19:31Z) - A Statistical Test for Probabilistic Fairness [11.95891442664266]
We propose a statistical hypothesis test for detecting unfair classifiers.
We show both theoretically as well as empirically that the proposed test is correct.
In addition, the proposed framework offers interpretability by identifying the most favorable perturbation of the data.
arXiv Detail & Related papers (2020-12-09T00:20:02Z) - Towards causal benchmarking of bias in face analysis algorithms [54.19499274513654]
We develop an experimental method for measuring algorithmic bias of face analysis algorithms.
Our proposed method is based on generating synthetic transects'' of matched sample images.
We validate our method by comparing it to a study that employs the traditional observational method for analyzing bias in gender classification algorithms.
arXiv Detail & Related papers (2020-07-13T17:10:34Z) - Mitigating Gender Bias Amplification in Distribution by Posterior
Regularization [75.3529537096899]
We investigate the gender bias amplification issue from the distribution perspective.
We propose a bias mitigation approach based on posterior regularization.
Our study sheds the light on understanding the bias amplification.
arXiv Detail & Related papers (2020-05-13T11:07:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.