Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification

Krasanakis, Emmanouil; Spyromitros-Xioufis, Eleftherios; Papadopoulos, Symeon; Kompatsiaris, Ioannis

doi:10.1145/3178876.3186133

Cited by 105 publications

(76 citation statements)

References 20 publications

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“…In-processing methods modify the learning algorithm to eliminate discriminatory behavior. These interventions are typically learner-specific [5], [9]- [12]. For instance, Zafar et al [5] add fairness-related constraints in the objective function of a logistic regression model to account for fairness.…”

Section: Related Workmentioning

confidence: 99%

FAE: A Fairness-Aware Ensemble Framework

Iosifidis

Fetahu

Ntoutsi

2019

2019 IEEE International Conference on Big Data (Big Data)

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Automated decision making based on big data and machine learning (ML) algorithms can result in discriminatory decisions against certain protected groups defined upon personal data like gender, race, sexual orientation etc. Such algorithms designed to discover patterns in big data might not only pick up any encoded societal biases in the training data, but even worse, they might reinforce such biases resulting in more severe discrimination. The majority of thus far proposed fairness-aware machine learning approaches focus solely on the pre-, in-or post-processing steps of the machine learning process, that is, input data, learning algorithms or derived models, respectively. However, the fairness problem cannot be isolated to a single step of the ML process. Rather, discrimination is often a result of complex interactions between big data and algorithms, and therefore, a more holistic approach is required.The proposed FAE (Fairness-Aware Ensemble) framework combines fairness-related interventions at both pre-and postprocessing steps of the data analysis process. In the preprocessing step, we tackle the problems of under-representation of the protected group (group imbalance) and of class-imbalance by generating balanced training samples. In the post-processing step, we tackle the problem of class overlapping by shifting the decision boundary in the direction of fairness.

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Section: Related Workmentioning

confidence: 99%

FAE: A Fairness-Aware Ensemble Framework

Iosifidis

Fetahu

Ntoutsi

2019

2019 IEEE International Conference on Big Data (Big Data)

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“…While there is a heavy literature on model interpretation [185], [186], it is not clear how to address feedback on the data level. In the model fairness literature [187], one approach to reducing unfairness is to fix the data. In data cleaning, ActiveClean and BoostClean are interesting approaches for fixing the data to improve model accuracy.…”

Section: Future Research Challengesmentioning

confidence: 99%

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective

Roh

Heo

Whang

2021

IEEE Trans. Knowl. Data Eng.

641

270

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Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research. TaskApproach Techniques Data discovery Sharing Collaborative Analysis [9]-[11] Web [12]-[17]

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“…Under such circumstances, one may wish to transit to a new model that inherits the original predictive performance but which ensures non-discriminatory outputs. A possible option is to edit the sensitive attributes to remove any bias, therefore reducing the disparate impact in the task

, and then training a new model on the edited dataset [ 62 , 63 ]. Alternatively, in very specific scenarios where the sensitive information is not leaked through additional features, it is possible to build a copy by removing the protected data variables [ 64 ].…”

Section: Differential Replication In Practicementioning

confidence: 99%

Environmental Adaptation and Differential Replication in Machine Learning

Unceta

Nin

Pujol

2020

Entropy

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When deployed in the wild, machine learning models are usually confronted with an environment that imposes severe constraints. As this environment evolves, so do these constraints. As a result, the feasible set of solutions for the considered need is prone to change in time. We refer to this problem as that of environmental adaptation. In this paper, we formalize environmental adaptation and discuss how it differs from other problems in the literature. We propose solutions based on differential replication, a technique where the knowledge acquired by the deployed models is reused in specific ways to train more suitable future generations. We discuss different mechanisms to implement differential replications in practice, depending on the considered level of knowledge. Finally, we present seven examples where the problem of environmental adaptation can be solved through differential replication in real-life applications.

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Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification

Cited by 105 publications

References 20 publications

FAE: A Fairness-Aware Ensemble Framework

FAE: A Fairness-Aware Ensemble Framework

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective

Environmental Adaptation and Differential Replication in Machine Learning

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