"Was my contribution fairly reviewed?"

Germán, Daniel M.; Robles, Gregório; Poo-Caamaño, Germán; Yang, Xin; Iida, Hajimu; Inoue, Katsuro

doi:10.1145/3180155.3180217

Cited by 28 publications

(9 citation statements)

References 63 publications

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“…ASE 2019 has organized first International Workshop on Explainable Software [10] where issues of ethical AI were extensively discussed. German et al have studied different notions of fairness in the context of code reviews [29]. In summary, the importance of fairness in software is rising rapidly.…”

Section: Previous Workmentioning

confidence: 99%

Fairway: a way to build fair ML software

Chakraborty

Majumder

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

106

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Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a biased manner that gives undue advantages to a specific group of people (where those groups are determined by sex, race, etc.). This łalgorithmic discriminationž in the AI software systems has become a matter of serious concern in the machine learning and software engineering community. There have been works done to find łalgorithmic biasž or łethical biasž in software system. Once the bias is detected in the AI software system, mitigation of bias is extremely important. In this work, we a) explain how ground truth bias in training data affects machine learning model fairness and how to find that bias in AI software, b) propose a method Fairway which combines preprocessing and in-processing approach to remove ethical bias from training data and trained model. Our results show that we can find bias and mitigate bias in a learned model, without much damaging the predictive performance of that model. We propose that (1) testing for bias and (2) bias mitigation should be a routine part of the machine learning software development life cycle. Fairway offers much support for these two purposes. CCS CONCEPTS • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning.

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Section: Previous Workmentioning

confidence: 99%

Fairway: a way to build fair ML software

Chakraborty

Majumder

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

106

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“…Egelman et al [36] focus on pushback, defined as "the perception of unnecessary interpersonal conflict in code review while a reviewer is blocking a change request." Other studies focus on specific aspects related to conflicts such as unfair treatment [42], and profanity and insults [70,72] during code review. Empirical findings by German et al [42] indicate that the majority of contributors to OpenStack, a large industrial OSS ecosystem, have experience with unfair treatment during code review.…”

Section: Social Code Review and Conflictsmentioning

confidence: 99%

Interpersonal Conflicts During Code Review

Gonçalves

Çalıklı

Bacchelli

2022

Proc. ACM Hum.-Comput. Interact.

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Code review consists of manual inspection, discussion, and judgment of source code by developers other than the code's author. Due to discussions around competing ideas and group decision-making processes, interpersonal conflicts during code reviews are expected. This study systematically investigates how developers perceive code review conflicts and addresses interpersonal conflicts during code reviews as a theoretical construct. Through the thematic analysis of interviews conducted with 22 developers, we confirm that conflicts during code reviews are commonplace, anticipated and seen as normal by developers. Even though conflicts do happen and carry a negative impact for the review, conflicts-if resolved constructively-can also create value and bring improvement. Moreover, the analysis provided insights on how strongly conflicts during code review and its context (i.e., code, developer, team, organization) are intertwined. Finally, there are aspects specific to code review conflicts that call for the research and application of customized conflict resolution and management techniques, some of which are discussed in this paper. Preprint: https://arxiv.org/abs/2201.05425 Data and material: https://doi.org/10.5281/zenodo.5848794

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“…The fairness definitions are mostly based on equality notions of fairness, but others might be more relevant for certain use-cases (e.g. affirmative actions [123], equity, need [58]). Besides, the identification of unfair situations through causality is also exploited by Madras et al [115].…”

Section: Overviewmentioning

confidence: 99%

“…Other lines of work within computer science research are also interested in fairness. We specifically highlight works on designing methods to develop fairer software [107,192], coping with software designer biases [32,81,94,148,162,193], fair processes to design software [25,58,145]. For instance, German et al [58] see code reviewing as a decision process where codes from different categories of population might be more or less often accepted, Rahman et al and Bird et al [25,145] point out that bug-fix datasets are biased due to historical decisions of the engineers producing data samples.…”

Section: "Fair" Software Engineeringmentioning

confidence: 99%

“…We specifically highlight works on designing methods to develop fairer software [107,192], coping with software designer biases [32,81,94,148,162,193], fair processes to design software [25,58,145]. For instance, German et al [58] see code reviewing as a decision process where codes from different categories of population might be more or less often accepted, Rahman et al and Bird et al [25,145] point out that bug-fix datasets are biased due to historical decisions of the engineers producing data samples. Other papers such as [16,22,24,61,80,136,165,189] reflect on how projects (data science process, creation of fairness definitions) are conducted and how unfairness is seen and might arise in general from the problem formulation perspective.…”

Section: "Fair" Software Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

2021

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The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.

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"Was my contribution fairly reviewed?"

Cited by 28 publications

References 63 publications

Fairway: a way to build fair ML software

Fairway: a way to build fair ML software

Interpersonal Conflicts During Code Review

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

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