A sociotechnical view of algorithmic fairness
Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates
systemic discrimination in automated decision‐making, providing opportunities to improve …
systemic discrimination in automated decision‐making, providing opportunities to improve …
Applications of statistical causal inference in software engineering
J Siebert - Information and Software Technology, 2023 - Elsevier
Context: The aim of statistical causal inference (SCI) methods is to estimate causal effects
from observational data (ie, when randomized controlled trials are not possible). In this …
from observational data (ie, when randomized controlled trials are not possible). In this …
Explaining black-box algorithms using probabilistic contrastive counterfactuals
There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that
aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to …
aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to …
Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and …
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 …
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
Tailoring data source distributions for fairness-aware data integration
Data scientists often develop data sets for analysis by drawing upon sources of data
available to them. A major challenge is to ensure that the data set used for analysis has an …
available to them. A major challenge is to ensure that the data set used for analysis has an …
Identifying insufficient data coverage for ordinal continuous-valued attributes
Appropriate training data is a requirement for building good machine-learned models. In this
paper, we study the notion of coverage for ordinal and continuous-valued attributes, by …
paper, we study the notion of coverage for ordinal and continuous-valued attributes, by …
Fairness-aware range queries for selecting unbiased data
We are being constantly judged by automated decision systems that have been widely
criticised for being discriminatory and unfair. Since an algorithm is only as good as the data …
criticised for being discriminatory and unfair. Since an algorithm is only as good as the data …
" Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?
RS Geiger, D Cope, J Ip, M Lotosh, A Shah… - arxiv preprint arxiv …, 2021 - arxiv.org
Supervised machine learning, in which models are automatically derived from labeled
training data, is only as good as the quality of that data. This study builds on prior work that …
training data, is only as good as the quality of that data. This study builds on prior work that …
Credibility of scientific information on social media: Variation by platform, genre and presence of formal credibility cues
Responding to calls to take a more active role in communicating their research findings,
scientists are increasingly using open online platforms, such as Twitter, to engage in science …
scientists are increasingly using open online platforms, such as Twitter, to engage in science …
Fairness & friends in the data science era
The data science era is characterized by data-driven automated decision systems (ADS)
enabling, through data analytics and machine learning, automated decisions in many …
enabling, through data analytics and machine learning, automated decisions in many …