SC
S.E. Carter
9 records found
1
Authored
e-Health data is sensitive and consenting to the collection, processing, and sharing involves compliance with legal requirements, ethical standards, and appropriate digital tools. We explore two legal-ethical challenges: 1) What are the scope and requirements of digital health da
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Contributed
From Data to Decision
Investigating Bias Amplification in Decision-Making Algorithms
This research investigates how biases in datasets influence the outputs of decision-making algorithms, specifically whether these biases are merely reflected or further amplified by the algorithms. Using the Adult/Census Income dataset from the UCI Machine Learning Repository, th
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A study on bias against women in recruitment algorithms
Surveying the fairness literature in the search for a solution
Algorithms have a more prominent presence than ever in the domain of recruitment. Many different tasks ranging from finding candidates to scanning resumes are handled more and more by algorithms and less by humans. Automating these tasks has led to bias being exhibited towards di
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Influence of Data Processing on the Algorithm Fairness vs. Accuracy Trade-off
Building Pareto Fronts for Equitable Algorithmic Decisions
Algorithmic bias due to training from biased data is a widespread issue. Bias mitigation techniques such as fairness-oriented data pre-, in-, and post-processing can help but usually come at the cost of model accuracy. For this contribution, we first conducted a literature review
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Algorithmic Fairness: Encouraging Exclusionary Diversity
(instead of Inclusionary Pluriversality)
AI is becoming significantly more impactful in society, especially with regard to decision-making. Algorithmic fairness is the field wherein the fairness of an AI algorithm is defined, subsequently evaluated, and ideally improved. This paper uses a fairness decision tree to crit
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Machine Learning (ML) algorithms have the potential to reproduce biases that already exist in society, a fact that leads to scholarly work trying to quantify algorithmic discrimination through fairness metrics. Although there are now a plethora of metrics, some of them are even c
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Large language models (LLMs) are widely used tools that assist us by answering various questions. Humans implicitly use contrast as a natural way to think about and seek explanations (i.e., "Why A and not B?"). Explainability is a challenging aspect of LLMs, as we do not truly un
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