Edward Small
YOU?
Author Swipe
View article: How robust is your fair model? Exploring the robustness of prominent fairness strategies
How robust is your fair model? Exploring the robustness of prominent fairness strategies Open
With the introduction of machine learning in high stakes decision-making, ensuring algorithmic fairness has become an increasingly important task. To this end, many mathematical definitions of fairness have been proposed, and a variety of …
View article: Navigating explanatory multiverse through counterfactual path geometry
Navigating explanatory multiverse through counterfactual path geometry Open
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models. Their generation is often subject to technical and domain-specific constraints that aim to maximise their real-lif…
View article: Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness Open
Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike. These two objectives, however, are incompatible when a scoring model is c…
View article: Comprehension Is a Double-Edged Sword: Over-Interpreting Unspecified Information in Intelligible Machine Learning Explanations
Comprehension Is a Double-Edged Sword: Over-Interpreting Unspecified Information in Intelligible Machine Learning Explanations Open
Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear whether users know what insights an explanation offers and, …
View article: Navigating Explanatory Multiverse Through Counterfactual Path Geometry
Navigating Explanatory Multiverse Through Counterfactual Path Geometry Open
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models. Their generation is often subject to technical and domain-specific constraints that aim to maximise their real-lif…
View article: An Analysis of Physics-Informed Neural Networks
An Analysis of Physics-Informed Neural Networks Open
Whilst the partial differential equations that govern the dynamics of our world have been studied in great depth for centuries, solving them for complex, high-dimensional conditions and domains still presents an incredibly large mathematic…
View article: Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations Open
Explainable artificial intelligence techniques are developed at breakneck speed, but suitable evaluation approaches lag behind. With explainers becoming increasingly complex and a lack of consensus on how to assess their utility, it is cha…
View article: How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies Open
With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this, many mathematical definitions of fairness have been propose…
View article: Co-Creation Facilitates Translational Research on Upper Limb Prosthetics
Co-Creation Facilitates Translational Research on Upper Limb Prosthetics Open
People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, hereafter called users, are yet to benefit from the fast-paced growth in academic knowledge within the field of upper …