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Found 12 papers, 1 papers with code
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems
Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life.
The Weak Supervision Landscape
In this paper we propose a framework for categorising weak supervision settings with the aim of: (1) helping the dataset owner or annotator navigate through the available options within weak supervision when prescribing an annotation process, and (2) describing existing annotations for a dataset to machine learning practitioners so that we allow them to understand the implications for the learning process.
Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models
At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners.
Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach
To this end, we produce estimates of the uncertainty of a given explanation by measuring the ordinal consensus amongst a set of diverse bootstrapped surrogate explainers.
Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups
We then study the effect of variety and number of age-groups used during training on generalisation to unseen age-groups and observe that an increase in the number of training age-groups tends to increase the apparent emotional facial expression recognition performance on unseen age-groups.
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage
This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings.
On the overlooked issue of defining explanation objectives for local-surrogate explainers
In this work we review the similarities and differences amongst multiple methods, with a particular focus on what information they extract from the model, as this has large impact on the output: the explanation.
Hypothesis Testing for Class-Conditional Label Noise
Differently, in this paper we assume we have access to a set of anchor points whose true posterior is approximately 1/2.
Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia.
FACE: Feasible and Actionable Counterfactual Explanations
First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e. g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports).
Ordinal Regression as Structured Classification
This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels.
Label Propagation for Learning with Label Proportions
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags.
