Kacper Sokol

Orcid: 0000-0002-9869-5896

Affiliations:
  • ETH Zurich, Switzerland
  • University of Bristol, Intelligent Systems Laboratory, UK
  • RMIT University, ARC Centre of Excellence for Automated Decision-Making and Society, Australia (former)


According to our database1, Kacper Sokol authored at least 36 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks.
CoRR, 2024

2023
Simply Logical - The First Three Decades.
Proceedings of the Prolog: The Next 50 Years, 2023

Can Users Correctly Interpret Machine Learning Explanations and Simultaneously Identify Their Limitations?
CoRR, 2023

Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse.
CoRR, 2023

Navigating Explanatory Multiverse Through Counterfactual Path Geometry.
CoRR, 2023

(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability.
CoRR, 2023

Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness.
CoRR, 2023

More Is Less: When Do Recommenders Underperform for Data-rich Users?
CoRR, 2023

Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations.
CoRR, 2023

Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication.
CoRR, 2023

2022
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency.
Softw. Impacts, December, 2022

Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019-2020 Australian Bushfires Case Study.
CoRR, 2022

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components.
CoRR, 2022

Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition).
CoRR, 2022

How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies.
CoRR, 2022

Ethical and Fairness Implications of Model Multiplicity.
CoRR, 2022

BayCon: Model-agnostic Bayesian Counterfactual Generator.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence.
CoRR, 2021

You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source.
CoRR, 2021

2020
One Explanation Does Not Fit All.
Künstliche Intell., 2020

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems.
J. Open Source Softw., 2020

Towards Faithful and Meaningful Interpretable Representations.
CoRR, 2020

LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees.
CoRR, 2020

One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency.
CoRR, 2020

Explainability fact sheets: a framework for systematic assessment of explainable approaches.
Proceedings of the FAT* '20: Conference on Fairness, 2020

FACE: Feasible and Actionable Counterfactual Explanations.
Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019
bLIMEy: Surrogate Prediction Explanations Beyond LIME.
CoRR, 2019

HyperStream: a Workflow Engine for Streaming Data.
CoRR, 2019

Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety.
Proceedings of the Workshop on Artificial Intelligence Safety 2019 co-located with the Thirty-Third AAAI Conference on Artificial Intelligence 2019 (AAAI-19), 2019

2018
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017
The Role of Textualisation and Argumentation in Understanding the Machine Learning Process.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016
Activity Recognition in Multiple Contexts for Smart-House Data.
Proceedings of the 26th International Conference on Inductive Logic Programming (Short papers), 2016


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