Cover Image

Explanation Hacking: The perils of algorithmic recourse

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sullivan, Emily, Kasirzadeh, Atoosa
Format:	Preprint
Published:	2024
Subjects:	Computers and Society Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Two Types of AI Existential Risk: Decisive and Accumulative
by: Kasirzadeh, Atoosa
Published: (2024)

Bridging the Gap in the Responsible AI Divides
by: Gyevnár, Bálint, et al.
Published: (2026)

Characterizing AI Agents for Alignment and Governance
by: Kasirzadeh, Atoosa, et al.
Published: (2025)

Measurement challenges in AI catastrophic risk governance and safety frameworks
by: Kasirzadeh, Atoosa
Published: (2024)

AI Safety for Everyone
by: Gyevnar, Balint, et al.
Published: (2025)

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
by: Tang, Zeyu, et al.
Published: (2025)

SIDEs: Separating Idealization from Deceptive Explanations in xAI
by: Sullivan, Emily
Published: (2024)

AI, Digital Platforms, and the New Systemic Risk
by: Hacker, Philipp, et al.
Published: (2025)

Epistemic Injustice in Generative AI
by: Kay, Jackie, et al.
Published: (2024)

Artificial Intelligence in Experimental Approaches: Growth Hacking, Lean Startup, Design Thinking, and Agile
by: Omidmand, Parisa, et al.
Published: (2026)

Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
by: Meding, Kristof, et al.
Published: (2023)

The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems
by: Luo, Ziming, et al.
Published: (2025)

To Defend Against Cyber Attacks, We Must Teach AI Agents to Hack
by: Zhuo, Terry Yue, et al.
Published: (2026)

CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models
by: Pistilli, Giada, et al.
Published: (2024)

Self-Explanation in Social AI Agents
by: Basappa, Rhea, et al.
Published: (2025)

Analysis of Customer Journeys Using Prototype Detection and Counterfactual Explanations for Sequential Data
by: Kinjo, Keita
Published: (2025)

Manimator: Transforming Research Papers into Visual Explanations
by: P, Samarth, et al.
Published: (2025)

Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
by: Löfström, Tuwe, et al.
Published: (2024)

How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law
by: Fresz, Benjamin, et al.
Published: (2024)

Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System
by: Ain, Qurat Ul, et al.
Published: (2026)

Ordre public exceptions for algorithmic surveillance patents
by: Wernick, Alina
Published: (2024)

Generative Value Conflicts Reveal LLM Priorities
by: Liu, Andy, et al.
Published: (2025)

Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking Forums
by: Moreno-Vera, Felipe
Published: (2024)

Counterfactual Explanations for Hypergraph Neural Networks
by: Veglianti, Fabiano, et al.
Published: (2026)

A debiasing technique for place-based algorithmic patrol management
by: Einarsson, Alexander, et al.
Published: (2023)

Cognitive Silicon: An Architectural Blueprint for Post-Industrial Computing Systems
by: Haryanto, Christoforus Yoga, et al.
Published: (2025)

Procedural Fairness via Group Counterfactual Explanation
by: Popoola, Gideon, et al.
Published: (2026)

Selecting the Right LLM for eGov Explanations
by: Limonad, Lior, et al.
Published: (2025)

Context-aware, Ante-hoc Explanations of Driving Behaviour
by: Grundt, Dominik, et al.
Published: (2025)

Investigating Algorithm Review Boards for Organizational Responsible Artificial Intelligence Governance
by: Hadley, Emily, et al.
Published: (2024)

A sequential transit network design algorithm with optimal learning under correlated beliefs
by: Yoon, Gyugeun, et al.
Published: (2023)

Beyond Technocratic XAI: The Who, What & How in Explanation Design
by: Dhar, Ruchira, et al.
Published: (2025)

Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
by: Smart, Andrew, et al.
Published: (2024)

Can LLMs Identify Gaps and Misconceptions in Students' Code Explanations?
by: Oli, Priti, et al.
Published: (2024)

The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
by: Ehsan, Upol, et al.
Published: (2021)

The Only Way is Ethics: A Guide to Ethical Research with Large Language Models
by: Ungless, Eddie L., et al.
Published: (2024)

KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education
by: Kim, Woojin, et al.
Published: (2026)

Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations
by: Hamman, Faisal, et al.
Published: (2025)

Prompt-Counterfactual Explanations for Generative AI System Behavior
by: Goethals, Sofie, et al.
Published: (2026)

TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE
by: Panagiotou, Emmanouil, et al.
Published: (2024)