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Found 11 papers, 5 papers with code
Demystifying the Draft EU Artificial Intelligence Act
In April 2021, the European Commission proposed a Regulation on Artificial Intelligence, known as the AI Act.
Decentralized Privacy-Preserving Proximity Tracing
This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale.
Cryptography and Security Computers and Society
Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data.
Human-Computer Interaction Computers and Society
Disparate Vulnerability to Membership Inference Attacks
Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model.
Algorithms that Remember: Model Inversion Attacks and Data Protection Law
Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms.
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race.
Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"?
As concerns about unfairness and discrimination in "black box" machine learning systems rise, a legal "right to an explanation" has emerged as a compellingly attractive approach for challenge and redress.
Some HCI Priorities for GDPR-Compliant Machine Learning
In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems.
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions---like taxation, justice, and child protection---are now commonplace.
Like trainer, like bot? Inheritance of bias in algorithmic content moderation
This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence.
Logics and practices of transparency and opacity in real-world applications of public sector machine learning
Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors.
