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Found 9 papers, 2 papers with code
Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms
In this paper, we focus on the most likely candidate for direct discrimination in the algorithmic context, termed inherent direct discrimination, where a proxy is inextricably linked to a protected characteristic.
Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation
Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of computational analysis.
Human-Computer Interaction
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
On the Apparent Conflict Between Individual and Group Fairness
It draws on theoretical discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fundamentally in conflict.
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.
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.
Fairness in Machine Learning: Lessons from Political Philosophy
What does it mean for a machine learning model to be `fair', in terms which can be operationalised?
Computers and Society
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.
