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Found 10 papers, 2 papers with code
SocialGenPod: Privacy-Friendly Generative AI Social Web Applications with Decentralised Personal Data Stores
Unlike centralised Web and data architectures that keep user data tied to application and service providers, we show how one can use Solid -- a decentralised Web specification -- to decouple user data from generative AI applications.
FairTargetSim: An Interactive Simulator for Understanding and Explaining the Fairness Effects of Target Variable Definition
Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications on fairness: biases are often encoded in target variable definition itself, before any data collection or training.
On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models
The surprisingly likely criterion in the seminal work of Prelec (the Bayesian Truth Serum) guarantees truthfulness in a game-theoretic multi-agent setting, by rewarding rational agents to maximise the expected information gain with their answers w. r. t.
Decentralised, Scalable and Privacy-Preserving Synthetic Data Generation
Synthetic data is emerging as a promising way to harness the value of data, while reducing privacy risks.
Human-Guided Fair Classification for Natural Language Processing
While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e. g., in cases of asymmetric counterfactuals).
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
For example, in multi-stage settings where decisions are made in multiple screening rounds, we use our framework to derive the minimal distributions required to design a fair algorithm.
Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain
For the first time, we show how to implement a trustless and transparent oracle in Ethereum.
Crowdsourcing with Fairness, Diversity and Budget Constraints
Recent studies have shown that the labels collected from crowdworkers can be discriminatory with respect to sensitive attributes such as gender and race.
Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd
We propose a novel mechanism that assigns gold tasks to only a few workers and exploits transitivity to derive accuracy of the rest of the workers from their peers' accuracy.
Parameter Database : Data-centric Synchronization for Scalable Machine Learning
We propose a new data-centric synchronization framework for carrying out of machine learning (ML) tasks in a distributed environment.
