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Found 12 papers, 4 papers with code
Can ChatGPT Read Who You Are?
We compare the personality trait estimations made by ChatGPT against those by human raters and report ChatGPT's competitive performance in inferring personality traits from text.
Addressing Membership Inference Attack in Federated Learning with Model Compression
In this paper, we show that the effectiveness of these attacks on the clients negatively correlates with the size of the client datasets and model complexity.
Structural Group Unfairness: Measurement and Mitigation by means of the Effective Resistance
We denote the social capital disparity among different groups in a network as structural group unfairness, and propose to mitigate it by means of a budgeted edge augmentation heuristic that systematically increases the social capital of the most disadvantaged group.
FairShap: A Data Re-weighting Approach for Algorithmic Fairness based on Shapley Values
Algorithmic fairness is of utmost societal importance, yet the current trend in large-scale machine learning models requires training with massive datasets that are frequently biased.
BIASeD: Bringing Irrationality into Automated System Design
Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions.
Racial Bias in the Beautyverse
This short paper proposes a preliminary and yet insightful investigation of racial biases in beauty filters techniques currently used on social media.
A Snapshot of the Frontiers of Client Selection in Federated Learning
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning.
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters
Augmented Reality or AR filters on selfies have become very popular on social media platforms for a variety of applications, including marketing, entertainment and aesthetics.
DiffWire: Inductive Graph Rewiring via the Lovász Bound
Graph Neural Networks (GNNs) have been shown to achieve competitive results to tackle graph-related tasks, such as node and graph classification, link prediction and node and graph clustering in a variety of domains.
Ranked #1 on
Graph Classification
on IMDB-BINARY
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
Furthermore, unlike existing methods, we introduce a location prediction branch in MobTCast as an auxiliary task to model the geographical context and predict the next location.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
