-
Fairness and Abstraction in Sociotechnical Systems
A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as... -
Fair detection of poisoning attacks in federated learning
Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model....-
PDF
The resource: 'Link to Publication' is not accessible as guest user. You must login to access it!
-
PDF
-
Conformity a Path-Aware Homophily measure for Node-Attributed Networks
Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached...-
PDF
The resource: 'Link to Publication' is not accessible as guest user. You must login to access it!
-
PDF
-
Designing for human rights in AI
In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance... -
Democratizing Algorithmic Fairness
Machine learning algorithms can now identify patterns and correlations in (big) datasets and predict outcomes based on the identified patterns and correlations. They can then... -
Big Data Ethics
The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and...
