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20202023
most citedBias in Machine Learning -- What is it Good for?

51 citations · 114 across the 12 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.MA2020

Modelling Human Routines: Conceptualising Social Practice Theory for Agent-Based Simulation

Rijk Mercuur, Virginia Dignum, Catholijn M. Jonker

Our routines play an important role in a wide range of social challenges such as climate change, disease outbreaks and coordinating staff and patients in a hospital. To use agent-b…

cs.AI2020★ 7 cited

Contestable Black Boxes

Andrea Aler Tubella, Andreas Theodorou, Virginia Dignum +1

The right to contest a decision with consequences on individuals or the society is a well-established democratic right. Despite this right also being explicitly included in GDPR in…

cs.CY2020

A socio-technical framework for digital contact tracing

Ricardo Vinuesa, Andreas Theodorou, Manuela Battaglini +1

In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applicatio…

cs.MA2020★ 1 cited

Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation

Frank Dignum, Virginia Dignum, Paul Davidsson +11

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How…

cs.AI2020

Improving Confidence in the Estimation of Values and Norms

Luciano Cavalcante Siebert, Rijk Mercuur, Virginia Dignum +2

Autonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior is aligned with…

cs.AI2020★ 51 cited

Bias in Machine Learning -- What is it Good for?

Thomas Hellström, Virginia Dignum, Suna Bensch

In public media as well as in scientific publications, the term \emph{bias} is used in conjunction with machine learning in many different contexts, and with many different meaning…