Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Ruf, Boris"'
Autor:
Ruf, Boris, Detyniecki, Marcin
Carbon footprint quantification is key to well-informed decision making over carbon reduction potential, both for individuals and for companies. Many carbon footprint case studies for products and services have been circulated recently. Due to the co
Externí odkaz:
http://arxiv.org/abs/2310.01278
Autor:
Ruf, Boris, Detyniecki, Marcin
To implement fair machine learning in a sustainable way, choosing the right fairness objective is key. Since fairness is a concept of justice which comes in various, sometimes conflicting definitions, this is not a trivial task though. The most appro
Externí odkaz:
http://arxiv.org/abs/2105.00667
Autor:
Ruf, Boris, Detyniecki, Marcin
Most fair regression algorithms mitigate bias towards sensitive sub populations and therefore improve fairness at group level. In this paper, we investigate the impact of such implementation of fair regression on the individual. More precisely, we as
Externí odkaz:
http://arxiv.org/abs/2104.04353
Autor:
Ruf, Boris, Detyniecki, Marcin
Fairness is a concept of justice. Various definitions exist, some of them conflicting with each other. In the absence of an uniformly accepted notion of fairness, choosing the right kind for a specific situation has always been a central issue in hum
Externí odkaz:
http://arxiv.org/abs/2102.08453
Autor:
Ruf, Boris, Detyniecki, Marcin
The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society. Many current legal standards demand to remove sensitive attributes from data in orde
Externí odkaz:
http://arxiv.org/abs/2009.06251
The potential risk of AI systems unintentionally embedding and reproducing bias has attracted the attention of machine learning practitioners and society at large. As policy makers are willing to set the standards of algorithms and AI techniques, the
Externí odkaz:
http://arxiv.org/abs/2003.06920
Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very ef
Externí odkaz:
http://arxiv.org/abs/1911.05369
The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this challenge for discrete variables, only a few ideas exist for continuou
Externí odkaz:
http://arxiv.org/abs/1911.04929
Towards conversational agents that are capable of handling more complex questions on contractual conditions, formalizing contract statements in a machine readable way is crucial. However, constructing a formal model which captures the full scope of a
Externí odkaz:
http://arxiv.org/abs/1910.04424
Publikováno v:
Data Science & Engineering; Jun2020, Vol. 5 Issue 2, p99-110, 12p