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Binh Luong Thanh

dblp:41/8809 · also Binh Thanh Luong · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › fairness › fairness auditing
discrimination discovery		 0.112011
k-NN as an implementation of situation testing for discrimination discovery and prevention · KDD 2011
Machine learning › Trustworthy machine learning › fairness › bias mitigation
discrimination prevention		 0.112011
k-NN as an implementation of situation testing for discrimination discovery and prevention · KDD 2011
Machine learning › Trustworthy machine learning
fairness		 0.112011
k-NN as an implementation of situation testing for discrimination discovery and prevention · KDD 2011
Data mining › predictive modeling
classification		 0.012011
k-NN as an implementation of situation testing for discrimination discovery and prevention · KDD 2011

Methods — techniques the papers use, named apart from their topics

situation testing · 0.2kNN classification · 0.1k-NN classification · 0.1
YearPublicationVenuePosition
2011 k-NN as an implementation of situation testing for discrimination discovery and prevention
abstract
With the support of the legally-grounded methodology of situation testing, we tackle the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification. A tuple is labeled as discriminated if we can observe a significant difference of treatment among its neighbors belonging to a protected-by-law group and its neighbors not belonging to it. Discrimination discovery boils down to extracting a classification model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classifier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals.
Binh Luong Thanh, Salvatore Ruggieri, Franco Turini
 KDD1