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Weizenbaum Institute for the
Networked Society
Berlin, Germany
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JOINT PROJECT
The Weizenbaum Institute
investigates the current changes
in all aspects of society occurring
in response to digitalisation.
4
Criticality of AI-based systems
5
5 2 14 20
Doktorand/innen Research Fellows Mitarbeiter in der Beirat mit Vertretern aus
Geschäftsstelle des Wirtschaft, Wissenschaft,
Weizenbaum-Instituts Medien, Zivilgesellschaft
Transportation Medicine Human
Resources
Public Safety Finance
AI: Domains
6
7
BIASED AI vs.
BIASED SOCIETY
8
Data
User
Algorithm/
Machine
Providers/
Developers
Impacted
people
BIAS in AI
9
BIG DATA ≠ GOOD DATA
• How was the data collected?
• By whom was the data collected?
• For which purposes?
• Is the data diverse enough to be
considered for training?
• Which historical or idealogical
mappings does it contain?
10
Bias in AI – Gender Shades project (MIT)
11
BIAS
12
Analogies
Photo credit: Quantifying and Reducing Gender
Stereotypes in Word Embeddings, Kai-Wei Chang, Tolga Bolukbasi, Venkatesh
Saligrama
13
Photo credit: Quantifying and Reducing Gender
Stereotypes in Word Embeddings, Kai-Wei Chang, Tolga Bolukbasi, Venkatesh
Saligrama
14
15
BIASED AI vs.
BIASED SOCIETY
16
BIASED AI vs.
BIASED SOCIETY
Educational Approach:
AI-child interaction
17
18
CONTACT
Weizenbaum Institute for the Networked Society
Hardenbergstr. 32, 10623 Berlin
www.weizenbaum-institut.de
Gunay Kazimzade
Gunay.kazimzade@tu-berlin.de

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Racial and gender bias in AI

Notas do Editor

  1. Detecting spams, forecasting stock markets Recongizing illnesess Image recongition Deep learning – autonomous cars Deep learning- the heart of it is NN