TY  - BOOK
AU  - Nielsen, Aileen
TI  - Practical Fairness : : achieving fair and secure data models
SN  - 9789385889783
U1  - 005.743 
PY  - 2021///
CY  - USA
PB  - SPD
KW  - Business Ethics
KW  - Data Science
KW  - Neural Networks
KW  - Human-Computer Interaction (HCI)
KW  - Artificial intelligence
KW  - Information modeling
N2  - Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.

Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms
ER  -