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.
- Identify potential bias and discrimination in data science models
- Use preventive measures to minimize bias when developing data modeling
pipelines
- Understand what data pipeline components implicate security and
privacy concerns
- Write data processing and modeling code that implements best practices
for fairness
- Recognize the complex interrelationships between fairness, privacy,
and data security created by the use of machine learning models
- Apply normative and legal concepts relevant to evaluating the fairness
of machine learning models
