Realize how the lack of privacy can hurt your customers and your business

Know the privacy implications of storing and processing personal data

Recognize current regulatory frameworks to comply with regarding privacy

Be aware of naive anonymization techniques and their limitations against privacy breaches

See what differential privacy is and how it mitigates privacy risks while enabling data processing

Identify how is it legitimate to store personal data for fairness validation and bias mitigation

Know how to create synthetic data which are statistically valid without violating user rights

See what fairness is, and how automated decisions can be unfair

Identify the possible sources of bias in systems

Know how to quantify and measure fairness

Recognize unfair behavior in decision making systems

Understand the ways of mitigating unfair decisions

Understand the connection between bias, fairness, transparency and explainability

Recognize the stakeholders of a model and why they have incentives for model expalinability

Be aware of various regulatory frameworks and their definition of transparency

Select methods and tools for explaining models, being familiar with their limitations