Learn techniques for creating responsible AI in high-risk applications
Today, ML systems make high-stakes decisions in employment, bail, parole, lending, and in many other high-risk applications throughout the world's economies.
But ML still presents risks to both operators and consumers, whether by unintentional misuse or malicious abuse. Algorithmic discrimination, data privacy violations, training data security breaches and more all damage the reputation and the potential of ML systems.
Such risks must be mitigated, and practitioners must prepare themselves for all possible outcomes when designing ML technologies.
If you'd like to sever the potential for your ML technologies to be abused, check out this e-book for the best practices behind automated decision-making design.