Gap Analysis and Gap Prevention
Problem:
We keep hearing "Responsible AI".
No idea what that actually means.
If this sounds familiar, you're not alone.
Responsible AI is trending.
But how do ideals and ethics translate into how we run a data science team?
Gap Analysis and Gap Prevention turn abstract concepts into practical changes.
Designed by globally-recognized Responsible AI experts to help you do better work, with less work.
Get to the root of how you work with data.
No one intends for unethical outcomes from their data systems. But they happen.
Gap Analysis and Prevention help you spot their root causes earlier and take small, templated steps to fix them, designed by globally-respected Responsible AI experts.
Get real.
Move the conversation about Responsible AI from:
"do the right thing" to "do the thing right".
This is at the heart of moving faster and breaking fewer things.
Better ways of working means less waste, high velocity, better ROI, and fewer harms and unintended consequences.
Efficacy
Execute Data science workflows effectively. Recall fewer systems from prod. Deal with less unacceptable and unexpected behavior.
Velocity
Projects move quickly from concept to production with clear expectations and requirements, coordination, and high visibility.
Consistency
Follow predictable standards and best-practices that everyone agrees on.
Continuity
Known data failure modes are analyzed and small steps are constantly taken to prevent them.
Knowability
An automated papertrail of how your people, processes, and tools worked together.
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