Explainability
Explainability is about making AI's decision-making transparent, so users can understand and trust how it reaches its conclusions.
Example - AI in Finance: For an AI deciding on loan approvals, explainability means it can show which factors influenced its decision, helping applicants understand or contest the outcome.
6
52 reads
CURATED FROM
IDEAS CURATED BY
Explore pillars of Trustworthy and Responsible AI
“
Similar ideas to Explainability
Establishing Ethical Guidelines for Transparent AI Development
To address this, there's a pressing need to establish clear guidelines and standards for AI development. These guidelines should encompass not only the technical aspects of AI but also ethical considerations.
Creating a transparent framework that outlines the development process, data sour...
Separate decision quality from results
People have a natural tendency to conflate the quality of a decision with the quality of its outcome. They're not the same thing.
You can make a smart, rational choice but still get poor results. That doesn't mean you should have made a different choice; it simply means that other f...
Read & Learn
20x Faster
without
deepstash
with
deepstash
with
deepstash
Personalized microlearning
—
100+ Learning Journeys
—
Access to 200,000+ ideas
—
Access to the mobile app
—
Unlimited idea saving
—
—
Unlimited history
—
—
Unlimited listening to ideas
—
—
Downloading & offline access
—
—
Supercharge your mind with one idea per day
Enter your email and spend 1 minute every day to learn something new.
I agree to receive email updates