Editing
Responsible Ai
(section)
Jump to navigation
Jump to search
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
== <span style="color: #FFFFFF;">Applying</span> == '''Measuring and mitigating bias with the Fairlearn library:''' <syntaxhighlight lang="python"> from fairlearn.metrics import MetricFrame, demographic_parity_difference from fairlearn.reductions import ExponentiatedGradient, DemographicParity from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score import pandas as pd # Assume X_train, y_train, sensitive_features (e.g., gender) are loaded # Train baseline model model = LogisticRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test) # Evaluate fairness metric_frame = MetricFrame( metrics={"accuracy": accuracy_score}, y_true=y_test, y_pred=y_pred, sensitive_features=sensitive_features_test ) print(metric_frame.by_group) # Shows accuracy broken down by each demographic group dpd = demographic_parity_difference(y_test, y_pred, sensitive_features=sensitive_features_test) print(f"Demographic Parity Difference: {dpd:.3f}") # 0.0 = perfect parity; larger = more disparity # Apply fairness constraint during training mitigator = ExponentiatedGradient(LogisticRegression(), constraints=DemographicParity()) mitigator.fit(X_train, y_train, sensitive_features=sensitive_features_train) y_pred_fair = mitigator.predict(X_test) </syntaxhighlight> ; Responsible AI frameworks and standards : '''EU AI Act''' β Risk-based regulation: prohibited uses, high-risk requirements, transparency obligations : '''NIST AI RMF''' β US government AI Risk Management Framework: Govern, Map, Measure, Manage : '''Google PAIR''' β People + AI Research guidelines for human-AI interaction : '''Microsoft Responsible AI''' β Fairness, Reliability, Privacy, Inclusiveness, Transparency, Accountability : '''Model Cards (Google)''' β Standardized documentation for ML models' intended use and limitations : '''Datasheets for Datasets''' β Documentation standard for training datasets </div> <div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
Summary:
Please note that all contributions to BloomWiki may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see
BloomWiki:Copyrights
for details).
Do not submit copyrighted work without permission!
Cancel
Editing help
(opens in new window)
Navigation menu
Personal tools
Not logged in
Talk
Contributions
Create account
Log in
Namespaces
Page
Discussion
English
Views
Read
Edit
View history
More
Search
Navigation
Main page
Recent changes
Random page
Help about MediaWiki
Tools
What links here
Related changes
Special pages
Page information