This document contains information about Mauro Bennici, an AI Architect and AI Ethicist. It lists his credentials and experience in fields including AI, data science, and ethics. It also provides links to his website and LinkedIn profile. The document then shares resources on responsible AI, explainable AI, and metrics for evaluating fairness in AI systems. It emphasizes the importance of using multiple metrics and examining individual results rather than averages alone.

1. Mauro Bennici
May 10, 2023
AI ethics
AI Architect & AI Ethicist
https://www.intelligenzaetica.it/
https://www.linkedin.com/in/maurobennici/

2. Mauro Bennici
AI Architect, Kaggle and TechStars mentor, .NET Foundation member, Data
Scientist, Professional Scrum Master (PSM I), Microsoft Certified Trainer (MCT),
Azure Cloud Solutions Associate (MCSA). Member of the SME Digital Alliance
Working Group for Ethics and Artificial Intelligence.
AI Architect & AI Ethicist
https://www.intelligenzaetica.it/
https://www.linkedin.com/in/maurobennici/

3. Source: https://commission.europa.eu/law/law-topic/data-protection/reform/rules-business-and-organisations/principles-gdpr_en

4. Dataset
Principali:
● Inconsci
● Culturali
● Storici
https://datasetsearch.research.google.com/

5. https://www.kaggle.com/competitions/quora-insincere-questions-classification
° Public Datasets
° APIs contracts

6. Source: https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/ethics/h2020_hi_ethics-self-assess_en.pdf

8. Mauro Bennici
Responsible AI

9. Title
Text
https://cloud.google.com/responsible-ai

10. Title
Text
https://sites.research.google/datacardsplaybook/

11. Title
Text
https://pair.withgoogle.com/

12. Numbers
Measuring outcomes for:
● Trust
● Security
https://pair.withgoogle.com/explorables/measuring-fairness/

13. Bias
Principles:
● Unconscious
● Cultural
● Historians
https://pair.withgoogle.com/explorables/hidden-bias/

14. Microsoft
● Products
● Azure
● Development
● Tools
https://github.com/tensorflow/fairness-indicators

15. Mauro Bennici
eXplainable AI

16. Tensorflow Fairnerss
Advantages:
● UI
● Integration with TF
● Examples
https://colab.research.google.com
/github/tensorflow/fairness-
indicators/blob/master/g3doc/tutor
ials/Fairness_Indicators_Pandas_
Case_Study.ipynb
https://github.com/tensorflow/fairness-indicators

17. Fairlearn
Advantages:
● Socio-Tech companies
● Communities
● Examples
https://colab.research.google.com
/github/tensorflow/fairness-
indicators/blob/master/g3doc/tutor
ials/Fairness_Indicators_Pandas_
Case_Study.ipynb
https://fairlearn.org/main/auto_examples/plot_credit_loan_decisions.html#sphx-glr-auto-examples-
plot-credit-loan-decisions-py

18. AI Fairness 360
Text
https://ai-fairness-360.org/

19. AI Fairness 360
Example with LIME
https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_lime.ipynb

20. Metrics
● We do not use summary averages
● We use more than one metric
● Let's look at the individual results
● EX: F1-Score MACRO

21. Mauro Bennici
Thank you!
Q&A time
AI Architect & AI Ethicist
https://www.intelligenzaetica.it/
https://www.linkedin.com/in/maurobennici/