The document discusses explainable AI (XAI) and responsible AI (RAI) as essential tools for transparency and accountability in AI. It covers the main principles of RAI, including fairness, transparency, accountability, and privacy. It explains why XAI is needed to build trust in models and ensure their fairness, safety, and user comprehension. Various XAI techniques are presented, like model explainers and feature importance, as well as challenges like ensuring explanations are useful and appropriate for users. Real-world examples using XAI tools on tasks like image classification and text analysis are provided.

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Ezequiel Lanza
AI open source Evangelist @intel
The Search for Transparency and
Accountability in the Age of AI: RAI &
XAI as essential tools

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Agenda
• Responsible AI
• Main principles
• Development phases
• Explainable AI - Algorithm-centered XAI
• Why is it needed?
• Examples, use cases (CV and NLP), SHAP, open source toolkit (Intel)
• Explainable AI – Human-centered XAI
• Challenges
• Approach

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Photo by Ivan Torres on Unsplash

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1st principle : Fairness
Photo by Pinar Kucuk on Unsplash

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2nd principle : Transparency
Photo by Nadya Spetnitskaya on Unsplash

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3rd principle : Accountability
Photo by Jay Gajjar on Unsplash

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4th principle : Privacy and data protection
Photo by Brenna Huff on Unsplash

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Conclusion
Photo by Giorgio Trovato on Unsplash

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Building Trustworthy models (Developer perspective)
• Development Phases
• Remove disparities on BIASED datasets. (IBM®’s AI 360 & Google®
What if)
• Detect Proxy variables ( marital status  Sexual orientation, Geographic
patterns  Ethnicity)
• Include data and model governance: datasheets& datasets / Model
cards
• Explainable AI: XAI is essential for building trust, enabling user
comprehension, and ensuring fairness and safety in AI applications.
• Protect data when implementing: safety, and security: PPML, OpenFL

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AIF360 (IBM®) – Check bias metric
Dataset: German credit scoring

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WHY IS XAI NEEDED?
Why did you do that?
Why not something else?
How do I correct an error?
Should I trust it?
Is this Email Fraud?

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“Explainability provides insights to a targeted audience to fulfill
a need, whereas interpretability is the degree to which the provided
insights can make sense for the targeted audience’s domain
knowledge.”
from paper Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities:
https://doi.org/10.1016/j.knosys.2023.110273

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Image from paper Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities:
https://doi.org/10.1016/j.knosys.2023.110273

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From Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
https://doi.org/10.1016/j.inffus.2023.101805

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Explainers
From Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
https://doi.org/10.1016/j.inffus.2023.101805

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Techniques
• Model explainers (Global)– Build a parallel explainable model
• Model agnostic (Local) – Feature weights
From Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
https://doi.org/10.1016/j.inffus.2023.101805

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Local (or global) Explanation
From https://shap.readthedocs.io/en/latest/

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Intel® Explainable
AI Tools
https://github.com/IntelAI/intel
-xai-tools/tree/main/notebooks

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EXAMPLE (Heart Disease) 1/4
• Explore dataset

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EXAMPLE (Heart Disease) 2/4
• Define a NN model
• Train

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EXAMPLE (Heart Disease) 3/4
• Connectivity graph

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EXAMPLE (Heart Disease) 4/4
• Explainabilty

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Text Classifier ("distilbert-base-uncased”) 1/2
• After a model is trained
• Run explainabity

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Text Classifier ("distilbert-base-uncased”) 2/2

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Are these explanations what the user needs?
From Explanation in artificial intelligence: Insights from the social sciences https://doi.org/10.1016/j.artint.2018.07.007

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Photo by Michal Czyz on Unsplash

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Help Me Help the AI
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim, Elizabeth
Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández (https://arxiv.org/abs/2210.03735)

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Merlin - Interview to end-users
• RQ1: What are end-users’ XAI needs in
real-world AI ap- plications?
• RQ2: How do end-users intend to use XAI
explanations1?
• RQ3: How are existing XAI approaches
perceived by end- users?
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
(https://arxiv.org/abs/2210.03735)

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Merlin - Explanations showcased to end-users (High-AI
and Low-AI knowledge)
• How are they perceived?
• What do they like or not
like?
• How those can be
improved?
• How much it helps to
understand AI reasoning?
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
(https://arxiv.org/abs/2210.03735)

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XAI Perceptions
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
(https://arxiv.org/abs/2210.03735)

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GAP in XAI
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
(https://arxiv.org/abs/2210.03735)

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Recap : XAI and RAI
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI : https://doi.org/10.1016/j.inffus.2019.12.0

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Conclusion
• There is no one-fits-all solution.
• Real user studies can expose pitfalls in XAI existing methods.
• Human cognition can help. Explanations should be designed with
end-users. Pick the WHO and the HOW to decide the right
approach.
• XAI has to answer WHY not just WHAT.
• Explanations are part of your value proposition

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Call to action
• Help to reduce the gap of what is needed (Creator consumer
gap), good for devs but not for users.
• Contribute to making models scale to multiple domains
(contribute to the applicability of the models, what worked on HC
can work Finance)

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Notices & Disclaimers
• © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its
subsidiaries. Other names and brands may be claimed as the property of others​.

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