This document discusses artificial intelligence (AI) in society and key issues around its development and use. It begins with an introduction to AI applications in areas like personalized recommendations, customer service, and fraud detection. It then covers a 5-spoke framework for understanding AI systems, including components like perception, reasoning, communication, decision-making, and interaction. Examples of applications in areas like computer vision, natural language processing, and reinforcement learning are provided. The document also discusses issues like bias, fairness, ethics, accountability and transparency in AI. It profiles Singapore as a thought leader in developing frameworks for ethical and responsible AI.

1. Artificial Intelligence
& Society
Fireside Chat
LUIS F. GONZALEZ
https://www.linkedin.com/in/lgeai/

2. Confused
Yet?
It’s ok, we all are

3. Agenda
Intro to AI in Society (Learning Algorithms) 20 mins
Understanding AI (5 Spokes Framework) 30 mins
• How they help & What it can go wrong
• FEAT (Fairness, Ethics, Accountability & Transparency
Singapore as thought leader 10 mins
• Future of Applied AI

4. AI IN SOCIETY

5. 5
Learn user’s behaviour based on voice commands
and can adjust settings automatically in
subsequent interactions
Target user with personalized products and
services ads based on their demographic profile,
search history, visited sites, liked social media
posts, etc.
Improve efficiency/quality in servicing customers by
using AI assistants (e.g., chatbots, robo-greeters in
bank branches and cardless ATM machines via facial
recognition)
Detect suspicious/fraudulent activities in network
and/or transactions using predictive analytics
Recommend music or videos based on user’s
historical consumption and preferences
Provide best driving routes, ETA, and/or match
drivers with riders based on historical and real-
time data
Smart Home Devices Media & Entertainment Navigation and Transportation
E-commerce & Targeted Ads Customer Service Assistants Security and Fraud Detection
What we see every day….

6. Obliquitous
enough?

7. Capitalising on deadly sins and fears in the name of
profit
Exploiting the Human Condition

8. But with the
potential to
Improve the
Human
Experience
https://aixexchange.com/#levels-of-aix-framework

9. 5 SPOKES AI FRAMEWORK
homo-cognitio

10. What are the Components of AI?
Sensing the world
Perception
Learning from every
interaction
Communication
Optimizing to specific
outcomes
Decision making
Understanding
concepts & relations
Reasoning
Taking actions in the
world to achieve goals
Interaction
Computer Vision
Natural Language
Understanding & Generation
Forecasting and Operations
Research
Knowledge Graphs
and Representations
Reinforcement Learning
Answer questions about a scene
Determine if a growth is cancerous or not
Infer what happened to
characters in a story
Drive on city streets and highways
Identify objects in a scene

11. Perception: Vision and its applications
https://aidemos.microsoft.com/computer-vision
Try it your self

12. Reasoning: Knowledge Representations
https://www.connectedpapers.com/mai
n/2cf3a6a01dfaaf5b399fd0c1508690d0
d3da318b/Smartphone-Price-Prediction-
in-Retail-Industry-Using-Machine-
Learning-Techniques/graph
Try it your self

13. Reasoning : Use case Covid 19 treatment
Knowledge
Graphs
Leading
Practice

14. Communication: Natural Language U/G/P
https://corenlp.run/
Try it your self

15. Other Applications today For NLP/G/U
Abstract Summarization helps us understand your Context:

16. https://towardsdatascience.com/deep-learning-for-time-series-classification-inceptiontime-245703f422db
letting the model learn how to process time series data
on its own is a more promising solution when dealing
with unstructured noisy data. Autoregressive CNN for
Asynchronous Time Series
Decision: Time Series and its applications
https://youtu.be/U9yZIaVMa2c
Try it your self

17. Interaction: Reinforcement Learning
Reward Functions
https://youtu.be/n2gE7n11h1Y
Try it your self

18. Reinforcement
Learning:
Dynamic Treatment
Regimes
Interaction: Reinforcement
Learning and its Applications

19. Ethical AI

20. Sensing the world
Perception
Learning from every
interaction
Communication
Optimizing to specific
outcomes
Decision making
Understanding
concepts & relations
Reasoning
Taking actions in the
world to achieve goals
Interaction
Computer Vision
Natural Language
Understanding & Generation
Forecasting and Operations
Research
Knowledge Graphs
and Representations
Reinforcement Learning
Answer questions about a scene
Determine if a growth is cancerous or not
Infer what happened to
characters in a story
Drive on city streets and highways
Identify objects in a scene
Mis-identification of Threat
Dis-advantaging Groups
Promoting Hate Speech
Incrementing Market Volatility
Pedestrian Fatality -Autonomous Vehicles
What are the implications to humans?

21. Fairness Ethics Accountability Transparency
FEAT principles were created
to guide better deployment of AI
Justifiability
Accuracy & Bias
Internal &
External Outcome
Explainability
Interpretability
Align to our Ethos

22. Source: https://searchenterpriseai.techtarget.com/feature/Combating-racial-bias-in-AI

23. Government
Systems
Designer
Research
Data
Vendor
AI Model
Service
Vendor
Platform User
Environment
And it becomes more complicated...
Who is accountable now?

24. AI & Fairness
Is the data used a fair representation
of reality.
Is our model having Unintended
Consequences, Systemic Issues?

25. AI & Transparency
Strong evidence on the accuracy of
the output for high-stake decisions.
Interpretation- why model output is counter-
intuitive & do I trust it?

26. AI for Compliance
Right to an explanation if receiving
an adverse decision
Why did we decide an unpopular decision?

27. AI for Bias
Ensure that people are not being unfairly or
unknowingly excluded

28. AI for Generalization
Ensure that the right model is
used for our business objective
(Concept Drift)

29. Managing Enterprise AI
Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/getting-to-know-and-manage-your-biggest-ai-risks

30. SINGAPORE
https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/resource-for-organisation/ai/sgmodelaigovframework2.pdf

32. IEEE P7003TM Standard for Algorithmic Bias
Considerations
•IEEE P7000: Model Process for Addressing Ethical Concerns During System Design
•IEEE P7001: Transparency of Autonomous Systems
•IEEE P7002: Data Privacy Process
•IEEE P7003: Algorithmic Bias Considerations
•IEEE P7004: Standard on Child and Student Data Governance
•IEEE P7005: Standard on Employer Data Governance
•IEEE P7006: Standard on Personal Data AI Agent Working Group
•IEEE P7007: Ontological Standard for Ethically Driven Robotics and Automation
Systems
•IEEE P7008: Standard for Ethically Driven Nudging for Robotic, Intelligent and
Autonomous Systems
•IEEE P7009: Standard for Fail-Safe Design of Autonomous and Semi-Autonomous
Systems
•IEEE P7010: Wellbeing Metrics Standard for Ethical Artificial Intelligence and
Autonomous Systems
Source: https://doi.org/10.1145/3194770.3194773

33. Artificial Intelligence
& Society
Fireside Chat
LUIS F. GONZALEZ