AI Policy Highlights &Global Media Monitoring

Marko Grobelnik (Marko.Grobelnik@ijs.si)

Digital Champion of Slovenia at EC

Artificial Intelligence Lab, Jozef Stefan Institute (http://ailab.ijs.si/)

UNESCO International Research Centre on Artificial Intelligence (https://ircai.org/)

Applied Artificial Intelligence Conference 2020

Evolution of AI per countryOECD AI Policy Observatory (https://oecd.ai/)

Observing how AI evolved through time and geography in the number of research publications(http://bit.ly/OecdAiObservatory-Research)

The name of the game.

To build an “AI Policy” we need first an operational definition of AI• “AI System” as defined by OECD could serve as a good start

OECD definition of an “AI System”:• An AI system is a machine-based system that can, for a given set of human-

defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.

• It does so by utilising machine and/or human-based inputs to: • i) perceive and/or analyse real and/or virtual environments; • ii) abstract such perceptions/analyses into models manually or automatically; and • iii) use model interpretations to formulate options for outcomes.

• AI systems are designed to operate with varying levels of autonomy.

Informal definition of AI

•AI is exactly the opposite from what is happening in the video…

•…instead of living beings mimicking machines, AI is supposed to make machines imitating living beings.

AI System

Environment State• An environment has its state, not

necessarily fully observable• An environment can change its

state with or without explicit actions by the AI System

Environment State Observability• Environments are real (physical,

social, mental) or artificial (e.g., games like chess)

• Real environments are typically too complex to be observable in its entirety (like physical or social)

• Artificial environments can be fully observable (like chess)

• Environments are observable through ‘percepts’ in a form of raw data

Environment

Perceiving(Percepts / Raw Data)

Sensors: (§1)• Machine (§2) • Human (§3)

Actuators: (§13)• Machine (§14)• Human (§15) Acting

(Physical or Informational Influence)

Model (§8)(e.g., rules or analytical function)

Model Construction Algorithm (§4)(e.g., machine learning)

Structured Data

Recommendations

Model Interpretation Algorithm (§10)(e.g., classification or logic reasoning)

Processed by Machine

Processed by Human

Ob

ject

ive

(§5

)

Human Interpretable or UninterpretableRepresentation (§9)

Objective (§11)

His

tori

cal d

ata

/ m

emo

ry (

§7)

AI Operational Logic

Performance Measure (§12)

Perf

orm

ance

M

easu

re (

§6)

AI System as defined by OECD – Graphical representation

AI System

Environment State• An environment has its state, not

necessarily fully observable• An environment can change its

state with or without explicit actions by the AI System

Environment State Observability• Environments are real (physical,

social, mental) or artificial (e.g., games like chess)

• Real environments are typically too complex to be observable in its entirety (like physical or social)

• Artificial environments can be fully observable (like chess)

• Environments are observable through ‘percepts’ in a form of raw data

Environment

Perceiving(Percepts / Raw Data)

Sensors: (§1)• Machine (§2) • Human (§3)

Actuators: (§13)• Machine (§14)• Human (§15) Acting

(Physical or Informational Influence)

Model (§8)(e.g., rules or analytical function)

Model Construction Algorithm (§4)(e.g., machine learning)

Structured Data

Recommendations

Model Interpretation Algorithm (§10)(e.g., classification or logic reasoning)

Processed by Machine

Processed by Human

Ob

ject

ive

(§5

)

Human Interpretable or UninterpretableRepresentation (§9)

Objective (§11)

His

tori

cal d

ata

/ m

emo

ry (

§7)

AI Operational Logic

Performance Measure (§12)

Perf

orm

ance

M

easu

re (

§6)

AI System as defined by OECD

AI System

Environment State• An environment has its state, not

necessarily fully observable• An environment can change its

state with or without explicit actions by the AI System

Environment State Observability• Environments are real (physical,

social, mental) or artificial (e.g., games like chess)

• Real environments are typically too complex to be observable in its entirety (like physical or social)

• Artificial environments can be fully observable (like chess)

• Environments are observable through ‘percepts’ in a form of raw data

Environment

Perceiving(Percepts / Raw Data)

Sensors: (§1)• Machine (§2) • Human (§3)

Actuators: (§13)• Machine (§14)• Human (§15) Acting

(Physical or Informational Influence)

Model (§8)(e.g., rules or analytical function)

Model Construction Algorithm (§4)(e.g., machine learning)

Structured Data

Recommendations

Model Interpretation Algorithm (§10)(e.g., classification or logic reasoning)

Processed by Machine

Processed by Human

Ob

ject

ive

(§5

)

Human Interpretable or UninterpretableRepresentation (§9)

Objective (§11)

His

tori

cal d

ata

/ m

emo

ry (

§7)

AI Operational Logic

Performance Measure (§12)

Perf

orm

ance

M

easu

re (

§6)

AI System as defined by OECD

AI System

Environment State• An environment has its state, not

necessarily fully observable• An environment can change its

state with or without explicit actions by the AI System

Environment State Observability• Environments are real (physical,

social, mental) or artificial (e.g., games like chess)

• Real environments are typically too complex to be observable in its entirety (like physical or social)

• Artificial environments can be fully observable (like chess)

• Environments are observable through ‘percepts’ in a form of raw data

Environment

Perceiving(Percepts / Raw Data)

Sensors: (§1)• Machine (§2) • Human (§3)

Actuators: (§13)• Machine (§14)• Human (§15) Acting

(Physical or Informational Influence)

Model (§8)(e.g., rules or analytical function)

Model Construction Algorithm (§4)(e.g., machine learning)

Structured Data

Recommendations

Model Interpretation Algorithm (§10)(e.g., classification or logic reasoning)

Processed by Machine

Processed by Human

Ob

ject

ive

(§5

)

Human Interpretable or UninterpretableRepresentation (§9)

Objective (§11)

His

tori

cal d

ata

/ m

emo

ry (

§7)

AI Operational Logic

Performance Measure (§12)

Perf

orm

ance

M

easu

re (

§6)

AI System as defined by OECD

Accountability and responsibility

Risk Management

Human-centred values

Transparency Robustness

Building human capacity and preparing for job transition

AI System and relation to higher level policy principles

Perception Bias

Activation Bias

Technical Bias

AI System and sources of various types of biases

Where AI Policies are being discussed?• UN – general principles, including AI certification

• UN Secretary-General’s High-level Panel on Digital Cooperation• https://www.un.org/en/pdfs/DigitalCooperation-report-for%20web.pdf

• UNESCO – focus on ethics• Ad Hoc Expert Group (AHEG) for the preparation of a draft text of a recommendation on the

ethics of artificial intelligence• https://en.unesco.org/artificial-intelligence/ethics

• Council of Europe – focus on human rights (will become legally binding)• Ad hoc Committee on Artificial Intelligence (CAHAI)• https://www.coe.int/en/web/artificial-intelligence/cahai

• OECD – focus on economy• OECD Network of Experts on AI (ONE AI)• https://oecd.ai/network-of-experts

• EU – focus on holistic regulation (will become legally binding)• White Paper on Artificial Intelligence: a European approach to excellence and trust• https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-

approach-excellence-and-trust_en

Automating Situational Awareness

with the “Event Registry” systemMarko Grobelnik, Gregor Leban, Mitja Jermol

Artificial Intelligence Lab, Jozef Stefan Institute / EventRegistry.org

Ljubljana, Slovenia

Marko.Grobelnik@ijs.si

What are we presenting? (quick summary)

• Complexity, scale and speed of the global social dynamics is too demanding for general human consumption and comprehension

• We are presenting a system: • (a) to observe global events in real-time• (b) to consume information across the world languages and • (c) to provide a structured view on how global events are interrelated

• The goal is to help automating “situational awareness” scenarios

• Current work includes: • (a) what-if simulations

• (b) predicting likely future events

• (c) predicting consequences of events

• The system is available at http://EventRegistry.org/

Why AI could help? (a philosophical note)

As long as there is some order in the data, AI can extract the signal

Understanding of the global social dynamics is based on the history and the task of AI is to find its ‘rhymes’

What kind of questions do we need to answer?

• Understanding a history of last years/decade• How events in the past were interconnected? How the history was

structured?

• Root-cause analysis• What were likely causes leading to a particular event?

• Event prediction• Given a current situation, what are likely events to happen in the future?

• Simulating possible future scenarios by What-If questions• What are likely future developments if an event would happen?

• Anomaly detection (detecting ‘unknown knows’)• What are surprising, possibly unnoticed, phenomena spotted in data?

“Event Registry” systemhttp://eventregistry.org/A system for real-time, cross-lingual, global media monitoring…

…to make the complexity of global social dynamics manageable

Key features of the “Event Registry” system

• …to collect data from global media in real-time

• …to identify and interconnect global events

• …to track evolving topics in a structured way

• …to extract information across world languages

• …to provide rich exploratory visualizations

• …to provide human understandable explanations

In development:

• …to identify causal relationships in evolving global topics

• …to predict events or likely future developments

• …to detect anomalies for early warnings

Data collection

• The system collects data from the Web (open and closed sources):• Core data comes from ~50,000 main stream and social media sources

• The collected data has a long-tail coverage• …from micro local events (from anywhere in the world) to the top global events

• Collection in real-time with a few minute delay from the time when a story appears

• The systems collects ~500,000 news items per day

• The data collection system available at http://newsfeed.ijs.si/

Events as a core of the global structured data

• The “Event Registry” system organizes global news into ‘events’• …where an event is a collection of reports on the same topic

• Each event is a structure along the following dimensions:• Topical dimension – What is being talked about? • Social dimension – Who is actor of the event?• Temporal aspect – When an event happened?• Geographical aspect – Where an event is taking place?• Publisher aspect – Who is reporting? • Sentiment/bias aspect – What are associated emotional signals?• Context – What other events are related? What is the storyline?

Example analysis of ‘Coronavirus’coverage from the time when acting was easier

“Event Registry” collected from Dec 28th

2019 to Jan 28th 2020:

• 75,416 articles in ~50 world languages

• Articles were compressed into 1814 interrelated events

When?

Media?Topics? Organizations?

People?

Sentiment?Places?

Languages?Event with 10,124 articles

The anatomy of a ‘Coronavirus’ event from Jan 28th 2020

Where ‘Coronavirus’ related events are happening?

European events

Chineese eventsGlobal events

US eventsVisualizing locations of ‘Coronavirus’ related events

Structure of connected topics related to ‘Coronavirus’

Visualization of the network of the most relevant connected concepts related to ‘Coronavirus’

Classification of ‘Coronavirus’ events

Visualization of the 1814 ‘Coronavirus’ events classified into predefined topics into three level topic taxonomy

Contextualized storyline of ‘Coronavirus’ events on the timeline

Demonstration of the “Event Registry” system

• The demoable version of the system is available at• http://eventregistry.org/

• The development version (for integration with other systems):• https://github.com/EventRegistry/event-registry-python

• Video of the “Event Registry” presentation:• http://videolectures.net/oercongress2017_leban_event_registry/

Summary

• “Event Registry” system allows (semi)automating the key “situational awareness” stages:• ‘Perception’ (i.e., real-time cross-lingual

global data collection) is enabled by the existing infrastructure

• ‘Comprehension’ (i.e., understanding of global social dynamics) is enabled by numerous functions in the system

• ‘Projection’ (i.e., automatic forecasting) is in the prototype stage

CoronavirusWatch(http://coronaviruswatch.ircai.org/)• Web portal to observe evolution of coronavirus in media and through numbers