The document discusses the need for AI technology certification to increase trust and regulate AI systems. It proposes a two-level certification approach that evaluates whether an AI system performs as claimed and complies with ethical standards. The document also describes how to implement certification, including agreeing on certifiable AI system classes, standardizing performance metrics, and building prototypes to automatically verify systems. The goal is to take the first technical steps towards establishing an operational certification process for AI technologies.
Towards an operational AI Technology CertificationMarko Grobelnik
Why AI Certification is needed?
There seems to be a global consensus on AI is influencing our personal and societal lives
AI Technology is for most of the people a mystery and creates fears
AI Technology is for its creators also in many ways a mystery, not really understanding all the relevant consequences of its use
…therefore it is necessary to regulate AI Technology to remove fears and to make it trustful
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
Towards an operational AI Technology CertificationMarko Grobelnik
Why AI Certification is needed?
There seems to be a global consensus on AI is influencing our personal and societal lives
AI Technology is for most of the people a mystery and creates fears
AI Technology is for its creators also in many ways a mystery, not really understanding all the relevant consequences of its use
…therefore it is necessary to regulate AI Technology to remove fears and to make it trustful
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
This presentation is an introduction to artificial intelligence: expert systems components. Topics covered are the following: defining artificial intelligence; expert systems key terms; expert systems requirements; expert systems components; and selecting appropriate problems for expert systems.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
Artificial Intelligence for Business - Version 2Nicola Mattina
This presentation is part of a workshop that will help you understand artificial intelligence tools and how they can be employed across your organization.
Lectures and activities are customized considering the background of the participants to highlight the use of artificial intelligence in a specific industry and in three different areas: product development, customer care, business operations.
Workshop structure
120’ lectures
2 activities to apply the concepts
1 practical toolkit
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
SmartData Webinar: Commercial Cognitive Computing -- How to choose and build ...DATAVERSITY
In the next five years, consumers and businesses will begin to demand more intelligence from the applications they use as they are exposed to smarter, more personalized systems in a variety of industries. Ranging from natural language tools to interact more naturally with users, to machine learning algorithms that discover untapped patterns and relationships in big data, the potential for these technologies is great but most firms don't have a roadmap for building their first cognitive computing solution. This webinar will help participants discover:
- What is cognitive computing(CC), and what can it do for my business?
- Which of my current applications would benefit from CC technologies?
- What new applications could we develop to disrupt our industry using CC?
- How do we know which CC vendors, products and services are really ready for prime-time?
- What are our competitors doing about it?
- How do we get started?
The presentation looks at the following: 1) Long range view of fundamental trends and shifts in computing and User Experience, 2)
What does IoT and context mean for ambient conversational AI?, 3)
How does Conversational AI work?
Self-Learning: Implicit and explicit customer feedback based learning.
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
This presentation is an introduction to artificial intelligence: expert systems components. Topics covered are the following: defining artificial intelligence; expert systems key terms; expert systems requirements; expert systems components; and selecting appropriate problems for expert systems.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
Artificial Intelligence for Business - Version 2Nicola Mattina
This presentation is part of a workshop that will help you understand artificial intelligence tools and how they can be employed across your organization.
Lectures and activities are customized considering the background of the participants to highlight the use of artificial intelligence in a specific industry and in three different areas: product development, customer care, business operations.
Workshop structure
120’ lectures
2 activities to apply the concepts
1 practical toolkit
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
SmartData Webinar: Commercial Cognitive Computing -- How to choose and build ...DATAVERSITY
In the next five years, consumers and businesses will begin to demand more intelligence from the applications they use as they are exposed to smarter, more personalized systems in a variety of industries. Ranging from natural language tools to interact more naturally with users, to machine learning algorithms that discover untapped patterns and relationships in big data, the potential for these technologies is great but most firms don't have a roadmap for building their first cognitive computing solution. This webinar will help participants discover:
- What is cognitive computing(CC), and what can it do for my business?
- Which of my current applications would benefit from CC technologies?
- What new applications could we develop to disrupt our industry using CC?
- How do we know which CC vendors, products and services are really ready for prime-time?
- What are our competitors doing about it?
- How do we get started?
The presentation looks at the following: 1) Long range view of fundamental trends and shifts in computing and User Experience, 2)
What does IoT and context mean for ambient conversational AI?, 3)
How does Conversational AI work?
Self-Learning: Implicit and explicit customer feedback based learning.
Impact of Technology on Profession: Human Vs. AI + BotVinod Kashyap
Innovations in technologies in auditing and assurance profession and its impact on the auditing profession which included Artificial Intelligence (AI), Robotic Process Automation (RPA), Audit Data Standards (ADS), Intelligent Process Automation (IPA) and Blockchain and Distributed Ledger Technologies (DLT) Systems.
Metaverse is the converged world of physical world and virtual world that is hyper-connected, hyper-visualized, hyper-interacted, and hyper-reality enabled. Metaverse is a collection of fully connected interoperable physically augmented digital worlds with physical persistence that are converged with the virtually augmented physical world in which people and digital representations of people (digital people) can fully interact with one another and digital objects/environments (including digital twins) with full reality. Patents are a good information resource for obtaining the state of the art of metaverse technology innovation insights. As a metaverse technology innovation application, US20210004076 illustrates an AI innovation platform that provides a virtual AI development and testing environment.
Language as social sensor - Marko Grobelnik - Dubrovnik - HrTAL2016 - 30 Sep ...Marko Grobelnik
At the HrTAL2016 conference I presented the talk on "Language as a Social Sensor to operate with Knowledge". The talk included a section on language as an interface between physical nature and the world of human mind and human society. The role of language as a 'sensor'has several consequences in uncertainties and inexactness of the language evolution, as we know it. The talk was accompanies with several live demonstrations of the systems on semantic annotation (wikifier.org) and media monitoring (eventregistry.org).
Global Media Monitoring presented through several systems for collecting, extracting and enriching data, forming and exploring events across languages in real-time - ...resulting in the system Event Registry (http://eventregistry.org/)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Towards an operational AI Technology Certification
1. Towards an operational
AI Technology Certification
Marko Grobelnik
(Marko.Grobelnik@ijs.si)
Artificial Intelligence Lab, Jozef Stefan Institute
Digital Champion of Slovenia at EC
UN Legal Week, UN Hq, New York, Oct 28th 2019
2. Three key questions: Why, What and How?
•Why AI Certification is needed?
•What AI Certification should include?
•How AI Certification could be implemented?
3. Why AI Certification is needed?
There seems to be a global consensus on AI is
influencing our personal and societal lives
• AI Technology is for most of the people a mystery
and creates fears
• AI Technology is for its creators also in many ways
a mystery, not really understanding all the
relevant consequences of its use
…therefore it is necessary to regulate AI Technology
to remove fears and to make it trustful
4. What AI Certification should include?
Two levels of certification needed:
(1) Is the AI System actually doing what its creators are claiming?
• …what kind of guarantees and uncertainties does it have?
• E.g., performance, robustness, transparency, and others
(2) Is the AI System compatible with a particular value standard?
• …like Ethical, Social and Legal norms, regulations, principles, criteria
• E.g., UN Human Rights Declaration, OECD AI Principles, EC Ethics
guidelines for trustworthy AI and others
5. How AI Certification could be implemented?
Where to start?
• We need an operational AI definition
• e.g., “AI System” as defined by OECD could serve as a good start
• Check how science developed standards to verify its claims
• …preventing scientists to lie and mislead themselves
• AI Systems need a standardized API backdoor for testing and monitoring
• …allowing an operational technical certification
6. 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.
7. 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
Objective(§5)
Human Interpretable
or Uninterpretable
Representation (§9)
Objective (§11)
Historicaldata/
memory(§7)
AI Operational Logic
Performance
Measure (§12)
Performance
Measure(§6)
AI System as defined by OECD
8. 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
Objective(§5)
Human Interpretable
or Uninterpretable
Representation (§9)
Objective (§11)
Historicaldata/
memory(§7)
AI Operational Logic
Performance
Measure (§12)
Performance
Measure(§6)
AI System as defined by OECD
9. 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
Objective(§5)
Human Interpretable
or Uninterpretable
Representation (§9)
Objective (§11)
Historicaldata/
memory(§7)
AI Operational Logic
Performance
Measure (§12)
Performance
Measure(§6)
AI System as defined by OECD
10. 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
Objective(§5)
Human Interpretable
or Uninterpretable
Representation (§9)
Objective (§11)
Historicaldata/
memory(§7)
AI Operational Logic
Performance
Measure (§12)
Performance
Measure(§6)
AI System as defined by OECD
13. Modes of certification of an AI System
• Methodological review (manual)
• …detecting possible flaws in construction of a system
• Offline testing review (semi automatic)
• …simulating real-live usage to test response under varying conditions
• Online monitoring review (automatic)
• …real-live observation to monitor behavior
• Review reports expressed in a form of measurable KPIs
• …many of the KPIs are already well established in scientific community
• …some KPIs might need further elaboration to measure relevant aspects (e.g., in
particular in relation to soft concepts like Human Rights)
14. …so, what are the first technical steps to
get an operational AI System certification
1. Agree on classes of AI Systems which are possible to certify
• …some types of systems might be beyond the scope of certification
2. Define and agree on hard and soft KPIs describing an AI System
• …some KPIs could be taken from research, some need development
3. Standardize backdoor API for testing and monitoring
• …enabler to observe AI Systems in action
4. Build a prototype certification system to allow (semi)automatic
verification of AI Systems
• …useful AI certification system could be constructed without a big investment
5. Get a series of success stories to gain trust on certification