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Testing the Intelligence of your AI
Iosif Itkin, CEO and co-founder
Elena Treshcheva, Researcher
Iosif Itkin, CEO and co-founder
Elena Treshcheva, Business Development Manager and Researcher
Exactpro Overview
● A specialist firm focused on functional and non-functional testing of
exchanges, clearing houses, depositories, trade repositories and other
financial market infrastructures.
● Incorporated in 2009 with 10 people, our company has experienced
significant growth and is now employing over 550 specialists.
● We were part of the London Stock Exchange Group (LSEG) from May 2015
till January 2018. Exactpro management buyout from LSEG was successfully
completed in January 2018. We are headquartered in the UK and have
operations in the US, Georgia and Russia.
Exactpro Client Network
AI-based Systems in Finance
Machine Learning in financial organizations:
- already passed an initial development phase
- the usage of live ML applications is about to
dramatically increase over the next three years
https://www.bankofengland.co.uk/-
/media/boe/files/report/2019/machine-learning-in-uk-
financial-services.pdf
AI-based Systems in Finance
Machine Learning in financial organizations:
- already passed an initial development phase
- the usage of live ML applications is about to
dramatically increase over the next three years
● Market Surveillance Systems
● Conversational Assistants
● Algo Trading Systems
● Pricing Calculators
● Machine Readable News
● Insurance Claims
https://www.bankofengland.co.uk/-
/media/boe/files/report/2019/machine-learning-in-uk-
financial-services.pdf
AI-based Systems’ Quality Characteristics:
- Ability to learn: The capacity of the system to learn from use for the
system itself, or data and events it is exposed to.
- Trustworthiness: The degree to which the system is trusted by
stakeholders, for example a health diagnostic
- Ability to generalize: The ability of the system to apply to different
and previously unseen scenarios.
A4Q AI and Software Testing
Foundation
Syllabus https://www.gasq.org/en/exam-modules/a4q-ai-and-software-testing.html
Testing the
Intelligence
of your AI
Ability to Learn:
https://www.deeplearning.ai/
• Training set — Which you run your learning algorithm on.
• Development set — Which you use to tune parameters, select
features, and make other decisions regarding the learning algorithm.
Sometimes also called the hold-out cross validation set.
• Test set — which you use to evaluate the performance of the algorithm,
but not to make any decisions regarding what learning algorithm or
parameters to use.
Trustworthiness:
https://innovation.defense.gov/ai/
During the DIB’s quarterly public meeting on October 31, 2019, the DIB
members voted to approve the proposed AI Principles.
Trustworthiness:
https://www.mas.gov.sg/news/media-releases/2019/mas-partners-financial-
industry-to-create-framework-for-responsible-use-of-ai
Ability to Generalize: Scope of End-to-End and Negative Testing
Congruence bias
Confirmation
bias
Law of triviality
Zero-risk bias
Anthropocentric
thinking
Illusion of control
Cognitive Biases Affecting Software Testing of AI-based Systems
Automation bias
AI-based Systems: Machine-Readable News
Confirmation Bias
Salman, I. (2016). Cognitive biases in software quality and testing. Proceedings of
the 38th International Conference on Software Engineering Companion - ICSE ’16.
Pp. 823-826.
Mohanani, R., Salman, I., Turhan, B., Rodríguez, P., & Ralph, P. (2018).
Cognitive Biases in Software Engineering: A Systematic Mapping Study.
IEEE Transactions on Software Engineering
AI-based Systems: Conversational Assistants (Chatbots)
Chatbot
Anthropocentric Bias
We should not
humanize computers.
Anthropocentric bias
They dislike it a lot!
Anthropocentric Bias: Testing a Mine-Defusing Robot
Anthropocentric Bias: Why We Treat Robots Like Humans
Darling, Kate and Nandy, Palash and Breazeal,
Cynthia “Empathic Concern and the Effect of
Stories in Human-Robot Interaction” (2015).
Proceedings of the IEEE International Workshop on
Robot and Human Communication (ROMAN),
2015. 6 p.
https://www.ted.com/talks/kate_darling_why_we_ha
ve_an_emotional_connection_to_robots
Anthropocentric Bias: Testing Chatbots
Anaphora / Context
Human: I bought 500 Company X shares two years ago. The stocks’
cost was 60,000 USD. What’s their today’s cost?
Chatbot: What currency would you like to have for the rate? X
Spelling / overall correctness
Human: What is the setlement date of the tradeId XXX??
Chatbot: ???
AI-based Systems: Algo Trading
Congruence Bias
Direct
Testing
Indirect Testing Methods
Information
extraction and
Machine learning
End-to-End
Automated Test
Library
Whatever it
takes!
Test execution
data and log
analysis
Passive Testing
Whatever it
takes!
Applications of the Proposed Approach
https://unsplash.com/search/photos/san-francisco
The First IEEE International Conference on Artificial
Intelligence Testing (IEEE AITest 2019), April 4-9 2019,
San Francisco East Bay, CA, USA
User-Assisted Log Analysis for Quality
Control of Distributed Fintech Systems
Iosif Itkin, Anna Gromova, Anton Sitnikov, Rostislav Yavorskiy,
Evgenii Tsymbalov, Andrey Novikov and Kirill Rudakov.
AI-based Systems: Pricing Calculator
Law of Triviality (the Bike-Shed Effect)
Automation Bias
AI-based Systems: Fraud Detection and Market Surveillance
Build Software to Test Software
Click to know more about
Exactpro Test Tools
AI-based Systems: Insurance Claims
Zero-Risk Bias
Non-deterministic Systems: Financial Market Infrastructures
The Illusion of Control and Happiness
Sherman, G. D., Lee, J. J., Cuddy, A. J. C., Renshon, J., Oveis, C., Gross, J. J., &
Lerner, J. S. (2012). Leadership is associated with lower levels of stress.
Proceedings of the National Academy of Sciences, 109(44), 17903–17907.
Fenton-O’Creevy, M., Nicholson, N., Soane, E.,
& Willman, P. (2003). “Trading on illusions:
Unrealistic perceptions of control and trading
performance”. Journal of Occupational and
Organizational Psychology, 76(1), 53–68.
The Illusion of Control and Performance
Thank you

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Testing the Intelligence of your AI

  • 1. Testing the Intelligence of your AI Iosif Itkin, CEO and co-founder Elena Treshcheva, Researcher Iosif Itkin, CEO and co-founder Elena Treshcheva, Business Development Manager and Researcher
  • 2. Exactpro Overview ● A specialist firm focused on functional and non-functional testing of exchanges, clearing houses, depositories, trade repositories and other financial market infrastructures. ● Incorporated in 2009 with 10 people, our company has experienced significant growth and is now employing over 550 specialists. ● We were part of the London Stock Exchange Group (LSEG) from May 2015 till January 2018. Exactpro management buyout from LSEG was successfully completed in January 2018. We are headquartered in the UK and have operations in the US, Georgia and Russia.
  • 4. AI-based Systems in Finance Machine Learning in financial organizations: - already passed an initial development phase - the usage of live ML applications is about to dramatically increase over the next three years https://www.bankofengland.co.uk/- /media/boe/files/report/2019/machine-learning-in-uk- financial-services.pdf
  • 5. AI-based Systems in Finance Machine Learning in financial organizations: - already passed an initial development phase - the usage of live ML applications is about to dramatically increase over the next three years ● Market Surveillance Systems ● Conversational Assistants ● Algo Trading Systems ● Pricing Calculators ● Machine Readable News ● Insurance Claims https://www.bankofengland.co.uk/- /media/boe/files/report/2019/machine-learning-in-uk- financial-services.pdf
  • 6. AI-based Systems’ Quality Characteristics: - Ability to learn: The capacity of the system to learn from use for the system itself, or data and events it is exposed to. - Trustworthiness: The degree to which the system is trusted by stakeholders, for example a health diagnostic - Ability to generalize: The ability of the system to apply to different and previously unseen scenarios. A4Q AI and Software Testing Foundation Syllabus https://www.gasq.org/en/exam-modules/a4q-ai-and-software-testing.html Testing the Intelligence of your AI
  • 7. Ability to Learn: https://www.deeplearning.ai/ • Training set — Which you run your learning algorithm on. • Development set — Which you use to tune parameters, select features, and make other decisions regarding the learning algorithm. Sometimes also called the hold-out cross validation set. • Test set — which you use to evaluate the performance of the algorithm, but not to make any decisions regarding what learning algorithm or parameters to use.
  • 8. Trustworthiness: https://innovation.defense.gov/ai/ During the DIB’s quarterly public meeting on October 31, 2019, the DIB members voted to approve the proposed AI Principles.
  • 10. Ability to Generalize: Scope of End-to-End and Negative Testing
  • 11. Congruence bias Confirmation bias Law of triviality Zero-risk bias Anthropocentric thinking Illusion of control Cognitive Biases Affecting Software Testing of AI-based Systems Automation bias
  • 14. Salman, I. (2016). Cognitive biases in software quality and testing. Proceedings of the 38th International Conference on Software Engineering Companion - ICSE ’16. Pp. 823-826.
  • 15. Mohanani, R., Salman, I., Turhan, B., Rodríguez, P., & Ralph, P. (2018). Cognitive Biases in Software Engineering: A Systematic Mapping Study. IEEE Transactions on Software Engineering
  • 16. AI-based Systems: Conversational Assistants (Chatbots) Chatbot
  • 17. Anthropocentric Bias We should not humanize computers.
  • 19. Anthropocentric Bias: Testing a Mine-Defusing Robot
  • 20. Anthropocentric Bias: Why We Treat Robots Like Humans Darling, Kate and Nandy, Palash and Breazeal, Cynthia “Empathic Concern and the Effect of Stories in Human-Robot Interaction” (2015). Proceedings of the IEEE International Workshop on Robot and Human Communication (ROMAN), 2015. 6 p. https://www.ted.com/talks/kate_darling_why_we_ha ve_an_emotional_connection_to_robots
  • 21. Anthropocentric Bias: Testing Chatbots Anaphora / Context Human: I bought 500 Company X shares two years ago. The stocks’ cost was 60,000 USD. What’s their today’s cost? Chatbot: What currency would you like to have for the rate? X Spelling / overall correctness Human: What is the setlement date of the tradeId XXX?? Chatbot: ???
  • 24. Indirect Testing Methods Information extraction and Machine learning End-to-End Automated Test Library Whatever it takes! Test execution data and log analysis Passive Testing Whatever it takes!
  • 25. Applications of the Proposed Approach https://unsplash.com/search/photos/san-francisco The First IEEE International Conference on Artificial Intelligence Testing (IEEE AITest 2019), April 4-9 2019, San Francisco East Bay, CA, USA User-Assisted Log Analysis for Quality Control of Distributed Fintech Systems Iosif Itkin, Anna Gromova, Anton Sitnikov, Rostislav Yavorskiy, Evgenii Tsymbalov, Andrey Novikov and Kirill Rudakov.
  • 27. Law of Triviality (the Bike-Shed Effect)
  • 29. AI-based Systems: Fraud Detection and Market Surveillance
  • 30. Build Software to Test Software Click to know more about Exactpro Test Tools
  • 33. Non-deterministic Systems: Financial Market Infrastructures
  • 34. The Illusion of Control and Happiness Sherman, G. D., Lee, J. J., Cuddy, A. J. C., Renshon, J., Oveis, C., Gross, J. J., & Lerner, J. S. (2012). Leadership is associated with lower levels of stress. Proceedings of the National Academy of Sciences, 109(44), 17903–17907.
  • 35. Fenton-O’Creevy, M., Nicholson, N., Soane, E., & Willman, P. (2003). “Trading on illusions: Unrealistic perceptions of control and trading performance”. Journal of Occupational and Organizational Psychology, 76(1), 53–68. The Illusion of Control and Performance