Testing AI Systems: Quality Characteristics and Cognitive Biases

Elena Treshcheva,
Researcher, Business Development Manager, Exactpro

AI Summit New York
11 December 2019

What recommendations for AI testing are in place and how well is the technology overseen? What abilities of AI systems should be tested? What challenges does the process pose from the software testing perspective? Exactpro's new white paper – Testing the Intelligence of Your AI – is now available at https://exactpro.com/ideas/white-papers/testing-intelligence-your-ai

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Transcript

1. Build Software to Test Software
   exactpro.com
   Testing AI Systems:
   Quality Characteristics and Cognitive Biases
   Elena Treshcheva,
   Researcher, Exactpro
   

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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.
   

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3. Exactpro Client Network
   

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4. 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.
   - Ability to generalize: The ability of the system to apply to
   different and previously unseen scenarios.
   - Trustworthiness: The degree to which the system is trusted
   by stakeholders, for example a health diagnostic
   A4Q AI and Software Testing Foundation Syllabus
   https://www.gasq.org/en/exam-modules/a4q-ai-and-software-testing.html
   

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5. 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.
   

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6. 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.
   

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7. Trustworthiness:
   https://www.mas.gov.sg/news/media-releases/2019/mas-partners-financial-industry
   -to-create-framework-for-responsible-use-of-ai
   

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8. Trustworthiness:
   How can we persuade people to trust an
   algorithm? Some important techniques are:
   ● Explainability
   ● Testing
   ● Boundary conditions
   ● Gradual rollout
   ● Auditing
   ● Monitors and alarms
   https://blog.deeplearning.ai/blog/google-ai-explains-itself-neural-net-fights-bias-ai-demoralizes-champions-solar-power-heats-up
   

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9. Ability to Generalize: Scope of End-to-End and Negative Testing
   

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10. 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
    

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11. 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.
    

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12. Confirmation Bias
    

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13. AI-based Systems: Machine-Readable News
    

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14. 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
    

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15. 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 settlement date of the tradeId XXX??
    Chatbot: ???
    

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16. AI-based Systems: Algo Trading
    

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17. Congruence Bias
    Direct
    Testing
    Indirect
    Testing
    Indirect
    Testing
    

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18. 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.
    

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19. Law of Triviality (the Bike-Shed Effect)
    

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20. AI-based Systems: Pricing Calculator
    

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21. Automation Bias
    

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22. AI-based Systems: Fraud Detection and Market Surveillance
    

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23. Build Software to Test Software
    Click to know more about
    Exactpro Test Tools
    

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24. Zero-Risk Bias
    

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25. Non-deterministic Systems: Financial Market Infrastructures
    

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26. 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.
    

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27. 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
    

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28. 28 Build Software to Test Software exactpro.com
    CONTACT:
    info@exactpro.com
    London: +44 (0) 203 319 1644
    Thank you
    https://exactpro.com/ideas/white-papers/testing-intelligence-your-ai
    

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