Formation au testing des Modèles

Formation au testing des modèles de machine learning, animée par Jean-Marie John-Mathews, co-fondateur de Giskard.

Transcript

1. Quality Assurance for AI
   Etalab - Programme 10%
   

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2. Quality is essential to all industries
   2
   🏦 Finance
   Audit
   🍒 Food
   Nutri-score
   🏭 Manufacturing
   ISO 9000
   💾 Software
   Test-Driven Development
   

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3. But the AI industry is struggling with incidents
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   ⚖ Ethics 🏦 Business 🛡 Security
   

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4. 4
   The shift:
   AI leaders & regulators
   are pushing for quality
   In the end, I only trust thorough testing.
   You can try to explain to me why a jet
   engine is reliable. But in the end, trust will
   come from looking at crash statistics.
   Yann LeCun, Chief AI Scientist
   Invented Convolutional Neural Networks
   “
   Providers of high-risk AI systems shall put a
   quality management system to ensure
   compliance.
   Article 17, European AI Act
   Proposed in 2021 for approval in 2023
   “
   

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5. AI quality is correlated with revenue
   6%
   Share of sales revenue
   AI quality penalty risk in the
   upcoming 󰎾 AI regulation
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6. Quality cures growing pains of AI teams
   6
   📔
   Prototype
   ⚙
   Production
   🚀
   Deployment
   Data Scientists
   spend months going back
   and forth with business
   stakeholders to validate
   model quality
   3-6 months
   to evaluate an AI prototype
   ML Engineers
   become firefighters after
   users report bugs which
   overwhelm Data Scientists
   with maintenance
   1,342
   AI incidents reported by
   the AI Incident DB
   ML Engineers
   struggle to implement AI
   model tests which cover all
   quality criteria and test
   cases
   87%
   of AI models never make it
   into production
   Sources: VentureBeat, AI Incident Database
   

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7. AI quality brings unique technical challenges
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   ML quality ≠
   data quality
   ✖ Clean data and bad
   model
   ✖ Errors are caused by
   data that did not exist
   when the model was
   created
   ML testing ≠
   software testing
   ✖ ML cannot be broken
   down into unit
   components
   ✖ Probabilistic
   ✖ Constant updating
   Quality Criteria
   to consider
   ✖ Business: performance,
   robustness
   ✖ Tech: security, reliability
   ✖ Ethics: fairness, privacy
   ✖ Environment: carbon
   impact, efficiency
   

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8. Behavioral Testing for NLP (Ribeiro et al., 2020)
   

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9. “Capabilities”, a solution to address NLP Testing
   Capabilities
   ✓ Vocabulary (important words or word
   types for the task)
   ✓ Taxonomy (synonyms, antonyms, etc)
   ✓ Robustness (to typos, irrelevant
   changes, etc)
   ✓ NER (appropriately understanding
   named entities)
   ✓ Fairness
   ✓ Temporal (understanding order of
   events)
   ✓ Negation
   ✓ Logic (ability to handle symmetry,
   consistency, and conjunctions)
   

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10. How to generate capabilities tests?
    Test types
    1. Minimum Functionality
    - Simple test cases designed to target a
    specific behavior
    - Ex: When “extraordinary” is in the
    sentence, sentiment should be positive
    2. Invariance
    - Perturbations that should not change the
    output of the model
    - Ex: Changing location name, typos
    3. Directional Expectation
    - Perturbations to the input with known
    expected results
    - Ex: adding negative phrases and checking
    that sentiment polarity does not increase
    10
    Test cases generation
    ✓ Templates help to generate edge cases
    - Ex: I {NEGATION} {POS_VERB} the
    {THING}.
    where
    - {NEGATION} = {didn’t, can’t say I, ...}
    - {POS_VERB} = {love, like, ...},
    - {THING} = {food, flight, service, ...}
    ✓ Smart templates with suggestions:
    

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11. Interactive session
    

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12. No ML bias in production
    Deliver ML products, better & faster
    Website | GitHub | Email
    

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