The adoption of machine-learning (a form of artificial intelligence) by lawyers is shaping the future of law –as a profession and institution. Today, pockets of practice and traditional workflows have undergone massive transformation, viz. legal research and document reviews in discovery, regulatory disclosures, M&A, and contract management. Many alternative legal services providers, “legaltech” vendors, and other “NewLaw” businesses were born in the 2010s. Yet, truly full-service “robo-lawyers” are nowhere in sight. This seminar will examine –from a legaltech industry perspective– the technological influences on emerging industry trends, including the applications of analytics and natural language processing. The discussion will focus on how software encodes the law and legal processes in software, the related technological and ethical challenges, and the adoption barriers in legal practice.

1. Machine Learning and the Challenges
of Digital Transformation in the Law
Presentation by Sebastian Ko
Webinar for CFRED, CUHK Faculty of Law
May 29, 2020

2. Speaker
Sebastian Ko, lawtech entrepreneur
• Led Asian eDiscovery review business for global ALSP
(PE-funded, ex NASDAQ), and led international corporate projects.
• Legal experiences in private practice (financial services regulation, dispute resolution)
and as in-house regional counsel.
• Past research includes probabilistic reasoning in law (joint Science & Law program).
• Chairman of the InnoTech Law Hub (WP), Law Society of Hong Kong.
• Member of the MIT Technology Review Global Panel.
• Int. Assoc. for Contract and Commercial Management, Council Member (Asia)
• Qualified as Hong Kong solicitor and U.S. attorney.
hk.linkedin.com/in/sebko
Sebastian Ko 2020 ©
All opinions are my own and in no way representative of any of my affiliations.
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3. Framing questions for this presentation
• How should legal services develop to
benefit from A.I.?
• How is A.I. applied in the law?
• Industry & technology trends
• Building blocks, drivers & barriers
• Implications for:
• Lawyers and law students
• Clients and the public
• Tech providers
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4. Global headlines
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5. Recent trends: Tech in business
Supply
• Availability of key technologies
• Computational power (integration of
1000s or 1Ms of data points)
• Self-learning machines (specific tasks)
• Natural language processing
(computational linguistics;
unstructured data)
• ↓ Costs + ↑Investments
• Building technology solutions
• Running tests and deployment of
solutions
• Business models
(cloud, SaaS, PaaS etc.)
Demand
• Big Data “5 Vs” – data explosion
• Volume, Velocity, Variety, Veracity,
Value
• Socialization of technology use:
personal à professional
• Sophisticated in-house IT expertise
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6. Common use cases: Are they ready for law?
• Speech recognition and
machine transcription
• Personalization (AUI;
recommendation engines)
• Email SPAM filters
• Fraud detection (banks)
Why don’t we ask Siri (Apple) or Alexa
(Amazon) for legal research help, yet?
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7. A.I. adoption by firms and in-house teams
Charts by Kira Systems, April 2020
Thomson Reuters In-house Technology Survey 2017
2019
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8. Where is A.I. applied today?
But why?
• Why are uptakes in
these areas?
• Why are there many
products? Or not
enough?
• Where next?
LawGeex, 2019 Sebastian Ko 2020 © 7

9. Definitions, Examples and
Challenges
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10. Key concepts and values
• Automation: cost-efficiency and risk management
• A.I. = ensemble of tech. that makes assumptions,
tests and learns autonomously
• ↑↑ Value of data integration
• Data analytics: Gaining insights and patterns
• Descriptive analytics: Tells you what happened in the
past (e.g. summary stats)
• Diagnostic analytics: Helps you understand why
something happened in the past
• Predictive analytics: Predicts what is most likely to
happen in the future (based on historical data)
• Prescriptive analytics: Recommends actions you can
take to affect those outcomes
Automation
Analytics & A.I.
Supervised &
Unsupervised
Learning;
Deep Learning
Yates, 2019
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11. Example: Third-party funders of claims
• Predictive analytics; a decision-
making aid
• Better information for p values
• Additional factors: Has the judge
eaten lunch yet? (PNAS, 2011)
• Litigation funding is
US$3 billion asset class (New
Yorker, 2016)
M. Victor, 2015 (litigation example)
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12. Examples: Rule-based, supervised and
unsupervised learning models
Classifying clauses (Kira)Cluster wheel of concepts (Brainspace)
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13. A.I. areas of applications and adoption by
industries
McKinsey, 2019
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14. Speed of adoption: Technical challenges
Collection
Source: Human
vs. machine; Real
vs. synthetic
Unstructured
data
Data sensitivity
(incl. transfer
restrictions)
Processing
Data volume,
quality
Cleaning &
Unitization
Standardization,
normalization,
protocols
Training /
Learning
Selecting the
right algorithm /
model
Unsupervised vs.
Supervised;
Reinforcement;
Deep
Analysis
Error acceptance
/ tolerance
Validity of
insights from
model's results
Adapting to new
cases
Present &
Audit
Visual design,
easy-to-
understand
Interpretable
Explainable
(Non-exhaustive lists) Sebastian Ko 2020 © 13

15. Massive volume of quality data needed
General
• Great models are useless without data
• “Massive” = 10k to 100k cases… and shrinking
• A.I. smarter with fewer lessons needed
• Real vs. synthetic data
• “Quality” of data
• Words vs. numbers
• Language(s)
• Context – industry-specific etc.
• Data preparation
• File types
• Text cf. MS Word vs. social media
• Filters (time, custodian etc.) depending
on case context
Legal Sector-Specific
• Sheer data volume held / processed
• Law firms vs. FAANG
• LegalTech Corporations
• Ability to share and pool data
• Client ownership of data
• Data sensitivity
• Cross-border data transfer
Extract and abstract relevant data points
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16. Legal, regulatory, and ethical constraints
• Sensitive data and cross-border
data transfers
• US HIPAA
• CN Cybersecurity Law
• SG Banking Secrecy Provisions
• Data-sharing models
• Attorney-client privilege; practice-
ownership; marketing rules
• SaaS & other business models
• Bias in A.I. decision-making and
profiling processes
• EU GDPR, Art. 22
• US Algorithmic Accountability Bill
How is the client-lawyer-vendor
relationship impacted?
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17. Finding the right models
• How to represent human-created text in
computer models to find relevancy and other
criteria?
• Existence of keywords
• Co-occurrences with e.g. metadata and
other artifacts
• Linguistic distances
• Model achieves an acceptable level of …?
• Accuracy, reliability / consistency
• Statistical standards
• Legal standards
(see Triumph v Primus (2019), E&W)
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18. Legal informatics issues
• Classification into computer-friendly
structures
• Manual ways: headnote searches, and
WestLaw KeyCite
• Natural Language Processing: Better
classification of legal text and queries
• Research on ontologies, argumentation
theory and computational logic
How to factor into relevancy?
• Words with both legal and common meanings
(e.g. director – company vs. film)
• Precedential value (common law)
• Open-ended terms that change meanings
over time (common law)
• Intention: a case favouring your side
• No. of citations (preceded Stanford page-
ranking methods, used by Google)
Relativity, search setup and search hits report
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19. Model validity and blackbox decisions
• Q: "What is covering the windows?”
• A.I. “There are curtains covering the
windows.”
• Bed > Bedroom > Bedrooms have
curtains covering windows
(training set)
• Does the machine understand the
substantive meaning?
• Is it learning the right lessons?
Virginia Tech A.I. eye-tracking research (2016)
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20. Whitebox and statistical interpretations
Predicting apartment prices
100s to 1000s of trees
Everlaw, Predictive Coding (SVM)
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21. Speed of adoption: Non-technical factors
Adoption
Barriers
Risk-averse
culture
Laws &
regulations
Funding,
business
model
Informed
tool
selection
Steep
learning
curve
Expert
support
Solutions to be adapted to
legal context
• Understand which processes
need automation
• Infrastructure / legacy
systems
• Change management
practices / implementation
• Skilled resources – service
providers and consultants
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22. Implications on
Legal Sector
and Justice
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23. Buyers and users
• Sales pitch vs. actual adoption,
and who cares?
• Luxury vs. necessity
• Lawyers vs. clients
• Lawyers vs. other consultants
• Reshaping the legal world for
mechanical savants
• Legal process outsourcing
(huge growth after GFC, 2008)
• “Legal operations” as a discipline
• Fundamental concepts about
the legal profession are questioned
New ways to deliver legal services?
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24. Builders, purveyors, beneficiaries, other
stakeholders
• Private sector-led innovations
• Export by e.g. US and UK (legal
cultures)
• How to get the “tool smiths” to
make better tools?
• Silo developments
• Users are often not the beneficiaries
• Tech providers; increasingly key
players in the legal sector
• LegalTech as niche sector
• General tech providers weighing in
• Consumer protection
• “Equality of arms”
• Lob-sided growth in commercial
practices vs. other areas
• Spurred by tech influence
• Assessing metrics (ROIs etc.) vs.
more abstract notions (justice?)
• Innovation policies
• Regulatory sandbox
• Privatized justice
• Stagnation of jurisprudence (in
certain pockets?)
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25. The tech and legaltech mindsets
Fundamental Rights /
Constitutional
Substantive Law
Procedural &
Evidentiary
Industry-Specific
Regulations
Professional Ethics
General (e.g. Privacy) /
Transactional
Rights &
Justice
Obligations
Efficiency
Risk
Manage
Revenue ↑ &
Productivity ↑
Cost ↓
Security ↑
Consistency ↑ &
(Human) Error ↓
Predictability ↑ &
Auditability ↑
Integration ↑ & Connectivity ↑
?
?
?
?
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26. Conclusion
Massive
development
gaps
End users ≠ end
payers
Highly
regulated, risk-
averse users /
buyers
Niche sector,
investments
Legal & tech
experts,
knowledge
sharing
Overzealous
vendors,
Product-market
fit lacking
• Raising tech baseline, supporting
SME firms’ tech adoption
• Public funding
• Guidance and education
• Develop building blocks of legal A.I.
“public goods”
• Open legal data
• Open-source algorithmic law models
• A.I. transparency and
interpretability
• Consumer protection
• Cross-disciplinary talent
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