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Copyright © 2015 Ron Dolin1
An Engineer's Journey
Through the Legal System
Notre Dame Computer Science
Ron Dolin
Notre Dame Law School
Stanford Law School
April 23, 2015
Copyright © 2015 Ron Dolin2
Anti-dilution: Lawyer Perspective
If new stock is issued at a price per share lower than the conversion price then in effect
for a particular series of preferred stock, the conversion price of such series will be
reduced to a price determined by multiplying the conversion price by the following
fraction:
[Common Outstanding pre-deal] + [Common issuable for amount raised at old conversion
price]
-------------------------------------------------------------------------------------------------------------------
[Common Outstanding pre-deal] + [Common issued in deal]
There are two primary variations of the weighted average formula depending on what
constitutes “Common Outstanding” in the above formula. The first, and more common, is
referred to as “broad-based weighted average” while the second is referred to as “narrow-
based weighted average.”
Broad-based weighted average formula
The calculation of “Common Outstanding” in the broad-based formula includes all shares
of common stock and preferred stock (on an as-converted to common basis) outstanding,
common issuable upon exercise of outstanding options, common reserved for future
issuance under the company’s stock option plan and any other outstanding convertible
securities, such as warrants.
Copyright © 2015 Ron Dolin3
Anti-dilution: Engineer Perspective
Copyright © 2015 Ron Dolin4
engineering v. Law

flip-flops and dog

long days (start late)

free food and snacks

ISO's and salary

peer-review

Glitzy Slides?!!

smart colleagues

the new new thing
Shirt and Shoes
(dress)
Long Days (start
early)
Pay or Bill for Meals
Billable Hours
Student Journals
hand slides?!!
Smart Colleagues
Copyright © 2015 Ron Dolin5
Legal Research: Old-er
Copyright © 2015 Ron Dolin6
Legal Research: Old-ish
Copyright © 2015 Ron Dolin7
Legal Research: New-ish
Copyright © 2015 Ron Dolin8
Legal Research: New-est
Copyright © 2015 Ron Dolin9
Litigation Binders: Old
Copyright © 2015 Ron Dolin10
Litigation Binders: New-ish
Copyright © 2015 Ron Dolin11
Litigation Binders: New-est
Copyright © 2015 Ron Dolin12
Similar Trends In...
Dispute Resolution
Document Automation
Decision Support
Data Analysis and Visualization
Knowledge Management
Process Management
Online Delivery
Engagement
Gamification
E-discovery
Copyright © 2015 Ron Dolin13
But...

Severe Access to Justice (A2J) problem

Vast untapped middle-class market

Corporate clients have paid $$$$$

Lots of under-employed lawyers

Associates spend time on tasks they hate

Technology adoption is very slow (if allowed)
WHY?
Copyright © 2015 Ron Dolin14
Protectionism

No single problem

Evolved around predictability and inefficiency

Little historic incentive to innovate

Attitude combines product with process

Lacking accountability: quality, efficiency

Self-regulating guild; closed (public) data

Always the right time to wait

Supply-driven market
Copyright © 2015 Ron Dolin15
Innovator's Dilemma Generally

Disruptive vs. sustainable technology

Changing market values

Diminishing high-margin market

Business structure dependencies

Market research useless

Discovery-based planning (failure IS
acceptable – try, try again)
Copyright © 2015 Ron Dolin16
External Pressures on Law

Moving from supply- to demand-driven market

New entrants
− low margin
− non-consumption

Technology's inevitable invasion

A2J increasingly unconscionable

Usual suspects (globalization, ABS, etc.)
Copyright © 2015 Ron Dolin17
Big Law Analysis

Defining core values – value, quality, efficiency

Restructuring model

Same people?

What is the “product”?

What is the function of big law in law?

Here to stay (in some form)?
Copyright © 2015 Ron Dolin18
Moving the Needle, Part 1

Quality Metrics

Incentive Structure

Attracting Engineers
Copyright © 2015 Ron Dolin19
Quality Questions

A2J: when is doing nothing better than doing
something “worse”?
− bad wills worse than probate
− bad contracts worse than UCC defaults
− wrong party pays worse than no one pays

Corporate clients – which firm is best?

Consumers – which lawyer is best?
Copyright © 2015 Ron Dolin20
Evaluation – Beyond Coin Tosses

Goals:
− efficiency
− quality (e.g. fairness – process, outcome)
− accuracy
− accessibility

How much quality is efficiency worth?

How do we measure quality?
Copyright © 2015 Ron Dolin21
Quality Control (QC) at Google

Purpose
− Improving core service (ads, search results)
− Efficiency gains w/o harm
− Preventing/discovering hidden problems

Method
− Manual setup
− Automated testing
− Scoring Result Quality (subjective/objective)
− Internal (absolute)
− External (relative)
Copyright © 2015 Ron Dolin22
Legal Services QC Goals

Clients
− compare (component) services
− value-driven purchasing
− ROI

New Entrants
− validation
− market segmentation

Incumbants
− high standards (not via proxy)
− efficiency (w/o loss of quality)
− staff performance metrics
Copyright © 2015 Ron Dolin23
Current QC Inadequate

Subjective

Coarse grain
− outcome aggregates (e.g., annual pay-out)

Proxy
− reputation
− pedigree

Manual
Copyright © 2015 Ron Dolin24
Necessary Methodology

Manual
− hierarchical, direct, per-item
− peer
− sampling
− fuzzy

Automated
− training data
− scoring function (e.g. bar exam essay scores)
− tuning and testing procedures
− machine learning
Copyright © 2015 Ron Dolin25
ODR Evaluation – New Approach

Online divorce mediation: # MSA's

eBay:
− 80% (of 60M!) resolved via automation
− closed ecosystem amenable to optimization
− implementing feedback is within eBay's
control
− lower # disputes per transaction (via
feedback)

DSD – Dispute Systems Design – prevention!
− negotiating interests, rights, or power
Copyright © 2015 Ron Dolin26
Litigation Example

Deposition “witness files”

Precision, recall based on opposing counsel

Weighted by importance/use, for example
− 3: shows up in court opinion
− 2: used as evidence in trial
− 1: used during deposition

Transparent, optimizes desired behavior

Effective value comparison (cost, quality)
Copyright © 2015 Ron Dolin27
Benchmarks

Value = Quality / Cost

Measurable quality metrics

Can't compare without the same quality metrics

Who has the money and power to drive this?

Empirical legal studies: benchmarks

Human evaluation → metrics → tuning

All components of the legal system
Copyright © 2015 Ron Dolin28
Engineering Problems in Law

Embedding semantics in network graphs

Machine learning, scoring, optimization, AI

Sophisticated HCI (beyond UI)

Info Inter: Multifaceted IR and KM, NLP

Distributed dynamic access control

Formal logic, expert systems, decision support

Little big data

Evaluation
Copyright © 2015 Ron Dolin29
Legal Tech & Informatics Course

Communication – actual startup examples:
− modeling courts with wrong data structure
− “Bates Number” vs. “Unique ID”

Informatics mindset

Mapping problems to tech toolkit and vice versa

Understanding context, market, barriers

Survey of various application areas

Understanding and shaping new profession
Copyright © 2015 Ron Dolin30
Moving the Needle, Part 2

Blogging

Consulting

Angel Investing

Active Research
− Quality Metrics
− Knowledge Management

Legal Tech & Informatics Course

Mentoring

Expanding Career Paths
Copyright © 2015 Ron Dolin31
Ron Dolin
Ron.A.Dolin@radicalconcepts.com
radicalconcepts.com
@LegalNoise

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An Engineer's Journey Through the Legal System

  • 1. Copyright © 2015 Ron Dolin1 An Engineer's Journey Through the Legal System Notre Dame Computer Science Ron Dolin Notre Dame Law School Stanford Law School April 23, 2015
  • 2. Copyright © 2015 Ron Dolin2 Anti-dilution: Lawyer Perspective If new stock is issued at a price per share lower than the conversion price then in effect for a particular series of preferred stock, the conversion price of such series will be reduced to a price determined by multiplying the conversion price by the following fraction: [Common Outstanding pre-deal] + [Common issuable for amount raised at old conversion price] ------------------------------------------------------------------------------------------------------------------- [Common Outstanding pre-deal] + [Common issued in deal] There are two primary variations of the weighted average formula depending on what constitutes “Common Outstanding” in the above formula. The first, and more common, is referred to as “broad-based weighted average” while the second is referred to as “narrow- based weighted average.” Broad-based weighted average formula The calculation of “Common Outstanding” in the broad-based formula includes all shares of common stock and preferred stock (on an as-converted to common basis) outstanding, common issuable upon exercise of outstanding options, common reserved for future issuance under the company’s stock option plan and any other outstanding convertible securities, such as warrants.
  • 3. Copyright © 2015 Ron Dolin3 Anti-dilution: Engineer Perspective
  • 4. Copyright © 2015 Ron Dolin4 engineering v. Law  flip-flops and dog  long days (start late)  free food and snacks  ISO's and salary  peer-review  Glitzy Slides?!!  smart colleagues  the new new thing Shirt and Shoes (dress) Long Days (start early) Pay or Bill for Meals Billable Hours Student Journals hand slides?!! Smart Colleagues
  • 5. Copyright © 2015 Ron Dolin5 Legal Research: Old-er
  • 6. Copyright © 2015 Ron Dolin6 Legal Research: Old-ish
  • 7. Copyright © 2015 Ron Dolin7 Legal Research: New-ish
  • 8. Copyright © 2015 Ron Dolin8 Legal Research: New-est
  • 9. Copyright © 2015 Ron Dolin9 Litigation Binders: Old
  • 10. Copyright © 2015 Ron Dolin10 Litigation Binders: New-ish
  • 11. Copyright © 2015 Ron Dolin11 Litigation Binders: New-est
  • 12. Copyright © 2015 Ron Dolin12 Similar Trends In... Dispute Resolution Document Automation Decision Support Data Analysis and Visualization Knowledge Management Process Management Online Delivery Engagement Gamification E-discovery
  • 13. Copyright © 2015 Ron Dolin13 But...  Severe Access to Justice (A2J) problem  Vast untapped middle-class market  Corporate clients have paid $$$$$  Lots of under-employed lawyers  Associates spend time on tasks they hate  Technology adoption is very slow (if allowed) WHY?
  • 14. Copyright © 2015 Ron Dolin14 Protectionism  No single problem  Evolved around predictability and inefficiency  Little historic incentive to innovate  Attitude combines product with process  Lacking accountability: quality, efficiency  Self-regulating guild; closed (public) data  Always the right time to wait  Supply-driven market
  • 15. Copyright © 2015 Ron Dolin15 Innovator's Dilemma Generally  Disruptive vs. sustainable technology  Changing market values  Diminishing high-margin market  Business structure dependencies  Market research useless  Discovery-based planning (failure IS acceptable – try, try again)
  • 16. Copyright © 2015 Ron Dolin16 External Pressures on Law  Moving from supply- to demand-driven market  New entrants − low margin − non-consumption  Technology's inevitable invasion  A2J increasingly unconscionable  Usual suspects (globalization, ABS, etc.)
  • 17. Copyright © 2015 Ron Dolin17 Big Law Analysis  Defining core values – value, quality, efficiency  Restructuring model  Same people?  What is the “product”?  What is the function of big law in law?  Here to stay (in some form)?
  • 18. Copyright © 2015 Ron Dolin18 Moving the Needle, Part 1  Quality Metrics  Incentive Structure  Attracting Engineers
  • 19. Copyright © 2015 Ron Dolin19 Quality Questions  A2J: when is doing nothing better than doing something “worse”? − bad wills worse than probate − bad contracts worse than UCC defaults − wrong party pays worse than no one pays  Corporate clients – which firm is best?  Consumers – which lawyer is best?
  • 20. Copyright © 2015 Ron Dolin20 Evaluation – Beyond Coin Tosses  Goals: − efficiency − quality (e.g. fairness – process, outcome) − accuracy − accessibility  How much quality is efficiency worth?  How do we measure quality?
  • 21. Copyright © 2015 Ron Dolin21 Quality Control (QC) at Google  Purpose − Improving core service (ads, search results) − Efficiency gains w/o harm − Preventing/discovering hidden problems  Method − Manual setup − Automated testing − Scoring Result Quality (subjective/objective) − Internal (absolute) − External (relative)
  • 22. Copyright © 2015 Ron Dolin22 Legal Services QC Goals  Clients − compare (component) services − value-driven purchasing − ROI  New Entrants − validation − market segmentation  Incumbants − high standards (not via proxy) − efficiency (w/o loss of quality) − staff performance metrics
  • 23. Copyright © 2015 Ron Dolin23 Current QC Inadequate  Subjective  Coarse grain − outcome aggregates (e.g., annual pay-out)  Proxy − reputation − pedigree  Manual
  • 24. Copyright © 2015 Ron Dolin24 Necessary Methodology  Manual − hierarchical, direct, per-item − peer − sampling − fuzzy  Automated − training data − scoring function (e.g. bar exam essay scores) − tuning and testing procedures − machine learning
  • 25. Copyright © 2015 Ron Dolin25 ODR Evaluation – New Approach  Online divorce mediation: # MSA's  eBay: − 80% (of 60M!) resolved via automation − closed ecosystem amenable to optimization − implementing feedback is within eBay's control − lower # disputes per transaction (via feedback)  DSD – Dispute Systems Design – prevention! − negotiating interests, rights, or power
  • 26. Copyright © 2015 Ron Dolin26 Litigation Example  Deposition “witness files”  Precision, recall based on opposing counsel  Weighted by importance/use, for example − 3: shows up in court opinion − 2: used as evidence in trial − 1: used during deposition  Transparent, optimizes desired behavior  Effective value comparison (cost, quality)
  • 27. Copyright © 2015 Ron Dolin27 Benchmarks  Value = Quality / Cost  Measurable quality metrics  Can't compare without the same quality metrics  Who has the money and power to drive this?  Empirical legal studies: benchmarks  Human evaluation → metrics → tuning  All components of the legal system
  • 28. Copyright © 2015 Ron Dolin28 Engineering Problems in Law  Embedding semantics in network graphs  Machine learning, scoring, optimization, AI  Sophisticated HCI (beyond UI)  Info Inter: Multifaceted IR and KM, NLP  Distributed dynamic access control  Formal logic, expert systems, decision support  Little big data  Evaluation
  • 29. Copyright © 2015 Ron Dolin29 Legal Tech & Informatics Course  Communication – actual startup examples: − modeling courts with wrong data structure − “Bates Number” vs. “Unique ID”  Informatics mindset  Mapping problems to tech toolkit and vice versa  Understanding context, market, barriers  Survey of various application areas  Understanding and shaping new profession
  • 30. Copyright © 2015 Ron Dolin30 Moving the Needle, Part 2  Blogging  Consulting  Angel Investing  Active Research − Quality Metrics − Knowledge Management  Legal Tech & Informatics Course  Mentoring  Expanding Career Paths
  • 31. Copyright © 2015 Ron Dolin31 Ron Dolin Ron.A.Dolin@radicalconcepts.com radicalconcepts.com @LegalNoise