Qualitative Legal Prediction - Prof. Daniel Katz

786 views

Published on

Professor Daniel Katz' presentation at lawTechcamp 2011 in Toronto, ON

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
786
On SlideShare
0
From Embeds
0
Number of Embeds
202
Actions
Shares
0
Downloads
14
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Qualitative Legal Prediction - Prof. Daniel Katz

  1. 1. Quantitative Legal Prediction ( Or How I Learned to Stop Worrying and Embrace Disruptive Technology) Daniel Martin Katz Michigan State University - College of Law
  2. 2. (1) Big Data and Moore’s Law
  3. 3. (1) Big Data and Moore’s Law(2) The ‘Soft’ AI Revolution
  4. 4. (1) Big Data and Moore’s Law(2) The ‘Soft’ AI Revolution(3) Prediction
  5. 5. (1) Big Data and Moore’s Law(2) The ‘Soft’ AI Revolution(3) Prediction(4) Mental Models v. Aggregation
  6. 6. (1) Big Data and Moore’s Law(2) The ‘Soft’ AI Revolution(3) Prediction(4) Mental Models v. Aggregation(5) Quantitative Legal Prediction
  7. 7. This is the Era of “Big Data”
  8. 8. This is the Era of “Big Data”Increasing Computing Power
  9. 9. This is the Era of “Big Data”Increasing Computing Power
  10. 10. This is the Era of “Big Data”Increasing Computing PowerDecreasing Data Storage Costs
  11. 11. This is the Era of “Big Data”Increasing Computing PowerDecreasing Data Storage Costs
  12. 12. This is the Era of “Big Data”Increasing Computing PowerDecreasing Data Storage CostsFundamentally Altering the Scope of Scientific Inquiry
  13. 13. Highlighting the Data Deluge
  14. 14. Highlighting the Data Deluge
  15. 15. Highlighting the Data Deluge 2008
  16. 16. Highlighting the Data Deluge 2008
  17. 17. Highlighting the Data Deluge 2008 2009
  18. 18. Highlighting the Data Deluge 2008 2009
  19. 19. Highlighting the Data Deluge 2008 2009 2010
  20. 20. Highlighting the Data Deluge
  21. 21. Highlighting the Data Deluge
  22. 22. Highlighting the Data Deluge 2011
  23. 23. Highlighting the Data Deluge 2011
  24. 24. Highlighting the Data Deluge 2011 2011
  25. 25. Highlighting the Data Deluge
  26. 26. Highlighting the Data Deluge
  27. 27. Highlighting the Data Deluge
  28. 28. Okay But ...
  29. 29. Data is only half the story
  30. 30. Computation andArtificial Intelligence
  31. 31. The Artificial Intelligence Revolution is On
  32. 32. The Artificial Intelligence Revolution is On
  33. 33. The Artificial Intelligence Revolution is On
  34. 34. The Artificial Intelligence Revolution is On
  35. 35. The Artificial Intelligence Revolution is On
  36. 36. The Artificial Intelligence Revolution is On
  37. 37. An Meaningful‘Soft’ AI Example
  38. 38. E-Discovery
  39. 39. Some Applicable Terms
  40. 40. Natural Language Processing
  41. 41. Natural Language ProcessingKnowledge Representation
  42. 42. Natural Language ProcessingKnowledge Representation Feature Extraction
  43. 43. Natural Language ProcessingKnowledge Representation Feature Extraction Feature Selection
  44. 44. Natural Language ProcessingKnowledge Representation Feature Extraction Feature Selection Machine Learning
  45. 45. Natural Language ProcessingKnowledge Representation Feature Extraction Feature Selection Machine Learning Classification
  46. 46. Natural Language ProcessingKnowledge Representation Feature Extraction Feature Selection Machine Learning Classification Clustering
  47. 47. Prediction
  48. 48. A Few WordsAbout Prediction
  49. 49. Imagine Two Different Complex Systems
  50. 50. Weather
  51. 51. Tides
  52. 52. vs.Easy/ Predictable Difficult / Chaotic
  53. 53. vs.Easy/ Predictable Difficult / Chaotic
  54. 54. vs.Easy/ Predictable Difficult / Chaotic
  55. 55. The Caliber of Prediction is
  56. 56. A Function ofVarious Factors
  57. 57. Including ...
  58. 58. Underlying SystemVariability
  59. 59. Quality of Inputs
  60. 60. Etc.
  61. 61. Formal Treatment of the question of prediction inalternative Domains
  62. 62. Quantitative Legal Prediction
  63. 63. It Already Exists ...
  64. 64. This is aGreat Example
  65. 65. But What Does the Market Really Care About?
  66. 66. Disputes v. Decisions
  67. 67. Disputes, Filings, etc.
  68. 68. Bargaining in theShadow of the Law
  69. 69. What is the Client’s First Question?
  70. 70. do i have a case?
  71. 71. how is that assessment generated?
  72. 72. Mental Models vsAggregated Data
  73. 73. lots of factors matter
  74. 74. Time Scales Matter
  75. 75. inherent systemvariability matters
  76. 76. ‘complexity’ matters
  77. 77. Some Potential Inputs
  78. 78. what is the relevantinformation contained therein?
  79. 79. text, citations, votes, etc.
  80. 80. other metadata
  81. 81. what form might the outputs take?
  82. 82. standard client memo +statistical portrait of10,000 ‘similar’ cases
  83. 83. How do we assess similarity?
  84. 84. Analogical Reasoning and the Science of Similarity
  85. 85. similarity measures =~distance measures
  86. 86. developing thesedistance functions
  87. 87. is an important part of my research
  88. 88. just one example ...
  89. 89. Six Degrees of Marbury v. Madison A Sink Based Visualization
  90. 90. Some Additional Thoughts
  91. 91. Study of Law CanBe a Scientific Exercise
  92. 92. The Practice of Law Can be Closer to Engineering
  93. 93. The Market forLegal Services
  94. 94. The Market forLegal Education
  95. 95. Have a Date With Destiny
  96. 96. Some Reading ForYour Consideration
  97. 97. @computational
  98. 98. AdditionalMaterialsAvailable Online!

×