The Three Forms of (Legal) Prediction: Experts, Crowds and Algorithms -- Prof...Daniel Katz
The Three Forms of (Legal) Prediction: Experts, Crowds and Algorithms -- Professors Daniel Martin Katz & Michael J. Bommarito - Illinois Tech Law / Univ of Michigan CSCS (Updated Version)
Artificial Intelligence and Law - A Primer Daniel Katz
Artificial Intelligence in Law (and beyond) including Machine Learning as a Service, Quantitative Legal Prediction / Legal Analytics, Experts + Crowds + Algorithms
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...Daniel Katz
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and the Modern Information Economy - By Michael Bommarito + Daniel Martin Katz from LexPredict
The Three Forms of (Legal) Prediction: Experts, Crowds and Algorithms -- Prof...Daniel Katz
The Three Forms of (Legal) Prediction: Experts, Crowds and Algorithms -- Professors Daniel Martin Katz & Michael J. Bommarito - Illinois Tech Law / Univ of Michigan CSCS (Updated Version)
Artificial Intelligence and Law - A Primer Daniel Katz
Artificial Intelligence in Law (and beyond) including Machine Learning as a Service, Quantitative Legal Prediction / Legal Analytics, Experts + Crowds + Algorithms
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...Daniel Katz
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and the Modern Information Economy - By Michael Bommarito + Daniel Martin Katz from LexPredict
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...Daniel Katz
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Daniel Martin Katz + Michael J Bommarito
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...Daniel Katz
Exploring the Physical Properties of Regulatory Ecosystems: Regulatory Dynamics Revealed by Securities Filings — Professors Daniel Martin Katz + Michael J Bommarito
Introduction to artificial intelligence and lawLawScienceTech
Presentation at Seminar on Artificial Intelligence and Law (15/03/2018) at the Norwegian Research Center for Computers and Law (NRCCL), University of Oslo
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.
Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (KD...Krishnaram Kenthapadi
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias / discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We motivate the need for adopting a "fairness by design" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing machine learning based models and systems for different consumer and enterprise applications. Then, we focus on the application of fairness-aware machine learning techniques in practice by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we present open problems and research directions for the data mining / machine learning community.
Algorithms are taking control of our information rich world. As the twin sibling to Big Data, increasingly they decide how society views us via constructed profiles (as criminals? as terrorists? as rich or poor consumers?); what we see as important, newsworthy, cool or profitable (eg Twitter trending topics, automated stock selling, Amazon recommendations, BBC website top news topics etc); and indeed what we see at all as algorithms are increasingly used to filter our illegal or undesirable content as tools of public policy. Algorithms are peceived by virtue of their automation as neutral, objective and fair, unlike human decision makers - yet evidence increasingly shows the opposite - eg a series of legal complaints assert that Google games its own search results to promote its own economic interests and demote those of competitors or annoyances; while in the defamation field, French, German and Italian courts have decided that algorithmically generated autosuggests in search can be libellous (eg "Bettina Wolf prostitute"). . This paper asks if any legal remedies do or should exist to *audit* proprietary algorithms , given their importance, and asks if one way forward might be via existing and future subject access rights to personal data in EU data protection law. The transformation of these rights as proposed in the draft Data Protection Regulation is not however hopeful.
LexPredict - Empowering the Future of Legal Decision MakingDaniel Katz
LexPredict is an enterprise legal technology and consulting firm, specializing in the application of best-in-class processes and technologies from the technology, financial services, and logistics industries to the practice of law, compliance, insurance, and risk management.
We focus on the goals of prediction, optimization, and risk management to enable holistic organizational changes that empower legal decision-making.
These changes span people and processes, software and data, and execution and education.
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the ...Daniel Katz
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Daniel Martin Katz + Michael J Bommarito
Exploring the Physical Properties of Regulatory Ecosystems - Professors Danie...Daniel Katz
Exploring the Physical Properties of Regulatory Ecosystems: Regulatory Dynamics Revealed by Securities Filings — Professors Daniel Martin Katz + Michael J Bommarito
Introduction to artificial intelligence and lawLawScienceTech
Presentation at Seminar on Artificial Intelligence and Law (15/03/2018) at the Norwegian Research Center for Computers and Law (NRCCL), University of Oslo
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.
Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (KD...Krishnaram Kenthapadi
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias / discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We motivate the need for adopting a "fairness by design" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing machine learning based models and systems for different consumer and enterprise applications. Then, we focus on the application of fairness-aware machine learning techniques in practice by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we present open problems and research directions for the data mining / machine learning community.
Algorithms are taking control of our information rich world. As the twin sibling to Big Data, increasingly they decide how society views us via constructed profiles (as criminals? as terrorists? as rich or poor consumers?); what we see as important, newsworthy, cool or profitable (eg Twitter trending topics, automated stock selling, Amazon recommendations, BBC website top news topics etc); and indeed what we see at all as algorithms are increasingly used to filter our illegal or undesirable content as tools of public policy. Algorithms are peceived by virtue of their automation as neutral, objective and fair, unlike human decision makers - yet evidence increasingly shows the opposite - eg a series of legal complaints assert that Google games its own search results to promote its own economic interests and demote those of competitors or annoyances; while in the defamation field, French, German and Italian courts have decided that algorithmically generated autosuggests in search can be libellous (eg "Bettina Wolf prostitute"). . This paper asks if any legal remedies do or should exist to *audit* proprietary algorithms , given their importance, and asks if one way forward might be via existing and future subject access rights to personal data in EU data protection law. The transformation of these rights as proposed in the draft Data Protection Regulation is not however hopeful.
LexPredict - Empowering the Future of Legal Decision MakingDaniel Katz
LexPredict is an enterprise legal technology and consulting firm, specializing in the application of best-in-class processes and technologies from the technology, financial services, and logistics industries to the practice of law, compliance, insurance, and risk management.
We focus on the goals of prediction, optimization, and risk management to enable holistic organizational changes that empower legal decision-making.
These changes span people and processes, software and data, and execution and education.
Daniel Martin Katz (Illinois Tech - Chicago Kent) & Michael Bommarito (Computational Legal Studies.com) Present Network Analysis and Law: Introductory Tutorial @ Jurix 2011 (Vienna)
AWS re:Invent 2016: Blockchain on AWS: Disrupting the Norm (GPST301)Amazon Web Services
Recent interest in leveraging distributed ledgers across multiple industries has elevated blockchain from mere theory and into the spotlight of real world use. Learn why some partners have a vested interest in it and how blockchain can be used with AWS. In this session, we explore the AWS services needed for a successful deployment and dive deep into a partner's blockchain journey on AWS.
Bit by Bit: Effective Use of People, Processes and Computer Technology in the...Jack Pringle
A somewhat updated attempt to offer some practical tips for attorneys in managing technology, change management, process improvement, and many other buzzwords
Legal Analytics Course - Class 9 - Clustering Algorithms (K-Means & Hierarchical Clustering) - Professor Daniel Martin Katz + Professor Michael J Bommarito
Last year, Matt Sawchak spoke on section 75-1.1 at a CLE program sponsored by the Antitrust and Complex Business Disputes Section of the North Carolina Bar Association. I’ve attached my slides here.
In the presentation, he addressed three perennial unsettled topics under section 75-1.1:
- the meaning of unfairness (as compared with deception),
- per se violations, where a violation of another source of law—even one that has no private right of action—is treated as a violation of section 75-1.1, and
- choice of law in unfair-trade-practices cases.
The first section of the presentation summarizes Kip Nelson’s and my North Carolina Law Review article on the unfairness doctrine. Here’s a link to that article.
Note especially slides 36-39, where I address a murky subject: when section 75-1.1 claims can be barred for having extraterritorial effects. I’ll address this subject in a future post.
Big Data Pushes Enterprises into Data-Driven Mode, Makes Demands for More App...Dana Gardner
Transcript of a BriefingsDirect podcast on how creating big-data capabilities are new top business imperatives in dealing with a flood of data from disparate sources.
BI and big data analytics Force an Overdue Reckoning Between IT and Business ...Dana Gardner
Transcript of a Briefings Direct podcast on the need to solidly align business and IT goals and bring further collaboration on innovation within the enterprise.
Sogeti on big data creating clarity - Report 1-4 on Big Data - Sogeti ViNTRick Bouter
Since 2005, when the term “Big Data” was launched, Big Data has become an increasingly topical theme. In terms of technological development and business adoption, the domain of Big Data has made powerful advances; and that is putting it mildly.
In this initial report on Big Data, the first of four, we give answers to questions concerning what exactly Big Data is, where it differs from existing data classification, how the transformative potential of Big Data can be estimated, and what the current situation (2012) is with regard to adoption and planning.
VINT attempts to create clarity in these developments by presenting experiences and visions in perspective: objectively and laced with examples. But not all answers, not by a long way, are readily available. Indeed, more questions will arise – about the roadmap, for example, that you wish to use for Big Data. Or about governance. Or about the way you may have to revamp your organization. About the privacy issues that Big Data raises, such as those involving social analytics. And about the structures that new algorithms and systems will probably bring us.
http://www.ict-books.com/books/inspiration-trends
Data Stewardship and Governance: how to reach global adoption and systematic ...Pieter De Leenheer
Data quality and regulations are perpetual drivers for Data Governance solutions that systematically monitor the execution of data policy. And yet, there is along road ahead to achieve Data Governance: the term is still relatively unknown, there is no political forum in the form of a Data Governance Council, and software support is moderate. Time for change ! Data Governance requires automation on the one hand and a wide adoption of business to ICT on the other.
In this lecture, we set out the basic principles to successful develop Data Governance. By way of example, we show how to translate this in Collibra's Data Governance Center. We pay particular attention to identifying and modelling data policies and rules, and to empowering them on the basis of data stewardship and configurable workflows across silos and functions in the organization. The example is drawn from the Flanders Research Information Space, where data quality is critical to drive and boost pan-European Research policy.
Defining Digital Transformation - the researchTom Rieger
Everyone is talking about it but not many are doing it. Or they think they are and doing it wrong.... I am talking about defining 'digital transformation'. In working with Lane Severson we completed some great research where nearly 200 executives in the LOB and IT helped us give their take on their present state and where they WANT to go.
Hard to go on a 'digital transformation' journey if you aren't sure what it is.
In the first interview in this series, which kicks off PwC’s 2018 CEO Survey, chief executive Safra Catz explains the broad culture shift brought on by AI and cloud technologies.
Since 2005, when the term “Big Data” was launched, Big Data has become an increasingly topical theme. In terms of technological development and business adoption, the domain of Big Data has made powerful advances; and that is putting it mildly.
In this initial report on Big Data, the first of four, we give answers to questions concerning what exactly Big Data is, where it differs from existing data classification, how the transformative potential of Big Data can be estimated, and what the current situation (2012) is with regard to adoption and planning.
VINT attempts to create clarity in these developments by presenting experiences and visions in perspective: objectively and laced with examples. But not all answers, not by a long way, are readily available. Indeed, more questions will arise – about the roadmap, for example, that you wish to use for Big Data. Or about governance. Or about the way you may have to revamp your organization. About the privacy issues that Big Data raises, such as those involving social analytics. And about the structures that new algorithms and systems will probably bring us.
http://www.ict-books.com/books/inspiration-trends
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...Dana Gardner
Transcript of a discussion on why business leaders need to prepare now to optimize and automate accounts payable functions to elevate overall financial situational awareness.
As organizations move to an environment characterized by cloud computing, the Internet of things, distributed computing, and mobile applications, it will be important to understand where and how these technologies can create value and support procurement decision-making.
ChainLink Analyst on How Cloud-Enabled Supply Chain Networks Drive Companies ...Dana Gardner
Transcript of a discussion on how technology innovators and new services from such suppliers as Tradeshift are translating advances in procurement and finance into business impacts.
Legal Analytics Course - Class 5 - Quantitative Legal Prediction + Data Drive...Daniel Katz
Legal Analytics Course - Class 5 - Quantitative Legal Prediction + Data Driven Future of Law Practice - Professor Daniel Martin Katz + Professor Michael J Bommarito
PRECEDENT AS A SOURCE OF LAW (SAIF JAVED).pptxOmGod1
Precedent, or stare decisis, is a cornerstone of common law systems where past judicial decisions guide future cases, ensuring consistency and predictability in the legal system. Binding precedents from higher courts must be followed by lower courts, while persuasive precedents may influence but are not obligatory. This principle promotes fairness and efficiency, allowing for the evolution of the law as higher courts can overrule outdated decisions. Despite criticisms of rigidity and complexity, precedent ensures similar cases are treated alike, balancing stability with flexibility in judicial decision-making.
ASHWINI KUMAR UPADHYAY v/s Union of India.pptxshweeta209
transfer of the P.I.L filed by lawyer Ashwini Kumar Upadhyay in Delhi High Court to Supreme Court.
on the issue of UNIFORM MARRIAGE AGE of men and women.
ALL EYES ON RAFAH BUT WHY Explain more.pdf46adnanshahzad
All eyes on Rafah: But why?. The Rafah border crossing, a crucial point between Egypt and the Gaza Strip, often finds itself at the center of global attention. As we explore the significance of Rafah, we’ll uncover why all eyes are on Rafah and the complexities surrounding this pivotal region.
INTRODUCTION
What makes Rafah so significant that it captures global attention? The phrase ‘All eyes are on Rafah’ resonates not just with those in the region but with people worldwide who recognize its strategic, humanitarian, and political importance. In this guide, we will delve into the factors that make Rafah a focal point for international interest, examining its historical context, humanitarian challenges, and political dimensions.
Military Commissions details LtCol Thomas Jasper as Detailed Defense CounselThomas (Tom) Jasper
Military Commissions Trial Judiciary, Guantanamo Bay, Cuba. Notice of the Chief Defense Counsel's detailing of LtCol Thomas F. Jasper, Jr. USMC, as Detailed Defense Counsel for Abd Al Hadi Al-Iraqi on 6 August 2014 in the case of United States v. Hadi al Iraqi (10026)
A "File Trademark" is a legal term referring to the registration of a unique symbol, logo, or name used to identify and distinguish products or services. This process provides legal protection, granting exclusive rights to the trademark owner, and helps prevent unauthorized use by competitors.
Visit Now: https://www.tumblr.com/trademark-quick/751620857551634432/ensure-legal-protection-file-your-trademark-with?source=share
DNA Testing in Civil and Criminal Matters.pptxpatrons legal
Get insights into DNA testing and its application in civil and criminal matters. Find out how it contributes to fair and accurate legal proceedings. For more information: https://www.patronslegal.com/criminal-litigation.html
WINDING UP of COMPANY, Modes of DissolutionKHURRAMWALI
Winding up, also known as liquidation, refers to the legal and financial process of dissolving a company. It involves ceasing operations, selling assets, settling debts, and ultimately removing the company from the official business registry.
Here's a breakdown of the key aspects of winding up:
Reasons for Winding Up:
Insolvency: This is the most common reason, where the company cannot pay its debts. Creditors may initiate a compulsory winding up to recover their dues.
Voluntary Closure: The owners may decide to close the company due to reasons like reaching business goals, facing losses, or merging with another company.
Deadlock: If shareholders or directors cannot agree on how to run the company, a court may order a winding up.
Types of Winding Up:
Voluntary Winding Up: This is initiated by the company's shareholders through a resolution passed by a majority vote. There are two main types:
Members' Voluntary Winding Up: The company is solvent (has enough assets to pay off its debts) and shareholders will receive any remaining assets after debts are settled.
Creditors' Voluntary Winding Up: The company is insolvent and creditors will be prioritized in receiving payment from the sale of assets.
Compulsory Winding Up: This is initiated by a court order, typically at the request of creditors, government agencies, or even by the company itself if it's insolvent.
Process of Winding Up:
Appointment of Liquidator: A qualified professional is appointed to oversee the winding-up process. They are responsible for selling assets, paying off debts, and distributing any remaining funds.
Cease Trading: The company stops its regular business operations.
Notification of Creditors: Creditors are informed about the winding up and invited to submit their claims.
Sale of Assets: The company's assets are sold to generate cash to pay off creditors.
Payment of Debts: Creditors are paid according to a set order of priority, with secured creditors receiving payment before unsecured creditors.
Distribution to Shareholders: If there are any remaining funds after all debts are settled, they are distributed to shareholders according to their ownership stake.
Dissolution: Once all claims are settled and distributions made, the company is officially dissolved and removed from the business register.
Impact of Winding Up:
Employees: Employees will likely lose their jobs during the winding-up process.
Creditors: Creditors may not recover their debts in full, especially if the company is insolvent.
Shareholders: Shareholders may not receive any payout if the company's debts exceed its assets.
Winding up is a complex legal and financial process that can have significant consequences for all parties involved. It's important to seek professional legal and financial advice when considering winding up a company.
Debt Mapping Camp bebas riba to know how much our debt
Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Together - Professor Daniel Martin Katz
1. measure twice, cut once
Solving the Legal Profession's Biggest Problems Together
daniel martin katz
blog | ComputationalLegalStudies.com
corp | LexPredict.com
page | DanielMartinKatz.com
edu | chicago kent college of law
16. the economics of law
the industrialization
of the artisan
toward an enterprise
data strategy in legal
fin (legal) tech
Legal Analytics +
#MLaaS
part 1:
part 2:
part 3:
part 4:
part 5:
42. play “whack-a-mole”, reacting to
problems by creating fear and
friction within organizations and
the impression that there is a
legal risk around every corner.
Mediocre Lawyers
43. can help clients shape
(perhaps distort)
external perception of risk.
Merely Clever Lawyers
44. design systems that
balance risk and improve
transparency, helping clients
correctly price risk internally
Great Lawyers
45. when it comes to risk …
one challenge with identifying
their value proposition
is the counterfactual
46.
47. why do we have law firms?
(in other words what do they solve for …)
55. an economic concept concerning the fee to a
“principal” (an organization, person or group of
persons), when the principal chooses or hires an
"agent" to act on its behalf.
Because the two parties have different interests
and the agent has more information, the principal
cannot directly ensure that its agent is always
acting in its (the principal's) best interests.
99. we can then sum to generate
predictions about the
distributional moments of an
overall matter (or phase)
(i.e. mean, variance, skewness, kurtosis)
100. this matter should take …
between 9-15 months
in 85% of the similar matters
(what about the long tail?)
101. this matter will cost…
most common range 275k - 345k
but the second mode is 555k - 625k
(and that second
mode typically is
achieved when the
following factors are
present … )
120. Meet Bob Bob is about to
engage in yet
another round of
markup on deal terms
lawyer on
a major
corporate
transaction
121. Meet Bob Bob is about to
engage in yet
another round of
markup on deal terms
this round is likely to
generate a delay on
the expected
close of the deal
lawyer on
a major
corporate
transaction
122. how much value is created
by these modifications?
how much delay
will be introduced?
vs.
123. Need a better understanding
of the actual drivers of risk
124. Being able to compute the
change in risk as a function
of a change in deal terms
125. Requires Mapping of Deal Terms
to actual substantive outcomes
#legaldata
#legalanalytics
126. this is particularly important
when non-lawyers are
doing the negotiation
(for example your global sales force)
145. Behavior will change
(i.e. rogue action will be done offline)
Corp Security Beginning to
mirror today’s NSA
146. Behavior will change
But Behavior Change will lag
(i.e. rogue action will be done offline)
(i.e. folks will craft incriminating communications
at least for a while)
Corp Security Beginning to
mirror today’s NSA
147. thus, discovery (in part)
becomes compliance and some
(only some) litigation is avoided
legal standards will still shift
real time monitoring will generate
lots of false positives
155. Columbia Law Review
October, 2004
Theodore W. Ruger, Pauline T. Kim,
Andrew D. Martin, Kevin M. Quinn
Legal and Political Science
Approaches to Predicting
Supreme Court Decision
Making
The Supreme Court
Forecasting Project:
180. “Software developers were asked on two
separate days to estimate the completion
time for a given task, the hours they
projected differed by 71%, on average.
When pathologists made two assessments of
the severity of biopsy results, the correlation
between their ratings was only .61 (out of a
perfect 1.0), indicating that they made
inconsistent diagnoses quite frequently.
Judgments made by different people are
even more likely to diverge.”
189. we have developed an
algorithm that we call
{Marshall}+
random forest
190. Benchmarking
since 1953
+
Using only data
available prior to
the decision
Mean Court Direction [FE]
Mean Court Direction 10 [FE]
Mean Court Direction Issue [FE]
Mean Court Direction Issue 10 [FE]
Mean Court Direction Petitioner [FE]
Mean Court Direction Petitioner 10 [FE]
Mean Court Direction Respondent [FE]
Mean Court Direction Respondent 10 [FE]
Mean Court Direction Circuit Origin [FE]
Mean Court Direction Circuit Origin 10 [FE]
Mean Court Direction Circuit Source [FE]
Mean Court Direction Circuit Source 10 [FE]
Difference Justice Court Direction [FE]
Abs. Difference Justice Court Direction [FE]
Difference Justice Court Direction Issue [FE]
Abs. Difference Justice Court Direction Issue [FE]
Z Score Difference Justice Court Direction Issue [FE]
Difference Justice Court Direction Petitioner [FE]
Abs. Difference Justice Court Direction Petitioner [FE]
Difference Justice Court Direction Respondent [FE]
Abs. Difference Justice Court Direction Respondent [FE]
Z Score Justice Court Direction Difference [FE]
Justice Lower Court Direction Difference [FE]
Justice Lower Court Direction Abs. Difference [FE]
Justice Lower Court Direction Z Score [FE]
Z Score Justice Lower Court Direction Difference [FE]
Agreement of Justice with Majority [FE]
Agreement of Justice with Majority 10 [FE]
Difference Court and Lower Ct Direction [FE]
Abs. Difference Court and Lower Ct Direction [FE]
Z-Score Difference Court and Lower Ct Direction [FE]
Z-Score Abs. Difference Court and Lower Ct Direction [FE]
Justice [S]
Justice Gender [FE]
Is Chief [FE]
Party President [FE]
Natural Court [S]
Segal Cover Score [SC]
Year of Birth [FE]
Mean Lower Court Direction Circuit Source [FE]
Mean Lower Court Direction Circuit Source 10 [FE]
Mean Lower Court Direction Issue [FE]
Mean Lower Court Direction Issue 10 [FE]
Mean Lower Court Direction Petitioner [FE]
Mean Lower Court Direction Petitioner 10 [FE]
Mean Lower Court Direction Respondent [FE]
Mean Lower Court Direction Respondent 10 [FE]
Mean Justice Direction [FE]
Mean Justice Direction 10 [FE]
Mean Justice Direction Z Score [FE]
Mean Justice Direction Petitioner [FE]
Mean Justice Direction Petitioner 10 [FE]
Mean Justice Direction Respondent [FE]
Mean Justice Direction Respondent 10 [FE]
Mean Justice Direction for Circuit Origin [FE]
Mean Justice Direction for Circuit Origin 10 [FE]
Mean Justice Direction for Circuit Source [FE]
Mean Justice Direction for Circuit Source 10 [FE]
Mean Justice Direction by Issue [FE]
Mean Justice Direction by Issue 10 [FE]
Mean Justice Direction by Issue Z Score [FE]
Admin Action [S]
Case Origin [S]
Case Origin Circuit [S]
Case Source [S]
Case Source Circuit [S]
Law Type [S]
Lower Court Disposition Direction [S]
Lower Court Disposition [S]
Lower Court Disagreement [S]
Issue [S]
Issue Area [S]
Jurisdiction Manner [S]
Month Argument [FE]
Month Decision [FE]
Petitioner [S]
Petitioner Binned [FE]
Respondent [S]
Respondent Binned [FE]
Cert Reason [S]
Mean Agreement Level of Current Court [FE]
Std. Dev. of Agreement Level of Current Court [FE]
Mean Current Court Direction Circuit Origin [FE]
Std. Dev. Current Court Direction Circuit Origin [FE]
Mean Current Court Direction Circuit Source [FE]
Std. Dev. Current Court Direction Circuit Source [FE]
Mean Current Court Direction Issue [FE]
Z-Score Current Court Direction Issue [FE]
Std. Dev. Current Court Direction Issue [FE]
Mean Current Court Direction [FE]
Std. Dev. Current Court Direction [FE]
Mean Current Court Direction Petitioner [FE]
Std. Dev. Current Court Direction Petitioner [FE]
Mean Current Court Direction Respondent [FE]
Std. Dev. Current Court Direction Respondent [FE]
0.00781
0.00205
0.00283
0.00604
0.00764
0.00971
0.00793
TOTAL 0.04403
Justice and Court Background Information
Case Information
0.00978
0.00971
0.00845
0.00953
0.01015
0.01370
0.01190
0.01125
0.00706
0.01541
0.01469
0.00595
0.02014
0.01349
0.01406
0.01199
0.01490
0.01179
0.01408
TOTAL 0.22814
Overall Historic Supreme Court Trends
0.00988
0.01997
0.01546
0.00938
0.00863
0.00904
0.00875
0.00925
0.00791
0.00864
0.00951
0.01017
TOTAL 0.12663
Lower Court Trends
0.00962
0.01017
0.01334
0.00933
0.00949
0.00874
0.00973
0.00900
TOTAL 0.07946
0.00955
0.00936
0.00789
0.00850
0.00945
0.01021
0.01469
0.00832
0.01266
0.00918
0.00942
0.00863
0.00894
0.00882
0.00888
Current Supreme Court Trends
TOTAL 0.14456
Individual Supreme Court Justice Trends
0.01248
0.01530
0.00826
0.00732
0.01027
0.00724
0.01030
0.00792
0.00945
0.00891
0.00970
0.01881
0.00950
0.00771
TOTAL 0.14323
0.01210
0.00929
0.01167
0.00968
0.01055
0.00705
0.00708
0.00690
0.00699
0.01280
0.01922
0.02494
0.01126
0.00992
0.00866
0.01483
0.01522
0.01199
0.01217
0.01150
TOTAL 0.23391
Differences in Trends
208. expert crowd algorithm
via back testing we can learn the
weights to apply for particular problems
ensemble method
learning problem is to discover when to use a given stream of intelligence
246. #Fin(Legal)Tech
application of those ideas and
technology to a wide range of
law related spheres including
litigation, transactional work
and compliance.
259. tomorrow?
learn from legal ops service
offering to build a commercial
insurance product offering
legal cost insurance ?
other exotic insurance offerings?
260. AIG to Launch Data-
Driven Legal Ops
Business in 2016
https://bol.bna.com/aig-to-
launch-data-driven-legal-
ops-business-in-2016/
261.
262. #fin(legal)tech
In such a world,
Law Firm is *not* interfacing
with client but rather insurance
company regarding fees