How Humans Judge Machines compares people’s reactions to human and machine actions across dozens of experiments, revealing when and why humans are biased in favor or against machines. The Digital Version is Free and available as of today. Obviously, should not judge book by cover but it looks to be an interesting book.
Over on the Public Dashboard of Good Judgement Project, their aggregated SuperForecasters have been predicting a wide range of geo-political and other events including critical questions associated with COVID-19. A key question is When will enough doses of FDA-approved COVID-19 vaccine(s) to inoculate 25 million people be distributed in the United States? Note: For purposes of this prediction — “Compassionate use” and “emergency use” authorizations would count as approval.
ABSTRACT: Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.” Access Paper here.
Interesting paper – takes me back to my days at Michigan CSCS with Mike Bommarito, Jon Zelner and many others …
I last taught our Michigan ICPSR Class on Complex Systems (which included social simulation / agent based modeling) in 2015. The class contained both theory and implementation (using NetLogo). Check out the old slides / materials !
ABSTRACT: “Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves and in the first imaging of a black hole. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analyzing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.” Access Paper via Nature.
“In 2020, we’ve seen an explosion in remote work, with an increasing number of people and companies turning to productivity apps to more deeply connect with their coworkers and work in ways not possible through conventional video conferencing. Add in VR and its ability to engender social presence—the feeling that you’re sharing a virtual space with someone else—and you have a recipe for successful collaboration at a distance. And it just got a whole lot easier with the launch of Spatial on Oculus Quest.” via Oculus Blog.
Science is a network, each paper linking those that came before with those that followed. In an exclusive analysis, researchers have delved into Nature’s part of that network. We explore their results, taking you on a tour of 150 years of interconnected, interdisciplinary research, as represented by Nature’s publication record.
In the broader field of Artificial Intelligence (A.I.) there is a major divide between Data Driven A.I. and Rules Based A.I. Of course, it is possible to combine these approaches but let’s keep it separate and easy for now. Rules Based AI in the form of expert systems peaked in the late 1980’s and culminated in the last AI Winter. Absent a few commercial examples such as TurboTax, the world moved on and Data Driven A.I. took hold.
But here in #LegalTech #LawTech #LegalAI #LegalAcademy – it seems more and more like we have gone ‘Back to the A.I. Future’ (and brought an IF-THEN back in the Delorean). As even in 2020, we see individuals and companies touting themselves for taking us Back to the A.I. Future.
There is nothing wrong with Expert Systems or Rules Based AI per se. In law, the first expert system was created by Richard Susskind and Phillip Capper in the 1980’s. Richard discussed this back at ReInventLaw NYC in 2014. There are a some use cases where Legal Expert Systems (Rules Based AI) are appropriate. For example, it makes the most sense in the A2J context. Indeed, offerings such as A2J Author and Docassemble are good examples. However, for many (most) problems (particularly those with a decent level of complexity) such rule based methods alone are really not appropriate.
Data Science — mostly leveraging methods from Machine Learning (including Deep Learning) as well as Natural Language Processing (NLP) and other computational allied methods (Network Science, etc.) are the modern coin of the realm (both in the commercial and academic spheres).
As the image above highlights, the broader A.I. world faces challenges associated with overhyped AI and faux expertise. #LegalAI also faces the problem of individuals and companies passing themselves off as “cutting edge AI experts” or “offering cutting edge AI products” without an academic record or codebase to their name.
In the academy, we judge scholars on academic papers published in appropriate outlets. In order for someone to be genuinely considered an {A.I. and Law Scholar, Computational Law Expert, NLP and Law Researcher} that scholar should publish papers in technically oriented Peer Reviewed journals (*not* Law Reviews or trade publications alone). In the Engineering or Computer Science side of the equation, it is possible to substitute a codebase (such as a major Python package or contribution) for peer reviewed papers. In order for this field to be taken seriously within the broader academy (particularly by technical inclined faculty), we need more Peer Reviewed Technical Publications and more Codebases. If we do not take ourselves seriously – how can we expect others to do so.
On the commercial side, we need more objectively verifiable technology offerings that are not in line with Andriy Burkov’s picture as shown above … this is one of the reasons that we Open Sourced the core version of ContraxSuite / LexNLP.
NLLP Workshop 2020 Session 1: Legal Text Analysis — Video of Natural Legal Language Processing Workshop is Now on YouTube.
Unfortunately, I was not available to participate as I was teaching class at the time of the workshop. However, Corinna Coupette and Dirk Hartung represented us well !
Copy of the paper presented is available here —
SSRN LINK: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3602098
arXiv LINK: https://arxiv.org/abs/2005.07646
Lots has happened since Mike and I launched this site back in 2009 including a much larger community of folks interested in Computational Law. See the picture above from the Second Post on this blog.
We are rebooting Computational Legal Studies after a ~16 month break and back filling it with content that we have posted on other platforms such as LinkedIn, Facebook, etc.
It is good to be back pursuing the Computational Legal Agenda through this site.
We have a bunch of things in the works including our book “Legal Informatics” which is being released by Cambridge University Press in February 2021!
Best,
Daniel Martin Katz & Michael Bommarito
