The basic focus of the event was on using Capital One and partner API-based services to “discover the relevance, reach, and value of innovative new API products”. We had exclusive access to a brand new set of Identity related APIs not yet available to the public and my team took full advantage of that!

Business/Legal Scenario We Hacked

The team settled on a classic patient identity use case in the context of efficient/effective clinical healthcare and accessible/exportable medical records.

On the surface, this is a perfectly logical alignment of technical capabilities and business/legal needs. Organizations covered under HIPAA are required to implement procedures to verify that a person or entity seeking access to electronic protected health information is the person or entity claimed and this is exactly what the Capital One Identity APIs purported to provide. However, based on the most recent 10 years of spectacular, continuous, system-wide failure to achieve digital identity as a service usable in clinical healthcare and medical records contexts, I was surprised the team believed this was a wise area of focus.

Despite the apparent impervious resistance of clinical healthcare settings and medical records systems to acceptance, use or reliance upon externally issued digital identity credentials, everybody else on the team thought it was a great applied context and the Capital One people seemed to agree so — we did it!

After much back and forth we honed down the specific business and legal scenario including the key roles and relationships the key sequences of interactions and the relevant rules, rights and responsibilities at each stage. The first key interaction is identification and registration for a Capital One credential holder who is onboarding as a patient with her new primary care physician. The other key interaction is patient identification and authentication for making appointments or requesting access and downloads of her medical records.

Identity-Focused APIs We Hacked

• REGISTER: Sign Up with Capital One API allows you to access an OAuth 2.0 standard implementation for authorization, and use it to verify the identity of a user who is registering for your site.
• AUTHORIZE: Verify with Capital One API is an identity-proofing product that allows you to match the personal information provided by one of your customers to the verified information of a Capital One customer.
• LOGIN: Sign In with Capital One API allows end customers to use their Capital One User ID and Password on integrated partner sites. This product includes a multi-factor authentication challenge and explicit customer consent. Leveraging Capital Oneʼs credentials also provides the benefit of strong authentication, device confidence and user verification.

The “Patient Connect” Hackathon Project

Some of the highpoints of the value proposition:

• We demonstrated an approach to solve the HIPAA authentication barrier using the Capital One API Suite of Next Generation Identity Services: REGISTER•AUTHORIZE•LOGIN
• Under HIPAA, a “covered entity” is required to implement procedures to verify that a person or entity seeking access to electronic protected health information is the person or entity claimed. Passwords are notoriously problematic — that rare mix of hard to remember/use & yet unsecure/risky.
• Substantially identical requirements exist for access to educational records (FERPA), personal data held by government (FIPA) and telecom, fair credit reporting, etc. Likewise, the Patient Connect general design pattern we demonstrated can serve as a reusable building block for e-commerce, travel, utilities, civic participation, … anything where identity is required!

How We Hacked

We used a one-day/one-project channel on the neutral hackerland Slack team called “OpenCollab” and a shared deck of Google Slides to communicate, contribute and collaborate on a unified project and to develop a coherent narrative.

Below is a short video and individual images of the (slightly corrected) slides we presented as our Hackathon Project. The slides provided an umbrella for:

• A short “skit” with some light-hearted role-playing of a doctor and patient going through the new and returning patient interactions, processes and scenarios;
• A live demo of an AWS OAuth 2 client/app integration with the Capital One Identity APIs; and
• A high level statement of the value proposition, business/legal fit and deployment readiness of this technology capability for our healthcare onboarding, appointments and medical records access scenario.

More Info about the July 22, 2017 Boston Seaport Capital One Cafe Hackathon

Below is some information about the hackathon currently available at https://www.devexhacks.com/boston

Immerse Yourself in Inventive APIs

You’re invited to join top developers, designers, product leaders, and industry experts on July 22nd for a day of building and challenging the status quo at the Capital One Café in Seaport

As a hand-picked participant in this invite and referral-only event, you will gain insider experience working with devs, industry and API experts from Capital One DevExchange and Amazon Alexa. You and your team will work to lower the barriers faced by consumers in the financial and related markets as you:

* Enable a whole-new customer-center platform experience.

* Utilize your skills to exceed at a high threshold.

* Gain firsthand insights with a suite of well-built targeted APIs in the booming Fintech industry.

* Engage with Fintech industry leaders and Capital One DevExchange’s API experts across diverse disciplines.

* Build new or enhance existing applications with powerful new APIs.

Reinvent the future at the Capital One DevExchange Hackathon. Pull a team together or apply solo and we’ll place you on a team once approved. Request your invitation today!

Featured APIs

Digital Identity Products

Online identity fraud continues to grow across every channel and touchpoint of the end-to-end customer engagement cycle. At Capital One, we understand that access to trusted identity information is not only critical to managing our business across the customer lifecycle — itʼs critical to digital commerce and the economy at large. That’s why weʼre excited to partner with select businesses to test a new line of Digital Identity Products that balance a simple, elegant user experience with advanced identity capabilities. Learn More

Bank Account Starter

Get a first look at an exciting new API that makes it easy to connect to the Capital One banking platform. Learn More

Rewards

Access information on the miles, points or cash rewards your customers have earned with their Capital One accounts. Learn More

Credit Offers

Returns a personalized list of Capital One credit card offers in under 60 seconds based on just a few pieces of personal information. Learn More

Amazon Alexa

As a special feature of this Hackathon, the Amazon Alexa team will be on site to teach teams how to create Alexa Skills for their apps. The Alexa Skills Kit is a collection of self-service APIs, tools, documentation and code samples that make it fast and easy for teams to add skills to Alexa. A prize will be awarded to the team with the best Alexa UX.

More information on building Skills for Alexa available here: https://developer.amazon.com/alexa

Awards

Each participant will receive a generous SWAG kit from Capital One DevExchange for participating. We’re also creating rewards for finishing at the top of each API and judging category as well as recognizing overall top finishers. It’s an opportunity to show what you’re capable of and gain recognition for yoursel and your team as fintech developers.

Schedule

8:00AM — Doors, Registration, Continental Breakfast, Team building for those who don’t have a complete team

9:00AM — Welcome, rules and introductions

9:15–10AM — API Introductions — 15 minutes per API

Noon — Lunch served

6:00PM — Code Stop & Break, Food & Drinks

6:30PM — Demos — 2 minutes each

7:30PM — Awards

8:00PM — Adjourn

Capital One Boston Hackathon Win was originally published in ComputationalLaw.org on Medium, where people are continuing the conversation by highlighting and responding to this story.

Blockchain Working Demo

Thanks to Anders Brownworth for creating this open source demo and supporting reuse by CIVICS.com as a law+technology teaching tool.

What is a “Hash”?

What is a “Block”?

What is a “Blockchain”?

What does “Distributed” mean?

What are “Tokens”?

What is “Coinbase”?

Blockchain for Lawyers

Popular media breaking it down in a way relevant to lawyers and other legal professionals:

• Lawyers Get Ready, There’s a Blockchain Coming
• The Two Topics in Law and Blockchain
• Who Controls the Blockchain?

Relevant Legislation & Statutes

Federal Legislative Definition

US House: A collaboration between law.MIT.edu and the US House on developing legislative definitions of “blockchain” and “distributed ledger technologies and systems

Enacted US Federal and Uniform State Law

The general (default) legal status of electronic signatures, contracts and other records has been established in the United States through UETA (uniform state law) and ESIGN (federal law), both of which are implementations of the UNCITRAL Model Law on Electronic Commerce.

• Federal Law: Electronic Signatures in Global and National Commerce Act (ESIGN): https://www.gpo.gov/fdsys/pkg/PLAW-106publ229/html/PLAW-106publ229.htm
• Uniform State Law: Uniform Electronic Transaction Act (UETA): http://www.uniformlaws.org/Act.aspx?title=Electronic%20Transactions%20Act

Pending State Legislation

• State Bill: Nevada (SB 398): establishes various provisions relating to the use of blockchain technology. (Link to bill)
• State Bill: Arizona (H B 2417): signatures; electronic transactions; blockchain technology — Signed into law April 3, 2017. (Link to bill)

Blockchain for Legal Notice and Publicly Verifiable Evidentiary Records

• Article: Law Itself is the Killer Blockchain App
• Working Code: The OpenTimestamps Blockchain Service
• Blockchain Evidence: Prosecuting Crimes

General Digital Transactions Law

International Model Law

• Model Law: International Model Law on E-Commerce http://www.uncca.org/working-groups/working-group-iv
• Model Law: A/CN.9/897 — Report of Working Group IV (Electronic Commerce) on the work of its fifty-fourth session: https://documents-dds-ny.un.org/doc/UNDOC/GEN/V16/097/10/PDF/V1609710.pdf?OpenElement

Transferable Records

The legal validity of Electronic Transferable Records (e.g. legally validating the use of electronic records for negotiable instruments, warehouse receipts, etc) was explicitly established in UETA and ESIGN but significant business, legal and technology uncertainty remains about more specific practices and rules needed to support stable use and reliance at wide-scale.

• Model Law: Continung work by UNCITRAL on next-phase model international law by the Working Group on Electronic Transferable Records: http://www.uncitral.org/uncitral/en/commission/working_groups/4Electronic_Commerce.html
• Legal Hack: Identifying issues and options through Massachusetts Legal Hackers: Rapid Prototyping Blockchain-Based Electronic Transferable Records

Digital Signatures, Contracts and Records

Law defining and governing digital signatures, electronic contracts, automated transactions and other electronic records or processes is important overarching legal context applicable to blockchain technology.

Building Blocks of Legal Identity and Enforceable Signatures

• Book: Electronic Signatures in Law (4th edition, Institute of Advanced Legal Studies for the SAS Humanities Digital Library, School of Advanced Study, University of London, 2016) http://ials.sas.ac.uk/digital/humanities-digital-library/observing-law-ials-open-book-service-law/electronic-signatures
• Research: Blockchain as Key Legal Identity Cross-Boundary Infrastructure Service
• Legal Hack: Modern Blockchain-Based Digital Signature Prototype: Massachusetts Legal Hackers Mock Trial
• Report: In the 1990’s, the ABA Digital Signature Guidelines provided a conceptual framework for legally understanding and working with cryptographically based digital signatures. This document provides an excellent overview of how hash digests and public-private key cryptography can be used to legally execute a digital signature, though some of the more speculative content about a possible role of “trusted third party” new models of business to provide “certification authority” functions did not pan out.

Evidence of Electronic Signatures and Records

• Book: Electronic Evidence (4th edition, Institute of Advanced Legal Studies for the SAS Humanities Digital Library, School of Advanced Study, University of London, 2017) http://ials.sas.ac.uk/digital/humanities-digital-library/observing-law-ials-open-book-service-law/electronic-evidence
• Journal: Digital Evidence and Electronic Signature Law Review http://journals.sas.ac.uk/deeslr (also available in the LexisNexis and HeinOnline electronic databases)

Help Make This (even) Better

• Suggest improvements in the GitHub Repo
• Enjoy the GitHub Pages version

The above article is based on the content outline of a CIVICS.com briefing I provided earlier today for in-house counsel of General Electric, a wonderful innovation company headquartered in Boston, Massachusetts.

Blockchain Briefing was originally published in ComputationalLaw.org on Medium, where people are continuing the conversation by highlighting and responding to this story.

The book on Electronic Evidence is best understood in the broader context of electronic signatures, electronic contracts and electronic transactions. Accordingly, it is worth checking out the recently published 4th edition of “Electronic Signatures in Law” and the “Digital Evidence and Electronic Signature Law Review”. These are all great examples of how to leverage the free, open “Creative Commons” standard copyright license as part of a successful publishing model for high quality, timely professional practice books and journals.

Your Weekend Reading:

Electronic Evidence (4th edition, Institute of Advanced Legal Studies for the SAS Humanities Digital Library, School of Advanced Study, University of London, 2017) http://ials.sas.ac.uk/digital/humanities-digital-library/observing-law-ials-open-book-service-law/electronic-evidence

Electronic Signatures in Law (4th edition, Institute of Advanced Legal Studies for the SAS Humanities Digital Library, School of Advanced Study, University of London, 2016) http://ials.sas.ac.uk/digital/humanities-digital-library/observing-law-ials-open-book-service-law/electronic-signatures

Digital Evidence and Electronic Signature Law Review http://journals.sas.ac.uk/deeslr/ (also available in the LexisNexis and HeinOnline electronic databases)

Electronic Evidence and Signatures Law was originally published in ComputationalLaw.org on Medium, where people are continuing the conversation by highlighting and responding to this story.

The basic idea is to develop one or more basic design pattern templates with simplified/streamlined workflows to handle extract/transform/load (ETL) so novice users can quickly move from a given dataset to an initial working VR expression of their dataset.

I want to test the hypothesis that this approach can enable users with little or no prior training or explanation to take the right actions to achieve common task-focused activities done today to quickly understand and “see” or “describe” and “understand” whatever a dataset is about (eg a budget, a transportation system, an ecological model, etc). If users could quickly/easily understand what the data is about, the next more complex and higher value capabilities would be to explore the data, gain insight from the data and finally to communicate ideas through the data as by “info-graphic” (or perhaps an “info-environment” or “info-experience” or “info-immersion” or some such extrapolation).

The above basic capability is premised on funneling standard data formats and structures to automated ETL funnels to rapidly build out VR environments tailored to achieve the purpose and task set associated with the template selected. The templates would provide standard visualizations (the environment and objects) and an instrument and control panel enabling a set of pre-configured query/search/filter/sort commands and navigations/exploration controls. The control panel that appears on the right side of the screen when we experimented with CSV data would be perfectly sufficient, with the addition of a few more readouts and controllers to enable whatever subset of commands and controls made most sense for the handful of things a pre-configured template provided. The basic idea is to package small sets of readouts (like a HUD, or a breadcrumb portal keeping track of the line numbers, column/row coordinates, json objects, etc corresponding to the current view or selection, etc).

To follow along this line of early research and collaborative expiramentation, check out: DataVR.xyz and stay tuned for a potential CIVICS.com rapid prototype jam and an MIT/IAP January short course, among other venues, where this concept will be further iterated, refined and tested.

Using VR to Better Explore, Express and Experience Legal Analytics was originally published in ComputationalLaw.org on Medium, where people are continuing the conversation by highlighting and responding to this story.

Use of blockchains for so-called “smart contracts” is by now old news. But there is a lot more to law than contracts and blockchain technologies may hold the key needed to transition this field into the digital age.

On a panel discussion about blockchains and the law at the Legal Hackers Congress in NYC this weekend, I’ll provide a sneak-peek at a new, systematic approach for using open blockchains to open the law. All the law.

My remarks will include an early look at prototype tools enabling state legislatures and agencies to publish official statutes and regulations via ordinary government websites by leveraging blockchain validators to meet key Uniform Electronic Legal Materials Act requirements. This approach could become a more streamlined, simplified native path than use of GPG software by each jurisdiction or outsourcing. It works for federal and city laws too.

Blockchain technology can provide the basis of a complete legal stack that connects everything from individual authorizations and approvals to enforceable contracts and licenses right up to the applicable statutes and regulations defining and government aspects of each transaction and instrument. The distributed architecture of open blockchains provide a direct way to publicly verify data has not been altered since it was inscribed in a block and added to the chain. This public verifiability has already been recognized as sufficient for meeting the needs of self-authentication under the rules of evidence in the state of Vermont. It could also be used for self authenticating statutes and regulations by any — or every — jurisdiction.

It would be reasonable to assume government would already have public law available as public information on free, open and public information along with the dates of elections, addresses of tax departments and rules for passing a driver license exam. Since the 1990’s much money, effort and attention has been invested to move important public data and processes onto websites, apps and other Internet systems. And yet, much of the content of legislative, judicial and administrative legal information is not available. More to the point, to my knowledge, virtually no official public law is published on standard, freely accessible government websites and other Internet channels. If you read the small print on the laws government publishes on the web, you will see a notice that the content is not official, may not be accurate and a disclaimer not to rely on the words. Because the laws published on government websites are not official, they can’t be entered into evidence in a judicial case in order to rely on the actual enforceable law. I am aware of no official versions of statutes or regulations published on the government huge number of websites, open data portals, apps and other services provided by the federal, state or city government of the United States.

Given that law could (or should?) be the quintessential “online government” web resource, why is this information not on government sites and services now? There are several factors in play and the circumstances are somewhat confusing and complex. The hardest aspects of putting the content of legislated and regulated public law online relate issues like getting agreement on workflows and approval chains among relevant stakeholders, development formats and standards for structuring the data and content, refining the user interface so the information and behaviors are easy to use and quickly feel intuitive and other basic challenges of deploying public-facing digital publishing systems. The Free Law Founders is one good example of people coming together to solve these issues. The work is being addressed thanks to public sector leaders like New York City Councilor Ben Kallos and to mission-driven technically expert groups like the OpenGov Foundation and a diverse, growing range of stakeholders from industry to academia to volunteer civic hacking groups across the country and the world.

Establishing a common approach to ensuring the electronic content has not been accidentally changed or intentionally tampered is necessary to publish the authoritative, official version of the law online. However, I believe one of the strategic inhibitors that has been slowing broad agreement to move law onto the web with other public record and open data has been lack of a workable, nationally scalable and common approach to address the need to ensure the content has not been changed from the exact law that was actually enacted and promulgated. There are several free tools, apps and services that can provide this data integrity assurance and can work with any product, method or process used by governments that make laws. Every governmental organization that promulgates law uses publishing workflows that can include stamping a verifier to the blockchain as part of a final step when new or amended statutes, regulations, executive orders or other laws are confirmed for publication. This step can be made invisible, cause no discernible delay and yet could become the killer blockchain use case for law.

The result of leveraging existing blockchains to provide publicly verifiable public law would be to render the law a genuinely public resource that is definitely discoverable, routinely reproducible and continuously computable. This use of Blockchain can provide a verifiably valid way to provably identify, cite, link, track and map the law at large. Given the free availability of open blockchains, this method can provide a new kind of public infrastructural ledger service at practically no additional cost to the public. This use of open blockchains as part of promulgating public law would provide an official, verifiable and standard means of transforming law into machine readable, software usable and scientifically computational data.

The law has been unknowably voluminous and unusably complex for a long time now. However, when the law becomes publicly verifiable and openly computational, it will be possible to make more definite legal statements and ensure more predictable legal outcomes. A killer blockchain app to liberate law as verifiable, standard data may also hold the key to render law understandable and usable by people, groups and organizations.

UPDATES:

Legal Hackers Talk:

Talk at Legal Hackers Internation Summit describing the LawChain.org prototype project idea:

https://youtu.be/q3Blnel8NAM?t=8m

Tools and Approaches

Open tools for prototype recommended by Jonathan Harvey-Buschel of MIT Bitcoin Club:

One service already does this: https://blocksign.com/

A better way to do it is to use this service: https://proofofexistence.com/ Combined with this wallet: http://www.multibit.org/

Specifically:

1. Have users Download the MultiBit software.

2. Have users create a wallet in MultiBit.

3. In the tools section on the left side, users can sign text and verify signatures.

4. Submit the text to https://proofofexistence.com/, and record the transaction id for lookup.

5. Sign the text, and record the signatures.

6. If the text + signatures + addresses used to sign + transaction id are posted publicly, anybody can verify that the text was committed to at some date, and that some users signed it.

Project Site

• Project site is live, at: http://LawChain.org

Law is the Killer Blockchain App was originally published in ComputationalLaw.org on Medium, where people are continuing the conversation by highlighting and responding to this story.

I’ve just posted a short video overview of the idea and prototype goals on the research project page here: https://law.MIT.edu/Automated-Credit-Union.

The Automated Credit Union was originally published in ComputationalLaw.org on Medium, where people are continuing the conversation by highlighting and responding to this story.

The following is a refined and extended version of notes from a high power prototyping hour this Tuesday at the Code for America “Boston Brigade” weekly civic hacking night.

1) Identify Apparently Discriminatory Listings on Services from Which a Lot of Listings Data can be Collected:

Based on manual word search queries identify a significant number of listings that definitely contain a significant number of listings that include the validated words and phrases associated with discriminatory housing practices and some range of data (eg based on a date span and/or geographic area, and/or type of listing, etc) that includes a large number of the apparently discriminatory listing

2) Collect a Statistically Sound Sample of Listings Data Including the Relevant Instances of Discriminatory Listing

Grab A Whack-Ton of listings data of which the relevant discriminatory listings are an included but small part. Specifically, use the data collection methods identified for Airbnb and Craigslist to suck down a whack-ton of the overall data for that span (ie: all listing including but not limited to the pre-identified discriminatory listings within the service(s) for the period of time or the area of space, etc) and

3) Clean the Data into Reliable and Easily Computable Form

Scrub and wrangle and the stuffing out of the data until it:

a) is evidently correct, comprehensive and complete enough (based on cross-checking for the presence of each of the manually identified discriminatory listings to ensure they are in fact present in the span of data we grabbed and generally eye-balling and programmatically spot-checking the data to test if there are any obvious errors or logical clues we have made mistakes in our process of sucking it down and wrangling it or faulty assumptions about the nature of the data we were collecting or other wonky issues) and

b) is evidently clean, consistent and coherent enough for initial exploration (eg: structured as JSON or tabularized in CSV format) such that it can be programmatically addressed with basic GREP, REGEX etc processes to look for some baseline descriptive aspects of the data, like

i) a percentage of all listings that contain discriminatory words and phrases

ii) a time-series display of discriminatory listings on hourly, daily, weekly, etc scales in comparison to the entire set of listings for the same period,

iii) a geo-location based cluster display of discriminatory listings to look for any major clustering or other distribution

iv) based on the results of an initial high level exploration along the lines above, identify any possible correlations (ie: of statistical significance) and other relevant patterns to inform choices about deeper analytics, simulations and other data science methods to try

4) Apply a Dab of Data Science:

a) Some Basic Types and Purposes of Analytics

Once some basic explorations of data along the above lines are accomplished, the big idea is to use more advanced statistical modeling methods and analytical tools in order to identify deep structures and better predictive capabilities. These types of capabilities can enable sort/filter/search and early detection/screening/interdiction of discriminatory listings. These methods can also provide a way to observe dynamics of behaviors related to discriminatory listings over time. Some applied data science methods to start with include basic statistical modeling with R and Machine Learning approaches with, for example, customized version of the Python libraries we found for Boston area pricing models (see the notes in our Slack channel: #housing-data-justice).

b) A Special Look at Eigenvectors

Of special interest to me would be looking at how eigenvectors could be used to model discriminatory housing listings and identifying what parts of the data could be expressed as values, parameters or features. Modeling the data with eigenvectors can help to understand the basic behaviors and provide a basis for predicting the occurrence of discriminatory housing listings. Based on the results of some research and study, it is possible these models could provide a sound basis for describing and predicting discriminatory housing listings in general and potentially may be especially powerful with particular population segments or for certain regions geography or types of housing or for certain types of listings. Given results achieved with eigenvector-based models of to describe and predict dynamic systems of large group (or population) behaviors in other contexts (commerce, public health, etc) this approach may yield the powerful and actionable results for understanding and addressing discriminatory housing listings.

Determining which data provides the best results when expressed as features, values and parameters on eigenvectors would take some time and experimentation. Some initial ideas on potential vectors include:

i) the property address and unit listed (there will be some neighborhood, building and other physical container or envelope nuances here)

ii) the role of the party behind the listing (some complexity here regarding what specific information is available on the party that posts the listing or is identified as the contact for more information about the listing, and moreover is the party associated with the listing acting in the role of brokers/agents, property managers, renters/subletters, property owners, etc and whether the party associated with the listing wrote or had authority to change the description in the listing);

iii) the transaction type of listing (eg: property for sale, room available, whole apartment, long term/short term, etc)

iv) the time of the listing (eg date/day/time listing was posted, the length of time the listing remains active, maybe frequency or duration the same property is listed suggesting turnover or churn, etc)

v) the system within which the listing exists (ie Craig’s List, Airbnb, etc) and if applicable, the service or application (eg from desktop or mobile or through some API or other integration like with MLS or university or other listing services or from particular mobile apps that feed back to the system, etc).

5) Identify Impact of Policy and Other Interventions Based on Reality

a) Revealing a Dynamic Data-Driven Model of Reality

I am confident some collection of statistical models, dimensional reductions and eventual reporting and visualizable displays of discriminatory housing listings can be cobbled together rapidly, by combining basic a few of the best-fit solutions (ie graph analytics, machine learning, eigenvectors, etc) to reveal a significantly deeper understanding of the discriminatory housing listings and practices as they verifiably exist in reality.

b) Application of Data Analytics as a Governance Tool

This use of applied analytics can be used for “reality based governance” such that the current state of discriminatory housing practices can be verifiably modeled and much better understood. If successful, this can be used to better target public resources like consumer education, free legal services, focused enforcement and maybe deploy better detection and filtering and alerts/monitoring measures by or for the services so as to interdict discriminatory listings before they appear publicly or quickly identify and respond to public listings so they can be fixed or removed as soon as possible.

c) Data Analytics as a Policy and Governance Application and Service

These analytics can be combined as a lightweight application, available at a simple web-interface and corresponding service usable by other applications. This application and service can provide (to some established degree of accuracy) a tool for dynamic, data-driven and fact-based decision making suitable for a smart city.

6) Measurable Capabilities of a Policy Analytics Tool

A successful tool could also provide a powerful new set of capabilities for continuous and adaptive policy making, program design and resource allocation. This use of applied analytics can provide a usable model of the circumstances and surrounding factors that constitute and predict discriminatory housing listings and practices, including the fact-specific situations and measurable behaviors. These insights may yield better ideas about which set of actionable solutions could impact materials factors in measurable ways that matter most.

Whether a tool of this type is successful could tested based upon how well it enabled

a) monitoring of discriminatory housing practices at greater accuracy and timeliness than previously possible,

b) simulation and prediction to aid deliberation about alternative potential decisions before committing to then, and

c) objective measurement of the impact past decisions (policy, enforcement, education, etc) are having on behaviors and outcomes at larger or smaller scales of time, population and space.

Good results from initial, rapid civic hacking on the discriminatory housing data with data science and web-facing service-oriented applications could be an input for further, better resourced and managed development of a production-ready tool along the above lines.

— Notes and Context —

The above article is based on notes documenting progress made for the benefit of a team of civic hackers at the Boston Brigade of Code for America who plan to work on a project to address discriminatory housing. The documentation included a sketch of potential future directions, of which the above article is a refined and expanded version.

Most of the content documented tools or methods (some of which we got working) to identify, collect and analyze potentially discriminatory housing listings. For context, the rest of the notes passed forward to the civic hackers are provided below. If you want to try hacking this challenge, please give a shout so we can compare ideas and potential solutions.

Words and Phrases

Current collection of “Discriminatory Words and Phrases”

From nynewspapers.com (See: http://nynewspapers.com/wp-content/uploads/2014/05/FairHousingTerms.pdf)

AVOID

able-bodied
adult community*
adult living*
adults only*
adult park*
African
agile
AIDS
alcoholics, no
American Indian
Appalachian
Asian
black(s)
blind, no
board approval required
Catholic
Caucasian
Chicano
children, no
child(ren), (number of)
Chinese
Christian
colored
crippled, no
church
deaf, no
disabled, not suitable for
drinker(s), no
employed, must be
empty nesters*
English only
(ethnic references)
golden agers*
group homes, no
handicapped,
not suitable for
healthy only
Hindu
Hispanic
HIV
Hungarian
impaired, no
Indian
Irish
Italian
integrated
Jewish
landlord, (description)
Latino
married
mature couple*
mature individual*
mature person(s)*
membership approval
required
mentally disabled, no
mentally ill, no
Mexican-American
migrant workers, no
Mosque
Muslim
(nationality)
Negro
non-drinkers
non-smokers
older person(s)*
one child
Oriental
parish
park rules,
must comply with
Philippine
physically fi t only
Polish
preferred community
Protestant
Puerto Rican
quiet tenant(s)
(religious references)
responsible
retarded, no
seasonal workers, no
senior discount*
smoker(s), no
Social Security Insurance
(SSI), no
Spanish speakingstable
Synagogue
Temple
tenant(s), description of
two people
unemployed, no
white, white only

CAUTION

55 and older community*
62 and older*
active
bachelor
bachelor pad
close to
country club, near
couple
couples only
curfew
exclusive
executive
female(s) only
female roommate
fi sherman’ s retreat
gay(s), no
(gender)
gentleman’ s farm
grandma’ s house
golden agers only*
handicap
handyman’ s dream
heterosexual
homosexual
ideal for…
lesbian(s), no
male(s) only
male roommate
man, men only
membership approval
required
Mormon Temple
mother-in-law apt.
nanny’ s room
near
newlyweds
one person
older person*
perfect for…
person(s), (number of)
play area, no
prestigious
quality neighborhood
restricted
retired
retiree(s)*
retirement home*
safe neighborhood
Section 8, no
secure
seeking same
senior(s)*
senior citizen(s)*
senior housing*
(sex or gender)
shrine
singles only
single person
single woman, man
sophisticated
straight only
student(s)
students, no
temple
traditional
two people
walking distance to…
woman, women only

From ohrc.on.ca (See: http://www.ohrc.on.ca/en/policy-human-rights-and-rental-housing/v-identifying-discrimination-rental-housing)

**building is:**

* “adults only”
* “adult lifestyle”
* “not suitable for children”
* “suitable for a single person or couple”
* “working people only”
* “professionals only.”

Discriminatory advertising that targets households with children may be more subtle. Where a landlord is attempting to discourage or deny applications from families with children, they may use specific euphemisms in their advertising.

* **Statements that a building is:**

* a “quiet building”[140]
* “not soundproof”
* “geared to young professionals”

Relevant Overviews and Explanations

Good guidance and explanatory resources describing housing discrimination rules, requirements and various practices:

What Is Housing Discrimination?

From: http://www.civilrights.org/fairhousing/laws/housing-discrimination.html

Housing discrimination is a pervasive problem nationwide. It is also severely under-reported. The U.S. Department of Housing and Urban Development (HUD) estimates that more than two million instances of housing discrimination occur each year, but fewer than one percent are reported.

Many people are unaware that they have been victims of housing discrimination. A 2002 study by HUD suggests that many renters and homebuyers do not fully understand which activities are illegal under the Fair Housing Act.

If you think your rights to fair housing have been violated, help is available. Housing discrimination complaints can be filed by phone or in writing, with HUD and/or with private fair housing enforcement agencies located across the country.

The Fair Housing Act prohibits discrimination in housing on the basis of:

Race or color

National origin

Religion

Sex

Familial status (families with children)

Disability

Under the Fair Housing Act, the following activities are illegal:

Refuse to rent or sell housing

Refuse to negotiate for housing

Make housing unavailable

Set different terms, conditions, or privileges for sale or rental

Provide different housing services or facilities

Falsely deny that housing is available for inspection, sale or rental

For profit, persuade owners to sell or rent (blockbusting)

Deny any access to or membership in a facility or service (such as a multiple listing service) related to the sale of housing

Refuse to make reasonable accommodations in rules or services if necessary for a disabled person to use the housing

Refuse to allow a disabled person to make reasonable accommodations to his/her dwelling

Threaten or interfere with anyone making a fair housing complaint

Refuse to provide municipal services, property insurance or hazard insurance for dwellings, or providing such services or insurance differently

Here is an awesome quiz and background tutorial on Fair Housing rules with focus on words and phrases in housing descriptions that are acceptable and unacceptable
* https://kaarmls.com/sites/default/files/downloads/events/to_protect_serve_handouts.pdf
* Also: https://slack-files.com/T0556DP9Y-F3C0KJZMW-a319f3a777

Here is a nice Nolo article on types of housing discrimination: https://www.nolo.com/legal-encyclopedia/free-books/renters-rights-book/chapter5-2.html

Here is a nice resource describing what housing discrimination is, at a high level:
* http://www.civilrights.org/fairhousing/laws/housing-discrimination.html

Google Searches to Monitor:

* https://www.google.com/webhp?sourceid=chrome-instant&rlz=1C5CHFA_enUS712US712&ion=1&espv=2&ie=UTF-8#q=housing+description+words+discrimination

* https://www.google.com/webhp?sourceid=chrome-instant&rlz=1C5CHFA_enUS712US712&ion=1&espv=2&ie=UTF-8#q=data+science+machine+learning+housing+apartment+listings

* https://www.google.com/search?rlz=1C5CHFA_enUS712US712&espv=2&biw=1408&bih=631&q=boston+housing+dataset+download&sa=X&ved=0ahUKEwi9m-fh6-DQAhVKySYKHb__CbkQ1QIIeSgE

* https://www.google.com/webhp?sourceid=chrome-instant&rlz=1C5CHFA_enUS712US712&ion=1&espv=2&ie=UTF-8#q=github%20housing%20data%20descrimination

* https://www.google.com/search?rlz=1C5CHFA_enUS712US712&espv=2&biw=1408&bih=631&q=boston+housing+dataset+download&sa=X&ved=0ahUKEwi9m-fh6-DQAhVKySYKHb__CbkQ1QIIeSgE#q=fair+housing+apartment+description+download

* https://www.google.com/webhp?sourceid=chrome-instant&rlz=1C5CHFA_enUS712US712&ion=1&espv=2&ie=UTF-8#q=evidence%20of%20descrimination%20of%20in%20listings%20descriptions%20housing%20apartment

* https://www.google.com/webhp?sourceid=chrome-instant&rlz=1C5CHFA_enUS712US712&ion=1&espv=2&ie=UTF-8#q=housing%20description%20words%20descrimination

Data collection tools, tricks and techniques:

CRAIG’S LIST:
​
Here is the final example for syntax to use when retrieving data from Craig’s List via manual http calls: https://boston.craigslist.org/search/gbs/abo?format=rss&query=%22no%20students%22

%22 = “ within URI
GBS = Boston/Cambridge/Brookline Subregion of Boston
ABO = Apartments [listed] by Owner

Here is a Craigslist Rental “API”: http://www.programmableweb.com/api/rentrent

Here is another option for getting apartment listings: http://YOURCITY.craigslist.org/search/sss?format=rss&query=SearchString

Here is a client library for searching, retrieving and creating Craigslist.com listings
* https://implicit.harvard.edu/implicit/
* https://www.npmjs.com/package/node-craigslist

Here is a rough syntax for retreiving more fine grained data from Craig’s List:
* http://boston.craigslist.org/search/gbs/roo?format=rss&query=%22no%20students%22

​Here is an “Online RSS Xml Viewer Tool” to view rss xml, convert rss xml to Json, rss to html Online RSS tool to help you view rss feed data into xml, Convert Rss feed url to xml. Rss xml to Html viewer and Convert rss feed xml to json Important because a lot of the data we are able to get with housing listings sources from RSS feeds and we need a quick way to collect and wrangle the content into shape for quick human scan as well as machine parsing.
* http://codebeautify.org/rssviewer
​

AirBnB Data API:
​
* Good overview of AirBnB API: http://lancenewman.me/fetching-airbnbs-listings-with-their-api/
* Comprehensive and detailed Airbnb Data API explanation: http://airbnbapi.org/
* Data: http://nerds.airbnb.com/data/
* Open Source: http://airbnb.io/projects/
* Nice Library: https://github.com/phamtrisi/airapi
​

Here is a nice AirBNB api library with NodeJS: https://github.com/phamtrisi/airapi

Here is an another link for airapi:
* https://slack-files.com/T0556DP9Y-F3C71DYKH-381977c245

Here is a pattern discovered by some iterative manual searching — frequently found under house rules on AirBNB: “May not be safe or suitable for children (0–12 years)”

Machine Learning Libraries and Other Resources

Here is a GitHub project with some relevant code for programmatically and statistically working with housing listings (the underlying use case is analytics on pricing): https://gist.github.com/jpotts18/dac94dc9514172ce020c

Here is a link to Tensor Flow in Node:
https://github.com/nikhilk/node-tensorflow

Here are more relevant resources: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2781297 and Github repos for the python scraping & national analysis: https://github.com/ual/rental-listings

Here is another nice piece on using ML for working with data of this type: http://dl.acm.org/citation.cfm?id=2776032
(the trick will be training the ML for the much more nuanced situations and circumstances of discrimination instead of economic price modeling)

Original Description of Project Challenge

From: https://codeforboston.hackpad.com/Discriminatory-Housing-Brainstorm-qqXfX9SAPfa

Discriminatory Housing Brainstorm**

Get data from CoB Fair Housing Complaint forms
Build up a set of discriminatory coded language examples
Examine specific realty websites: Chestnut Hill, Maven, etc.
Question: Is there a distinction between realtors and roommates looking to fill a room?
Question: How can
Q: Is the model of discriminatory language going to be based off of user-reported language, or some pre-existing set of language?
Q: Do we have pre-selected sites that we’d like to focus attention on?
Q: How do you account for edge cases such as neighborhoods with a historical ethnic or cultural heritage?
Q: Can we reach out to Craigslist to provide their flagged listings? Examples of abuse/discrimination?
Q: Is it easier to keep track of discrimination by address not owner?
Q: Is this about enforcing penalties on abusive landlords or about providing resources to renters?
Q: Who is the actual audience? Landlords? Renters? Both?
Is it about providing services, preventing discrimination?
Q: Does a website or service count as a broker?
Q: Is there anything the Equal Housing badge means in this case?
Q: What are the limits of the language that you’re looking to search for? Just legal terms? Or coded language as well?
AND what actions can you take for potential discrimination that falls into a questionable grey area?
Q: Are we looking for one-offs or frequency of discrimination?

Applied Legal Analytics Rapid Prototyping was originally published in ComputationalLaw.org on Medium, where people are continuing the conversation by highlighting and responding to this story.