Arlo is a web-based risk-limiting audit (RLA) tool used to conduct post-election audits in the United States. The tool helps election officials complete a statistically valid audit of vote tabulation processes by comparing the votes marked on a random sample of original paper ballots with the electronically recorded votes for those same ballots. This type of audit can confirm that the reported winner did indeed win, or correct the outcome through a full hand recount if the reported outcome cannot be confirmed.

As part of the audit, Arlo:

• Uses basic election data to determine how many ballots should be examined

• Randomly selects individual ballots to be examined from a list of all ballots cast in particular contest(s), and provides auditors with the information they need to find those ballots in storage

• Provides supplemental materials necessary to maintain chain of custory while retrieving ballots

• Checks whether votes recorded by auditors examining each ballot match what we would expect if the reported outcome is correct, more specifically whether the desired risk-limit has been achieved based on these results

  • If not, randomly selects additional ballots to expand the sample size and continue the audit, up to a full hand recount if necessary

• Provides monitoring & reporting so that election officials and public observers can follow the progress and outcome of the audit

Arlo currently supports ballot polling risk-limiting audits of single or multi-winner plurality contests. Only one targeted contest is supported at this time, although mutliple instances of the tool may also be run in parallel if multiple targeted contests are desired. If multiple jurisdictions are participating in the audit, ballot manifests and vote/ballot totals for each jurisdiction must be manually combined. Votes from individual audited ballots are currently recorded & tallied manually, then entered into the tool as totals.

Arlo uses the BRAVO ballot polling method of measuring risk and estimating how many ballots need to be examined (Lindeman et al, 2012).

Random sampling of ballots is done using Rivest's Consistent Sampler.

To run a basic ballot polling audit you will need:

• Election name (string, e.g. "November 2019 General Election)

• Targeted contest name (string, e.g. "Constitutional Amendment 1a")

• Candidate/choice names for the targeted contest (strings, e.g. "Ann Marie Smith" or "Yes/Approve")

• Vote totals for each candidate/choice (integers, e.g. "453")

• Total ballot cards/pages cast (integer, e.g. "10023")

  • Note that this is total ballot cards/pages rather than total ballots - if you have a single-page ballot those numbers will be the same, but if you have a multi-page ballot each page is counted individually. For example, if 1000 two-page ballots are cast, the total ballot card/page count is 2000.

• Ballot manifest file, listing all the batches of ballots in storage and how many ballot pages/cards are stored in each batch (see file format requirements below)

The Ballot Manifest file must:

• Be a comma-separated file saved in .csv format

• Contain a header row with two column headers, labeled "Batch Name" and "Number of Ballots"

Note that batch names are strings with no required naming conventions - use whatever names or IDs are normally associated with your ballot storage batches/containers. "Number of Ballots" should be an integer, and is the number of ballot pages/cards in each ballot storage batch.

Ongoing development is planned to support:

• Ballot-by-ballot data entry

• Multiple targeted contests

• Distributed multi-jurisdiction contests

• Batch comparison RLAs

• Ballot comparison RLAs

• Public audit dashboard

• Additional election types (proportional contests, etc.)

• More efficient statitstical methods

Arlo is open-source software (AGPL v3.0), meaning you are free to use it, modify it, and redistribute those modifications as you'd like, provided that, when you redistribute your modifications, you share them in the same open way. Because Arlo is open-source, anyone can review it or run their own copy, thus ensuring that, when used in a real audit, it is performing according to specification.

Like any open-source software, Arlo welcomes suggested changes in the form of pull requests on GitHub. If you're interested in getting a change merged into Arlo, please consider the following:

• test coverage is mandatory. We won't merge code without it.

• significant / risky changes may take some time to review, and are not likely to be merged unless they've been discussed first. The stability of Arlo is a prime concern. A good way to start a conversation around a large change is by opening up a ticket.

• we really want to know about anything that gets in the way of installing and using Arlo. Please file tickets, suggest changes to our installation instructions, etc.

Before submitting a pull request, please review our Contribution Guidelines.

Auth0 is used for authentication, as documented at Auth0.

Arlo is configured mostly through environment variables:

• FLASK_ENV: environment for the Flask server
• DATABASE_URL: PostgreSQL database url, e.g. postgresql://localhost:5342/arlo.
• ARLO_SESSION_SECRET: the secret key used to encrypt/auth client-side cookie sessions
• ARLO_HTTP_ORIGIN: the proper HTTP/HTTPS origin where this Arlo server is running, e.g. https://arlo.example.com:8443 (as any web origin, no trailing slash)
• ARLO_AUDITADMIN_AUTH0_BASE_URL, ARLO_AUDITADMIN_AUTH0_CLIENT_ID, ARLO_AUDITADMIN_AUTH0_CLIENT_SECRET: base url, client id, and client secret for the auth0 app used for audit admins.
• ARLO_JURISDICTIONADMIN_AUTH0_BASE_URL, ARLO_JURISDICTIONADMIN_AUTH0_CLIENT_ID, ARLO_JURISDICTIONADMIN_AUTH0_CLIENT_SECRET: base url, client id, and client secret for the auth0 app used for jurisdiction admins.

Rather than manually config the environment, you can also run the setup script discussed below.

Organizations are, for example, the State of Massachusetts. Administrators are individual users that administer audits for an organization. All authentication is done via auth0 with email addresses, so users in the Arlo database also need to be mirrored in the appropriate auth0 tenant user database.

To create an organization in the database:

pipenv run python -m scripts.create-org <org_name>

which returns the organization_id.

Then, to create an administrator for the organization:

pipenv run python -m scripts.create-admin <org_id> <admin_email>

which returns the user_id.

If you're upgrading Arlo, right now the only way is to destroy and recreate the database. The easiest way to do that, if your database connection is set up properly, is:

make resetdb

If you're running on Heroku or on another system where database destruction and recreation cannot be done from Python, because the database is provisioned externally, the steps are then:

• clear the database, e.g. on heroku heroku pg:reset -a <app_name>
• create just the data model, e.g. on heroku heroku run -a <app_name> python -m scripts.resetdb --skip-db-creation
• you may need to restart servers, e.g. on heroku heroku restart -a <app_name>

We recommend Ubuntu 18.0.4.

• Install Node10. See https://joshtronic.com/2018/05/08/how-to-install-nodejs-10-on-ubuntu-1804-lts/
• make dev-environment or, if you prefer, look at individual make tasks like deps, initdevdb, install-development, and resetdb
• bash ./run-dev.sh

If you would just like to run Arlo and do not wish to setup a custom configuration, you can run pipenv run python -m scripts.setup-dev, which provides interactive configuration. The script optionally installs VotingWorks' nOAuth locally, runs it, and configures Arlo to use it. It creates the necessary audit administrator and jurisdiction administrator credentials discussed above, and launches a dev instance of Arlo. Once you have navigated to localhost:3000 in your broswer, you should be able to log in as an audit admin using the credentials you configured earlier in the script.

• Postgres is best installed by grabbing postgresql-server-dev-10 and postgresql-client-10.
• psychopg2 has known issues depending on your install (see, e.g., here). If you run into issues, switch psychopg2 to psychopg2-binary in the Pipfile
• pipenv install can hang attempting to get a lock on the packages it's installing. To get around this, add the --skip-lock flag in the Makefile (the first line should be pipenv install --skip-lock).
• A password may have to be set in the DATABASE_URL env var depending on your install of postgres. To do this, change postgresql://postgres@localhost:5432/arlo to postgresql://postgres:{PASSWORD}@localhost:5432/arlo, replacing {PASSWORD} with the password.
• You may need to create arlo and arlo-test databases manually via postgres.
• If you run into the error fe_sendauth: no password supplied when running make dev-environment, it means there's no password set for the default postgres user. You can change the postgres authentication method to not require a password by editing /etc/postgresql/10/main/pg_hba.conf and changing md5 to trust for both the IPv4 and IPv6 local connections settings, and then restart postgres via sudo systemctl restart postgresql.

To run the tests all the way through, use these commands:

• make resettestdb (to reset the testdb)
• make test-server or make test-server-coverage
• make test-client
• ./client/run-cypress-tests.sh

To run tests while developing, you can use these commands to make things more interactive:

• Server tests: pipenv run pytest (you can add flags - e.g. -k <pattern> only runs tests that match the pattern, -n auto to run the tests in parallel)
• Client tests: yarn --cwd client test (runs interactive test CLI)
• End-to-end tests: first run FLASK_ENV=test ./run-dev.sh to run the server, then, in a separate shell, run yarn --cwd client run cypress open (opens the Cypress test app for interactive test running/debugging)