There are three reasons:
1 - We were inspired by the Toblerone Affair, a case in which a Swedish politician was pushed to resign after being caught paying simple Toblerone chocolate bar with public money. That’s what we want to do: empower social control of public expenditures including values as low as a chocolate bar.
2 - It sounds like a nonsense operation name typical from the Brazilian Federal Police, and that’s very cool.
3 - It literally means love serenade — so this is our love serenade to Brazil.
We are a group of technology enthusiasts. The project started as the data scientist Irio Musskopf’s initiative, it has grown and today it has a life of its own. Every code is open, and now we have a team of 10 professionals, plus hundreds of volunteers: collaborators from all over the world, including journalists and researchers.
Our team is spread across different cities and countries. We benefit from technology to host meetings, discuss the project, and keep up with the work remotely, without needing a physical venue to gather.
Open source is a term used to describe open source code distribution, in which everyone interested in contributing to the evolution of the project can freely add their ideas without paying for license or intellectual property. In other words, Serenata is free for anyone to access.
We use public data - whether data made public by the Access to Information Law (LAI in the Portuguese acronym), or private data publicly available from companies such as Google, Foursquare, Yelp etc. In the public sphere we obtain data from the Chamber of Deputies, Federal Revenue, Brazilian Government Transparency Portal, data.gov.br etc.
The Quota for Exercise of Parliamentary Activity (CEAP in the acronym in Portuguese) is a monthly amount of up to R$ 45 thousand that each Chamber of Deputies Member is entitled to reimbursement for expenses that are not fit for public bidding. A lunch or taxi payment, for example.
Good question! Rosie, our robot, identifies suspicions, that is to say, outlier behaviors. She achieves that using algorithms in a fully automatic way. She observes spending that does not follow what was expected for a given context, such as a very high expenditure on a single meal, arguably something suspicious. But not everything Rosie finds is necessarily irregular. After she lists her suspicions a human is needed in order to confirm or decline her findings. A irregularity is anything that goes against what is written in the norms, regulations and laws: overpriced expenses, food payments to third parties and other things you might have seen here and there.
Have you noticed that parliamentarians change parties quite often? The is why we avoid indexes and classification of data based on parties of our congresspeople. In addition, our main goal is information, not setting a witch-hunt, promoting hate speech or polarizing between right and left.
We're currently using English, in a Telegram open group. You are very welcome to join him.
We have a community created around the project that goes beyond Brazil – that is to say, people from other countries interested in contributing. Within the GitHub repository and the Telegram group we chose to use the English language so we can include the point of view of these people. In addition, several people from other countries have shown interest in our code since day zero. Lastly, by keeping the code and all technical documentation in English we make it easy for our effort to be used in other countries too.
Jarbas is the web platform to visualize data Rosie, our robot, uses and generates. Jarbas is crucial for the human investigative work that follows Rosie’s task. Lastly Jarbas was created mostly as an internal tool and gradually we are investing in his UX and UI to transform him in a great public utility tool.
Rosie is our robot. She has been programmed to identify suspicious uses of public expenditure, starting with CEAP. She analyzes every reimbursement claimed by our congresspeople telling us what are the reasons that make some of them suspicious.
Rosie is a piece of software we call a robot because it performs its work in an autonomous and automatic way. In other words, someone coded Rosie and "put her to work". Next she handles all the tasks by herself: download available data from the sources, analyzes it, runs algorithms (hypotheses) and finally identifies suspicions. Because she is a computer program she can be installed by anyone who has intermediate skills in tech, but she does not have a face or looks like any humanoid we see in movies.
Unfortunately, we have not started to act in smaller areas yet, and that is due to technical limitations: we do not have a team to scale the work and there is little standardization of data in the municipal spheres. This means that we would have to officially request data and study the structure of the each of the datasets gathered, for each of the cities involved.
Sure, this idea is awesome — and also this idea is one of the main purposes of our adoption of open source.
Have doubts? Ask in our Facebook page or find us on Twitter. For technical issues you can drop a line in English at our GitHub or our technical group on Telegram. If none of this works, send us an e-mail