Coding for the Canada Energy Regulator

As promised, here are some excellent podcasts for jumping into libraries through tech, building indigenous-centric systems, minecraft, open data, virtual bowling leagues, metadata and more. Have fun!

-Roan

As a Code for Canada fellow at the Canada Energy Regulator, I am part of a team which is redesigning a key energy database which contains historical energy, environmental, Indigenous, and energy regulation information for the nation. Much AI is embedded in it's functioning and it relies heavily on an accurate timeline of events. For example, over 50 years of permafrost data for our tundra lies in this database, it must be accurate over time for us to track changes in the climate throughout these decades. In my work, one key component of AI policy development which hasn't been touched on here is the government's role in data ethnography which refers to the analysis of how people live with data over time.

All AI algorithms are trained with datasets; these are, by necessity, datasets that have been collected from our past. For example, copyright licenses expire after 50 years or more and enter the public domain via open data. This data from books, art, and more, has a history and with it, the many biases of that history, which we may not want to repeat itself. This issue become apparent when we train assistant bots, such as Siri and Alexa, with female voices or when Google images correlates gorillas with African faces. Using datasets from our past, repeats that past. Moreover, this data is incomplete and is always changing--this is concerning given that data forms the basis of many policy decisions. Understanding where that data comes from, where it has traveled, and its general behavior is key to trusting the data. Measuring this path, not only ensures decision-making is conducted in an informed way, but lays the groundwork for assessing algorithmic impact. It gives us a way to interrogate the data and find answers informed by the limits of the data. 

To put words to this ethnographic analysis of AI and algorithms, the "Max Planck Institute for Human Development, proposes a radical idea: the best way to understand them is to observe their behavior in the wild. In Nature Magazine, Rahwan (and 22 colleagues) calls for the inauguration of a new field of science called “machine behavior.” (https://blog.experientia.com/the-anthropologist-of-artificial-intelligence/). 

Terms are yet to be created to define this ethnographic analysis of AI and algorithms, machine behavior is only one way to describe it. Another approach is referring to this kind of data as "thick data", in which "ethnography and big data analytics can work together to provide a more comprehensive picture of big data, and can thus, generate more societal value together than each approach on its own." (https://link.springer.com/chapter/10.1007/978-3-319-93061-9_2_) To me, this combination asks, what of machine sociology? In what ways are algorithms socialized? How does this impact our decision-making on an individual basis, as a society, as governments? In what ways can we educate, but also facilitate the public's access to AI and AI algorithms, can they create their own AIs with their own data? 

In Estonia, citizens have access to a portal with their own data which they can access at anytime. This record of their data can behave as a governmental ethnographic analysis of each citizen's data, upon which informed decisions can be made, policy or otherwise. David Eaves of Harvard Kennedy School, in his parliamentary address as evidence in Canada earlier this year, stated " easy to pull disparate information about a citizen all together to get a very clear view about who that person is, and then to offer that information to different parts of government as it's trying to do its service.  This is very different from many other countries...". (https://www.ourcommons.ca/DocumentViewer/en/42-1/ETHI/meeting-135/evidence)

I'd venture to say that this is just one way to do this, we have yet to re-imagine what this can look like in other countries, in other government contexts, in our many futures as a global society. 

Lequanne

__________

Our sense of play and embrace of creativity has lead to a week of unexpected wins with far reaching impacts! Sometimes the best ideas come from having fun and not taking ourselves too seriously, this has proven fruitful  for us in many ways in our work at the Canada Energy Regulator (CER).

Using NFC to increase discoverability of REGDOCs GIS data

On a fateful, chill day this week, we were discussing random ideas that would be interestingly artsy when past work using NFC arose in the discussion. What if discoverability can mean beyond the screen? What are new ways to interact with REGDOCS beyond a website? Excited, Lequanne happened to have NFC tags at home and brought it into work the next day. The idea built over the week into a geotag concept in which landowners can tap the tag for a proximity search to determine what land, pipelines, conversations surround them so they are informed before contacting the CER directly.

Connecting engagement, prototyping and testing in the public sector

From Toronto to Ottawa to Montreal, we’ve linked with Statistics Canada to explore our prototypes, test, and hack at the Data Challenge for university students in these cities. It’s an event where “ Various Government of Canada departments submit real-life scenarios to solve...The scenarios [are] diverse and include anything from establishing new processes to acquiring data, to creating a cloud-based working space, to developing a statistical dashboard, etc.” With over 8, 000 applications last year, we’re excited to co-create and engage fellow young people with REGDOCS data over the coming months! Since we know, as a team of three people, we can’t reach everyone across Canada, we’ll be using digital technologies such as Bang The Table and Github to ensure we work in the open.

Increasing design research capacity of our fellow CER colleagues

As we build our research and engagement plan, our reach is expanding across Canada as it should. Beyond Toronto, Ottawa, and Montreal, we’ve also planned to go to Vancouver, and Yellowknife. As a national organization, there are many stories, ideas, and insights with the Canadian people which are coloured by their lived experiences. The principles of the CER include inclusivity and participation, which we, are embodying and modelling as Code for Canada fellows. So much so, that our approach is inspiring new ways of working such as including our fellow colleagues on these travels to learn more about these stories and perspectives, to develop empathy and co-create with the public, to be part of the changing ever evolving role of a energy regulator and it’s data.

Connecting with the library  

Inspired by Indigenous knowledge and respecting the nature of their relationship with the CER, we’ve connected with the library in various ways. Notably, we’ve learned that the CER receives many artifacts and gifts as part for their relationship. These are respectfully housed in the library, but are not displayed in a way which explains the history of how these gifts were given. The library is exploring how to do this and, inspired by our conversations around interaction design, are open to exploring how to use our digital approaches in an analog way. We’ve only just begun walking on this path with the library, but we are happily skipping along!

Littletree, an Indigenous librarian and researcher of Indigenous knowledge at X̱wi7x̱wa library, said. On top of differing categorization methods, she said, the systems also can’t easily incorporate Native American languages that use non-Roman characters in the spelling of certain words.

From our challenges to our triumphs, the Code for Canada fellows for this cohort e-met to learn from each other, share laughs and reinvigorate the fire.