It’s the French presidential election this weekend. Expect the unexpected.

A lot of people went to bed on the night of November 8, 2016, confident that they would wake up to news of President-elect Hillary Clinton’s election victory. This is not, of course, what happened. Donald Trump’s stunning win confounded pollsters and pundits alike, much as had the UK’s decision to Brexit months earlier.

Political unpredictability has continued apace in 2017, and France’s presidential election represents an early test of whether the nationalistic tremors of 2016 will continue to haunt liberal democracy in its heartlands. One thing’s for sure: no-one can confidently predict the results of this contest. But to borrow a phrase, there are some known unknowns to brush up on ahead of time.

How does France elect a president?

Presidential elections in France are a two-step process, with the top two candidates from this Sunday’s first round progressing to a head-to-head run-off a fortnight later – which means that, whatever happens, we won’t know who’ll become France’s next president on Sunday, but we know who won’t. The two-stage system aside, the process is quite straightforward – the candidates with the largest vote totals progress.

So who’s in the running?

In recent years, two main parties have dominated French politics: the left-wing Socialists, and the center-right Republicans. The current president is Socialist François Hollande, who announced last year that he would not seek re-election, in large part due to staggeringly low approval ratings, which hit an eye-popping low of 4% (not a typo!) late last year.

As in any election without the incumbent running, the field is wide open this year – albeit to an extent unprecedented in French politics, for several reasons. First, with or without Hollande, France’s Socialist party is in disarray: similar to Democrats in the US and the UK’s Labour Party, it is riven by in-fighting between left wing forces and more centrist impulses.

This has served to fragment the electorate. The official Socialist candidate, Benoît Hamon, is struggling under the weight of his predecessor’s unpopularity. Meanwhile, two former Socialist ministers, Jean-Luc Mélenchon and Emmanuel Macron, have each seized chunks of the party’s traditional vote from the left and the right with their own new movements, Unsubmissive France and En Marche!, respectively.

The right wing has also splintered. The National Front, led by Marine Le Pen – the daughter of Jean-Marie Le Pen, who scored a place in the run-off election in 2002 – has seized on antipathy towards Muslim immigrants to lead many first-round polls this year. Meanwhile François Fillon, the Republican candidate, is riven by allegations of financial impropriety relating to salaries given to members of his family, which have threatened to upend his candidacy.

OK, so it’s a broad field. But who’s going to win?

It’s very hard to predict who will make it through to the run-off – let alone win – but right now there are four viable candidates who, polls suggest, each clustered at between 18% and 23% of the vote. Mélenchon, with a radically leftist agenda, has been gaining recently at the expense of Hamon, and is running at around 18%, for what would be a close-fought fourth. Fillon has resisted calls – even from among his own party – to drop out, and his support seems to have stabilized, as he is currently running in a close third. Macron and Le Pen, meanwhile, have been trading the lead for the last few weeks, with each averaging around 23% of the vote.

The state of the French presidential polls at the time of writing. (Wikipedia)

The results of the run-off will depend, of course, on who makes it through. As things stand, one candidate – Macron – would win his head-to-head with each of the other three viable candidates, while another – Le Pen – would lose all of hers. (Mélenchon beats Fillon in the least-likely match-up.) But polling the run-off accurately is difficult while other candidates remain in the race, particularly when three out of the four leading candidates represent parties who have never won the presidency. In particular, if Fillon continues his comeback and makes it through to the run-off against Le Pen, the achingly familiar prospect of an experienced but scandal-plagued establishment candidate losing to a xenophobic outsider seems plausible.

How important is this election?

In a word, very. In constitutional terms, the French presidency represents something of a middle way between a mostly-symbolic head of state like the German presidency, and the powerful executive in the American system. Compared with America, periods of “cohabitation” – where one party controls parliament while another occupies the presidency – have been relatively rare, and in these instances, the president tends to take a back seat.

But one area in which French presidents have the most control is in foreign affairs, and France’s election this year represents, in a certain sense, another referendum on the European Union. Only the far-right Le Pen has vowed to leave the Euro currency, but both she and Mélenchon have adopted the fateful promise made by Britain’s David Cameron to renegotiate France’s relationship with the EU and put the resulting settlement to a formal referendum vote.

Meanwhile, Le Pen and Russian President Vladimir Putin have been open about their mutual respect, and Fillon is also notably more comfortable with Russia than other Republican figures – while Macron, the only avowedly pro-European candidate has been hit with a barrage of cyberattacks and fake news. Again all this sounds familiar, perhaps that’s because it is.

Both politically and geographically, France is much more central to the European project than Britain ever was, so a rebuke by voters would represent a much more existential threat to the Union – creating precisely the kind of instability that Russia’s Putin is said to want.

What else is there to know?

The polls are changing daily, and the election is now too close to call, according per multiple outlets. While this uncertainty creates volatility, in everything from markets to geopolitics, at least pundits and the public alike are more prepared for multiple outcomes than they were on the mornings of June 24 and November 9, 2016, when British and American voters created political earthquakes. It’s always useful to know what you don’t know.

Waymo, Otto, Uber, Google: A Lawsuit in Need of an Explanation

Amongst the many stories of Uber’s recent controversies, you may have heard of a lawsuit between Waymo and Uber. The case centers on intellectual property infringement, which is already a complicated, technical issue on its own when you break down how a judge or jury determines if the technology is infringing on a patent.

Let’s break down just what’s going on in this situation:

What is Waymo?

To understand what Waymo is, we need to go back to Google Co-Founder Larry Page’s open letter in which he announced that Google would become a subsidiary of Alphabet Inc., a holding company that will be the parent company for several of the company’s endeavors. This includes X, the company’s “moonshot factory” or investment lab, GV and CapitalG, the company’s two investment arms, and Waymo, the company’s self-driving car project that spun out of X.

Waymo began as a self-driving car research project at X (then called Google X) in 2009. The name Waymo, which Alphabet unveiled in 2016, is short for “a new way forward in mobility.”

Seems like a weird phrase to shorthand.

Yes, yes it does.

Let’s go back to this Alphabet company. Why was it created and did it replace Google?

Google still exists and includes everything you would associate with the brand: search, ads, YouTube, Maps, Chrome, and Android. But now it’s one of several entities under the Alphabet umbrella.

When it comes to the why, that’s a bit trickier to explain. There’s plenty of speculation that the creation of Alphabet is all about improving visibility into the company’s operations and revenue breakdown for investors. Each of these companies now operates independently, with separate budgets and revenue that are reported out in the quarterly earnings the company is required to file by the US Securities and Exchange Commission, the the governing body of the US financial markets.

So, back to Waymo. Why was it spun out of X?

Companies are spun out of X when they’ve moved past the research stage and are ready for commercialization. If Alphabet is confident the company has a sound business model and product that’s ready for the market, it’s moved out of X to become a stand-alone company. According to Waymo CEO John Krafcik, “what you’re feeling from the Waymo team is confidence that we can bring this [technology] to [people].”

How exactly does Uber fit into all of this?

Uber, like many other technology and automotive companies, is developing self-driving car technology of its own as part of its Advanced Technologies Group. CEO Travis Kalanik first began recruiting engineers for the project in Pittsburgh in late 2014.

Pittsburgh seems pretty random.

It’s not. It’s home to Carnegie Melon University (CMU), which has a well-respected robotics department where many of the top experts in the field spent time conducting research. Originally, Uber partnered with CMU’s National Robotics Engineering Center to develop the technology. Then, Uber poached about 50 people from CMU, which was about one third of the Center’s researchers.

Wait, Uber stole all of the workers away from its partner?

That’s a story on it’s own. Let’s just say it was not a popular move in the technology community.

Did Uber’s technology from the CMU partnership infringe on Waymo’s patents?

No. The patent infringement issue starts with Otto, a startup that was developing self-driving technology for trucks. The company was founded by former Google employees Anthony Levandowski, Lior Ron, Don Burnette, and Claire Delaunay. Levandowski formerly led Google’s self-driving car project and incorporated Otto two weeks after leaving the search giant. The team first announced the existence of the company in May 2016. Only three months later, Uber acquired Otto for $680 million.

Why did Uber acquire Otto? And when are we getting to the patent infringement?

We’re finally getting there. There are a number of reasons Uber bought Otto, including its relationship to car manufacturers, its talent, and its technology. That technology includes LiDAR, or light detection and ranging. LiDAR works by using lasers to detect objects, space, and anything else in an environment by tracking how long it takes for the laser to hit the object and bounce back to create a 3D map. It’s a mechanical form of echolocation. The technology is used for autonomous guided vehicles (aka self-driving cars) to detect everything on and around the road, from other vehicles to obstructions.

This is the technology that Waymo is suing Uber over.

So why does Waymo think Uber is infringing on the LiDAR technology? And when did they file the lawsuit?

The lawsuit began in February. According to Waymo, it all started with an email: “One of our suppliers specializing in LiDAR components sent us an attachment (apparently inadvertently) of machine drawings of what was purported to be Uber’s LiDAR circuit board — except its design bore a striking resemblance to Waymo’s unique LiDAR design,” the company announced in a Medium post.

That email sparked an investigation by Waymo, which eventually led the company to discover that a month and a half before he resigned, Levandowski downloaded more than 14,000 proprietary files, including the designs of the company’s LiDAR technology and circuit board. Waymo also claims that other former Google employees downloaded confidential information about suppliers and manufacturing. The full filing goes into details about just what these employees supposedly stole.

In the filing, Waymo asked the court for an injunction against Uber’s self-driving car program. Translation: Waymo wants to stop Uber from continuing to work on the technology.

How did Uber respond?

Uber released a statement to Business Insider on February 24th denying all allegations, claiming the lawsuit is “a baseless attempt to slow down a competitor.”

Levandowski also released a response of sorts. The Otto founder exercised his Fifth Amendment right to avoid self incrimination on March 30th. He also hired his own criminal counsel for the suit, though he is not formally named in Waymo’s filings.

Uber officially filed its formal response to the lawsuit on April 7th, which included details about the differences in the two company’s LiDAR technologies, the lack of evidence around the 14,000 files.

Did Waymo respond?

Waymo originally claimed that Uber failed to disclose the proper documents related to the lawsuit on April 3rd. Waymo asked Judge William Alsup to compel Uber to produce all of the documents or assume the company is hiding documents.

Waymo also reiterated its claims in its response, saying “Uber’s assertion that they’ve never touched the 14,000 stolen files is disingenuous at best, given their refusal to look in the most obvious place: the computers and devices owned by the head of their self-driving program.”

So where does the lawsuit stand now?

The last update with the lawsuit has to do with Levandowski’s Fifth Amendment claim. William Alsup, the judge presiding over the case, rejected Levandowski’s request and ordered Uber to disclose documents created by a third party when it conducted due diligence for the acquisition of Otto. The due diligence report must be included without any redactions related to Levandowski in a “privilege log,” which is a document a party in a lawsuit produces that they do not think should be opened in court because of the proprietary nature of the material.

Waymo filed the last update in the case, filing an opposition request over Uber’s motion to keep the dispute private by going into arbitration. Uber originally filed for arbitration because of Alphabet’s employee agreements state that any disputes with the company should be settled in arbitration. But Waymo believes this is not a valid claim, as Levandowski is not the defendant in the case, Uber is.

So what now?

Prepare for a long lawsuit filled with more filings and claims. Unless some settlement is reached, the case could be dragged out for months.

The American Community Survey – 3 in 1: explainer, engagement, data story

I have thought about creating a census fan page many times. Looking at data all day makes one appreciate the history, scale, and effort of this massive public endeavor. Not only does the census provide official guidance to the formulation of public funding and policy, it has over the years also ritualistically structured our understanding of our environment. Since 1790, the census evolved not just to adapt to the massive increase in population(from under 4 million to 318 million today) and migration(from 5.1% urban to 81% in 2000), but its format has also changed to reflect our attitudes. In this 3 part(hopefully) assignment/makeup assignments, I focused on explaining and visualizing the American Community Survey(ACS), a newer data offering of the census that is a yearly long form survey for a 1% sample of the population.

Last summer, while interning at a newsroom, I built a twitter bot based on the ACS inspired by how nuanced and evocative the original collected format of the dataset is. Each tweet is a person’s data reconstituted into a mini bio. In the year since, people have retweeted when an entry is absurd or sad, but most often when an entry reminded them of themselves or someone they know. It quickly became clear that narratives are more digestible than data plotted on a map. However, I was at a loss on how to further this line of inquiry to include more data in bigger narratives.

Part of my research is to experiment with ways of making public data accessible so that individuals can make small incremental changes to improve their own environment. Many of these small daily decisions are driven by public data, but making the underlying data public is not always enough. While still plotting data on maps regularly, I started to think about narratives. Can algorithmically constructed narratives and narrative visualizations stand alone as long-form creative nonfiction?

There are so many wonderful public data projects that go the extra step out there. Socialexplorer does a great job of aggregating the data, so does actually ancestry.com. Projects from timeLab show many examples of how census data has been used for a variety of purposes, even entertainment. And just last week, the macroconnections group unveiled a beautiful and massive effort to expose public datasets with datausa.io that takes data all the way into a story presentation.

Constraints are blessings…

It’s fortunate that I work in such a time and environment but also very intimidating. What can I contribute to an already rich body of work where each endeavor normally requires many hours and even months of teamwork, not to mention the variety of skills involved? More selfishly, what can visual artists add to the conversation that is beyond simply dressing up the results? This series of 3 assignments is a start.

1. Explainer – the evolution of the census

Instead of focusing on how the population has changed, here is a visualization of how census questions have changed to reflect the attitudes and needs of the times. Unfortunately this was unfinished and only goes from 1790 to 1840 right now.

 

1790_1840view closeups here – 1790_1840

2. Engagement – how special are you?

I have been procrastinating by spending a lot of time on guessing the correlation. I think that buzzfeed-type quizzes are one of the best data collection tools. Of course there is also this incredible NYT series. People who commented on the census bot often directly address tweets that describe themselves. This is an experiment to get people to learn something about the data by allowing them to place themselves in it.

Screen Shot 2016-04-19 at 9.42.30 PMScreen Shot 2016-04-19 at 9.42.00 PM  This is also still very much in progress: http://jjjiia.github.io/censusquiz/

3. Data Story

To be continued …

What Is A Bot, Anyway?

(with Adrienne)

Bots are having their 15 minutes, so to speak. Recently, Microsoft launched the “Tay” AI bot and chaos ensued. But bots had already been making a name for themselves on Twitter, on Tumblr, and even on collaboration platforms like Slack or Github. But just because we might recognize a bot when we see it, doesn’t help us understand what’s going on. To make the lives of non-coders everywhere easier, we’ve prototyped an app that can create and configure a vertible cornicopia of bots, no code required.

* For those who are interested in a little more detail, we’ve also created a simple example, an activist bot that echoes quotes excerpts from the Boston Police Patrolmen’s Association newsletter which is…unfortunately surprising. 

What is a bot?

Broadly speaking, a bot is computer program that acts like a human user on a social media platform. Though we haven’t yet seen the passing of the Turing Test by any artificial intelligence, so it is pretty easy to distinguish the humans from the code. Essentially, a bot takes in some information or content from source A (or A + B, or A + B + C, or…well you get the idea), and then potentially transforms it based on rules the developer has given it, and saves the newly crafted content to a database. From here, the bot could also have instructions to share their creation on Twitter, but it’s not a requirement.

Minimum Viable Bot is just Information In, Information Out.

What are the different kinds of bots?

Screen Shot 2016-03-30 at 02.45.59

Bots can take lots of different forms depending on their purpose. Some bots can help you schedule meetings through email. Others are more nefarious, and try to circumvent spam filters in your email or on Twitter. Funnily enough, the hugely popular @horse_ebooks started out as a scam bot, until it was taken over by a reporter from Buzzfeed.

We should note that there is no canonized taxonomy, but we’re going to offer a few informal categories here.

Mash Up Bots:
These bots combine different sources of content and post them.
Example: A bot that tweets out a combination headlines.

 

Image Poster Bots:
These bots post an image, sometimes with additional information, or generated content.
Example: A bot that posts live TV stills and improvises subtitles for them.

Smart Learner Bots:
Some bots will grow more “intelligent” the more they are interacted with. Smart learner bots require an extra level of human care, as Microsoft learned with Tay. To learn more about ethics in bot curation, Motherboard just posted a great explainer with some of the leaders in social bot technology
Example:
Microsoft’s ill-fated “Tay”, who “learned” by accepting as valuable everything that was said to it.

 

Auto Notifier Bots:
Auto Notifiers listen to a content source, and then perform an action when new content is posted, or something changes. It’s kind of like If This, Then That, the extremely popular service for connecting various web platforms together. These bots are also very common in journalism. They frequently take template text and “fill in the blanks” with the latest relevant information. 

Our demo bot is a version of this kind of bot, because we are not transforming our text in any way. We are simply waiting for a new newsletter to be posted, and then periodically tweeting sentences from it.
Screen Shot 2016-03-30 at 02.34.11
Example: A twitter bot that tweets each time there is an earthquake near L.A.

 

Replier Bots:
These bots talk to the user based on rules written by the developer. Sometimes this needs to be something the user says directly to the bot, and sometimes these bots will tweet at someone in reaction to something that’s been said. Many platforms (e.g. Twitter) have
rules for keeping these bots on their best behavior.
Example: A bot that takes nouns from your tweets and turns them into tributes to deities.

 

Expert Bots:
Much like the phone trees, these bots may either offer (semi-) useful information, or take responses and decide what to say next based on them. These bots can also sometimes be found on e-commerce sites with services like Live Chat. The bot will help to quickly sort the chatter for a human.
Example: The Bank of America customer service bot.

 

Where do bots live?

  • Email
  • Github
  • Slack
  • Twitter
  • IRC
  • and many more!

How do bots work?

Bots typically have a place where they get their content from. In some cases, this may be a very advanced system. In the case of our demo app and bot we simply feed in a web address pointing to our desired content, and it will post sentence by sentence is located.

Screen Shot 2016-03-30 at 02.42.08

With any program that deals with a large amount of data, most of the work is typically in cleaning up the data so that, e.g. in this case, what the bot says is correct.

Screen Shot 2016-03-30 at 02.42.15

Some bots will try to detect what is relevant in the data you feed it. Some will simply take the data and reproduce it without a second thought. Tay’s “repeat after me” feature did this, to disastrous effect.
Screen Shot 2016-03-30 at 02.44.32

It’s common for one person, once they have acquired the skills, to make and manage many bots! To see this ailment in action, have a look at the work of the wonderful Darius Kazemi!

To end, here is an example of the code that would run our bot that tweets out random sentences from the Boston Police Patrolman Association’s newsletter. This script would typically be set up on a server and run on a schedule.

Screen Shot 2016-03-30 at 01.46.03

It may not have been as complicated as you thought to build your own bot! If you would like an even more automated route, have a look at the article How To Make A Twitter Bot with Google Spreadsheets.

 

How bad does Shelby County discriminates against black businesses?

This bad. (The link is to a YouTube video. And yes, I know the first slide says it’s under 2 minutes but the video is 2:12. That will be updated in later versions.)

I’ve written about the racial disparity in municipal contracting processes – which sounds dry as sh*t, I’d be the first to admit – but it’s actually really important. (Here’s an Infogr.am graphic I created a couple of years ago on the topic.)

The short version: Black people finance their own discrimination when they pay taxes into a county government that then awards an unfair share of county contracts to businesses owned by white men. This has been happening in the county (and city) where I’m from for decades.

My video is an attempt to explain the issue by stressing the consensus values in the middle of Hallin’s Sphere and the deviance of continuing to use tax dollars to give one group an unfair advantage over another.

Here’s the story I was trying to explain. The story includes the numbers I cite, but people who won’t read the story WILL watch a video.

I’d like to redo it and make it snappier, add some sound effects and pictures. Some of the slides with not much text could have been shorter, but there’s no (easy) way in Keynote to vary the length of the slides.

Creating this was a beast. (Pro tip: If you create a Powerpoint, export the slides as JPEGs and import the JPEGs into iMovie, the stills will be so blurry as to be unreadable. A workaround: Use Keynote to create a slideshow, export it as a QuickTime movie and then upload to YouTube or wherever. Or alternately, get a legit video editor like Premiere or Final Cut.)

I think THIS is the future of news. I’d like to create a series of videos like this, ideally under a minute. The series (#MLK50, referenced at the end of the video) will be focused on how public policy reinforces racial/economic injustice in Memphis – and what policies would create a more economically equitable environment.

My “fierce urgency of now” is that in two years, Memphis will mark the 50th anniversary of the violent interruption of Martin Luther King’s vision of economic equality. King came to Memphis to make sure that local government treated mistreated black sanitation workers fairly, but 48 years later, the black community is still getting the short end of the stick.

Refugee Resettlement in the United States

An explainer on the process refugees go through to relocate to the U.S. — a collaboration from Brittany and me…


From Brussels to Paris, the growing number of terror attacks in the West has bled both fear and ignorance around the number of Syrian refugees resettled in the United States. The Republican presidential frontrunner has even gone so far as to pledge that he will send resettled refugees back to Syria if elected. Yet, for all of the hand-wringing about the influx of potential jihadists, official government data tells another story.

Since the Syrian civil war broke out in March of 2011, just under 2,200 refugees have been admitted into the United States. According to the Pew Research Center, of the 70,000 refugees the United States was able to legally accept in the 2015 fiscal year, roughly 25% were from Burma, 20% from Iraq, and 13% from Somalia.  While the Obama Administration will raise the refugee cap to 85,000 to accommodate 10,000 Syrian refugees in 2016, Syrians will still make up less than 12% of the total admitted refugee population. Also, while the average processing time for refugees is 18 to 24 months, Syrian applications can take significantly longer because of security concerns and difficulties in verifying their information. Aid organizations currently put the actual processing time at 33 months.

Rather than just throwing more numbers at the reader, we decided to let he or she engage with the Syrian asylum application process directly via Typeform. A survey with style, easy on the eyes Typeform allows the designer to simulate a conversation through “logic jumps”, which adapts the survey based on a respondent’s answer. Try your hand at the journey here.

My [future] tool: Uliza

I heard the phrase “digital divide” for the first time about six months ago. As someone just sticking their toe in to the larger debate around ICTs, net neutrality, and zero-rating products, it’s been a slightly overwhelming dive down the rabbit hole to say the least. It has also lead me to the tool I’ll be introducing today: Uliza.

What is it?

Uliza, which means “ask” in Swahili, is a telephone service that leverages existing technologies in voice recognition, cloud-computing, and translation to provide access to information for the 4.5 billion people who are off-net or illiterate in a major internet language. It is currently being developed for market in East Africa by a team of graduate students at The Fletcher School, MIT, and UC Berkeley.

How does it work?

Anyone with a phone can call a toll-free number, ask a question in their own language, and receive an answer through an automated service, at no cost.

Caller experience:Uliza caller process Back-end experience:Uliza backend process

Why does it matter?

With only 5% of the world’s languages available on the internet, representing linguistic diversity online continues to be a major challenge. Uliza is one product in growing suite of tools which seek to bridge the information divide between networked and un-networked communities. The original three-person team behind Uliza — who collectively have more than a decade’s worth of experience working in East Africa — chose to roll out Uliza in Kenya due to the high adoption of mobile technology, even among low-income population, growing telecom industry, and a need to scale Swahili-language resources.   

 

 

(How) Can Algorithms be Racist?

Technology can be the ultimate equalizer: once access is provided, it can erase borders, education, race, class. But a new study offers that the same tools that are said to provide a level playing field might also be blind spots.  Are the algorithms that are used to drive images and ads perpetuating human prejudices?  One study says yes. But, how can algorithms (which seem to be based on reason) discriminate?

Flash preview: (How) can algorithms be racist? An illustrated story #doodles #datamining #race #partnews

A video posted by Sophie C (@petit.chou) on

For this assignment, Alicia and I wanted to tackle the issue of bias and discrimination in algorithms in a creative way. Our response is to this short article from the Guardian, “Can Googling be Racist?“.  The Instagram video is a preview of the resulting story, which I plan to scan into a static web-readable series.

To explain, we  supplemented Latanya Sweeney’s research paper with my own knowledge of data mining and algorithms, in a easily-digestable format. One of my biggest gripes as a computer scientist/machine-learner is the assumption that algorithms are either value-free or a mysterious black box. As Mark Twain (might have) said,

“There are three kinds of lies: lies, damned lies, and statistics.”