AI and the Danger of Fake Data

Sapa Profiles, an Oregon-based metals manufacturer, supplied fake data along with its materials to NASA, causing rockets to burst into flames and costing hundreds the agency of millions of dollars. A report alleging fake votes in the recent Indian elections is, in turn, accused of providing fake data. Another report shows cryptocurrency exchanges wildly exaggerate their trading volumes—with fake data. The report says as many as “87% of trading volumes reported by virtual currency exchanges was suspicious.”

In many ways, public knowledge has become simulated reality rather than shared understanding. Jean Baudrillard, a French sociologist and philosopher who died in 2007, wrote Simulacra and Simulation, arguing that public institutions have replaced all reality and meaning with symbols and signs, making human experience more simulation than reality. If that’s true, artificial intelligence must surely make it even more true.

Much has been written about the implications of fake video. “Imagine a jury is watching evidence in your trial,” forensic video expert David Notowitz writes. “A video of the suspect committing murder is playing. The video is clear. The suspect can be identified. His voice is heard. The victim’s mother shouts, “My baby!” The verdict is now a forgone conclusion. He’s convicted and executed. Years later you learn the video of the murder was doctored.” Notowitz notes that we’ve already seen convincing videos of incongruent faces and bodies, engineered through “deep learning,” a type of AI used to create such images. Facebook and Twitter were recently involved in a row involving doctored videos of House Speaker Nancy Pelosi. “Deepfake technology,” Notowitz writes, “is becoming more affordable and accessible.” These systems are improving and rely on “convolutional neural networks,” essentially artificial neurons that learn things.

Of course, it’s even easier for AI to help create fake non-video data on people, in a manner far more sophisticated than the artificial fake data generators available for systems testing. How might bad actors deploy that kind of “deepfake data?” What if large volumes of fake voter demographic or ideological data were to infect political pollsters or messaging strategists in one or another campaign?  What if state and local governments received fake data in environmental impact assessment? Remember, we aren’t just talking about fudged or distorted data, but data created out of whole-cloth.

Calls for a universal code of AI ethics should always include calls for enforcement of provisions—or the development of new ones— against the generation of false data. Each example mentioned here could end up being very high-stakes situations—exploding rockets, financial crashes, and so on.


Companies Big and Small Grapple with Data AI Ethics

Five years ago, in The Data Revolution, Rob Kitchin defined “Big Data Ethics” as the construction of systems of right and wrong in relation to the use of (in particular) personal data. The magnitude of data use, and its effect on things like elections and public policy, might have seemed exotic or quaint in 2014, but now we’re seeing companies like Google, Facebook, and others “setting up institutions to support ethical AI” in relation to data use. Google recently created the “Advanced Technology External Advisory Council” with a mission to steer the company towards “responsible development and use” of artificial intelligence, including facial recognition ethics. The advisors are “academics” from the fields of ethics, public policy, and technical applied AI. Entrepreneur.com also reports that the council includes members from all over the world.

It’s certainly a good time for companies to be conspicuously and conscientiously doing things like this. We’re learning that AI can often inadvertently (a strange word to use in this context) behave in ways that, if humans so behaved, we’d call “conspiratorial” or collusive. The Wall Street Journal recently reported (behind a paywall) on algorithms “colluding” to unfairly raise consumer prices. When competing algorithms received “price maximization goals,” they integrated consumer data to figure out where to raise prices, and out-compete one another in doing so.

But self-governance will always have limits–and those limits are not necessarily attributable to the bad intent of actors in the system. In the case of price “colluding” algorithms, as Andrew White wrote, “[r]etailers have been using neural networks to optimize prices of baskets of good for years, in order to exploit shopping habits.” Advances in AI simply allow the logic of price optimization to run its course without the intervention of retailers’ personal street wisdom about pricing.

And Facebook’s creation of similar advisors seems not to have kept it from asking some new users to provide their email address passwords “as part of the sign-up process,” which is a pretty tremendous failure to read the room by that platform.

And finally, the teeth that these advisory boards have is, of course, always going to be limited by the will of the companies that support them. In a world where people can legitimately reject the monopoly-like holds of Google or Facebook, the findings of such groups would carry some weight, but that’s proving to be practically impossible. In the end, such hopes also ignore the basic paradox that users want their preferences to matter, but are skittish about having their data mined–what some have called the “personalization and privacy paradox.”
The conversation among data scientists may offer the best guide for ethical practices by corporations. Last year Lucy C. Erickson, Natalie Evans Harris, and Meredith M. Lee, three members of Bloomberg’s Data for Good Exchange (D4GX) community, published “It’s Time to Talk About Data Ethics,” where they bring up the “need for a ‘Hippocratic Oath’ for data scientists,” and report on efforts to hold large conferences and symposia soliciting dozens of proposal papers on codes of ethics, from which working principles could be distilled. It’s scientists using something very much like the scientific method to develop ethics for their own methods. Not a bad model.


Four Ways Big Data Can Teach Us About People

Well, we spent almost two billion dollars on political digital ads last year around the world, and that’s a low number compared to what we’ll spend next year. We don’t need to walk through the dozens of articles published every month on the implications of this, except to say that people who think about the social effects of technology are ever-concerned about big data. Systems are so prone to abuse that some progressive governments are regulating them, Spain being the latest example, with its call “for a Data Protection Officer (DPO), a Data Protection Impact Assessment (DPIA) and security measures for processing high risk data” in elections, and its insistence that “for personal data to be used in election campaigning it must have been ‘freely expressed’ – not just with free will but in the strictest sense of an exercise of the fundamental rights to free expression and freedom of political opinion protected by Articles 16 and 20 of the Spanish Constitution.”

So on the bright side, here are four potentially helpful ways we can engage with big data responsibly, reciprocally, and in the public interest.

1. Tracking Local Political Participation.

“In 2018, three BU political scientists used big data to study local political participation in housing and development policy.” They coded “thousands of instances of people who chose to speak about housing development at planning and zoning board meetings in 97 cities and towns in eastern Massachusetts, then matched the participants with voter and property tax data.” Their findings that the conversations tended to be dominated by older white male homeowners instead of being representative of residents in general can help inform activists of the barriers to participation in policy discussions that exist now. “[T]he dynamic,” the researchers conclude, “contributes to the failure of towns to produce a sufficient housing supply. If local politicians hear predominantly from people opposed to a certain issue, it’s logical that they may be persuaded to vote against it, based on what they think their community wants.”

2. Big Data as Ethnographic Tool

This seems counterintuitive because people think big data contributes to the abstraction of political views and lifestyle preferences, but some researchers are concluding something in the other direction, arguing that “[Big data] can be used as a powerful data-recording tool because it stores (…) actual behaviour expressed in a natural environment,” a practice “we normally associate with the naturalistic ideals of ethnography: ‘Studying people in everyday circumstances by ordinary means’.”  We aren’t just learning numbers; we’re seeing how people behave in everyday life.

3. Cognitive Bias Training

I’m including this because it teaches data readers about themselves. The conversation stems from recent attempts at self-correction by Facebook, Google, and other big companies. Web tester Elena Yakimova spoke to former head of Facebook elections integrity operations Yael Eisenstat, who touts “cognitive bias training [as] the key along with time, better Data Science and bigger, cleaner input data” as ways that those who read that data–and ask the questions–can check their own biases while searching for wider and deeper variables to circumvent their own cognitive (and therefore social) biases.

4. Open Data Days

This is the coolest of all the ideas– it’s a way to engage big data to teach people about people.  In Guatemala and Costa Rica, public officials are creating events like open and participatory election surveys where people can not only participate in the questionnaires, but also examine the results; or participate in examining data for participatory budgeting and other municipal functions. Thus, the “For Whom I Vote?” virtual platform has “users fill a questionnaire that measures their preference with parties participating in the electoral process. This allows each user to identify firstly their own ideological position, but also how closely they are with each political party.” It’s all transparent, and participants learn about the process as they participate.

That commitment to openness is a good way to wrap the post. As data practices evolve, there are opportunities for “dissemination of knowledge in free, open and more inclusive ways.”


How Influencers Use Twitter Replies to Build An Audience

Too often it seems that national news headlines are backed up by nothing other than a handful of celebrity tweets. But in a new Atlantic article, “The Resistance Media Weren’t Ready for This,” it’s appropriate. Staff writer McKay Coppins details how the large Facebook pages and Twitter celebrities who built their following on building up the Mueller investigation are adapting to its conclusion. It’s worth a quick read, but what is even more interesting for marketers and would-be pundits is the method with which these “Resistance Media” influencers were able to create a cottage industry out of their political passions.

I used a Twitter thread to detail how “resistance” and “Russiagate” personalities and pages take advantage of human psychology and the quirks of social media to amass huge followings – and how you can learn from their tactics.

 

The first innovation is outrage – by tapping into hot, highly emotional news stories (with the help of services like ActionSprout, NewsWhip, or CrowdTangle) and either reposting or repackaging content with a photo meme or short video, guerilla publishers push their brands into mainstream consciousness with viral content. “Found” videos – often from cell phones capturing shocking interpersonal conflicts are turned into branded viral content as well. However, Facebook is continually working on its algorithms to de-emphasise contrived viral content to keep user feeds focused on friendlier inter-personal content and its own advertising. Posting popular mainstream news under a brand or influencer page is one of the safest ways to increase name ID without running afoul of Facebook rules.

Over on Twitter, there is no sign of a crackdown on contrived content. Mini-celebrities like those mentioned in Coppins’ article found the emotional outrage in the Mueller investigation of Trump – so how did the breakouts happen? Some of the media figures involved in Russiagate had full-time content producing jobs, but others, like the notorious Krassenstein brothers, used raw marketing smarts to center themselves in Resistance Twitter. It’s a formula that’s easy to copy – if you (or a virtual assistant) have time.

The secret to predictable, overnight increases in exposure on Twitter is being among the first to offer a relevant reply to a set of large, popular accounts. In practice, what this looks like is several hundred accounts replying within seconds to tweets by top politicians, executives, and Hollywood celebrities. And what it looks like in your own Twitter analytics is the bars on the right:

This strategy to generate hundreds of dollars or more in organic impressions per day plus a steady stream of new followers works like this:

  • Create a bank of content and a strategy for updating it regularly.
  • Turn on notifications on the Twitter account you will use for replies.
  • Turn on notifications for the list of popular users (they get lots of replies) that best matches your content strategy.
  • Be one of the first to reply whenever these users tweet.
  • Mix up the content and list so that you’re not reusing a meme or link too frequently.

 

In a month-long experiment with this strategy, my average impressions jumped 3x. You can do the same.

Adriel Hampton is a marketing consultant and founder of The Really Online Lefty League.


Reputation & Read Rate Roundup

In just the last few weeks, perhaps because we’re jumping into new political and marketing campaign seasons, a few articles have popped up on read and response rate. One common denominator in rate enhancement is the maintenance of a good sender reputation.

The most noticeable is probably Dmytro Spilka’s audaciously-titled “How I got 80% open rate in my email outreach campaign,” which lists factors like target identification, a masterful subject line, actually using preview snippets, solid sender reputation, and effective follow-ups. One thing the article could have included at the top of the list, though, is the maintenance of data or list hygiene–updates that correct your recipient addresses and remove “unwanted names, undeliverable addresses, or individuals who have chosen not to receive direct mail offers or who have unsubscribed from email lists.” Data append services like Accurate Append provide this.

But what I like about Dmytro’s post is that it emphasizes the dynamic at work in sender reputation assessment: Email platforms are committed to giving their users a good experience, and that means “whittling down any perceived junk automatically,” while senders want the emails to be seen and opened. Reputation score is the way you negotiate through those competing imperatives. Yannis Psarras at Moosend points out that a good reputation keeps emails out of the recipient’s spam folder. Psarras’s post also has a cute “periodic table of delivery score” that has to be seen for itself, with element-like abbreviations like Fc for fewer complaints, Bl for few bounces, Vo for consistent volume, and so on.

There are various reputation checks that guide deliverability. Consistency is one that a lot of new senders aren’t aware of. “A consistent volume of email campaigns, without major drops or spikes, plays a significant role in sender reputation,” Psarras says. “For example, if you send out an email to your list twice a week, switching to three times a week, will cause ripples. There will be times when you will want to send out more emails than normal. For example over the busy Christmas period. But aim for a regular, consistent schedule where possible.”

A particularly useful document that also posted during the last month is Return Path’s “Sending Best Practices,” a detailed PDF listing the top factors that impact sending reputation and deliverability. The document discusses complaints (you need complaint feedback loops to suppress complainers from future versions of that list), getting rid of “unknown users” after the first bounce, opt-in permission methods, and one often-neglected piece of the puzzle: giving subscribers good, relevant content when they indicate they want to receive messages from you.

2019 and 2020 should be heavy-rain years for voter- and consumer- directed emails. Systemic use of sending best practices and good data hygiene is going to be the key to recipient engagement rather than landing in the spam folder or, worse, finding yourself in email sender jail, unable to get your messages out.


Musings on Nonprofits, Advocacy Campaigns, and Big Data

I’m a big fan of how entrepreneurs can use and manage data, but nonprofits have to use and manage data too. Most people know (or would not be surprised to learn) that data append services help nonprofits data-cleanse at the end of the year. This is vital when you devote so much time to finding new donors and keeping consistent donors in the loop–and keeping up with changes in their contact info.

But what about other facets of data management in nonprofits? Specifically, what about nonprofits’ relationship to “big data,” or data sets “too large or complex to be dealt with by traditional data-processing application software,” as Wikipedia defines the term? Interestingly, we’ve recently seen several articles on big data and nonprofits, and depending on which article you read, you might conclude that nonprofits can easily use big data, that nonprofits can only ride on the coattails of private businesses that use big data, or you may learn many ways your organization can both acquire and use it.

You can access some big data for free
Kayla Matthews’ piece last September at Smart Data Collective points out that nonprofits who can’t afford costly data platforms can get free data sources mediated by entities as diverse as Amazon, Pew Research and the U.S. Food and Drug Administration. They offer “open data” aggregation and platform services that interest groups can use at no cost. There’s also a group called the Nonprofit Open Data Collective, “a consortium of nonprofit representatives and researchers [that] is working to analyze electronically submitted Form 990 data and make it more useful to everyone who wants to see it.”

There are high-visibility organizations using it
Matthews provides a couple of powerful anecdotes in her post from last October, including the Jane Goodall Institute’s use of data entered by private citizens throughout Africa speaking to the status of and threats to chimpanzee populations, and UNICEF’s dissemination of health stats like infant mortality into public hands. It’s not just about being nice, though, as Matthews points out: “Viewing the hard data for themselves might encourage individuals to give generously when it’s time for fundraising campaigns.”

One of our favorite new apps–and new approaches–is Branch, which builds on the success of Kiva, a great platform helping small entrepreneurs –such as beginning family farmers– crowdsource startup loans. It turns out that Kiva’s co-founder, Matthew Flannery, started Branch as a new nonprofit, hoping to solve a challenge that came to be associated with Kiva: “Due to having limited connectivity, loan officers in those countries would have to travel to each borrower to distribute the money, resulting in additional costs. However, with mobile dominating digital technology worldwide, it’s now becoming possible to skip the loan officers entirely and send the money directly to the borrower via mobile. Flannery wants to use machine learning to assist with making sound lending decisions and swiftly deliver loans via mobile payment.” Pretty cool.

So what’s the problem?
For smaller organizations, the problem may simply be scale of human resources to data. In small organizations, people have a lot of hats to wear and no one person may have the capacity and training for big data management. But there may be other challenges intrinsic to the models and iterations of nonprofits. The bloggers at Pursuant say that a leading problem nonprofits have with big data is that they compartmentalize it too much. “Most organizations already have a lot of data,” they write, “but they store it in departmental silos . . . Instead of synthesizing the data from all sources, nonprofits look at one area at a time. But that approach doesn’t unleash the power of big data. Information gleaned from donor data files, special events, emails opened or closed, and what donors click on at your website must be looked at holistically. But doing that requires breaking out of departmental silos. Diffused data isn’t good for the donor, your mission, or your organization’s long-term sustainability.”

So there’s capacity, but there’s also too much diffusion of data across areas that don’t interact much with each other, and so offer no incentive or expertise on data synthesis.

Good data management
Avery Phillips, writing for Inside Big Data, suggests that taking data management to the next level “requires a structured approach that incorporates cleaning up the data (e.g. paring it down to genuinely useful and trusted information) and creating larger networks of employees involved in the decision-making process beyond those that are tasked with handling the data itself.” Your organization might not be big enough to do that yourself, so consultants may be inevitable. While that costs money, the stories in the various posts we read, including the UNICEF example, suggests it could make your organization even more money. It’s up to you whether you are at the level you believe is appropriate for the service you need. The important thing, Phillips says, is that your IT team isn’t the only department “aware of pertinent big data that might influence” an organizational decision. What you want is “a larger umbrella of team members . . .  incorporated into the ‘web of knowledge’ that big data can provide. . . helping them to maneuver themselves into the ever-crowded spotlight, communicate their mission statement effectively, and raise funds at unprecedented rates.”

So, while data management services are available no matter what, the challenge and promise is in managing your data holistically and with as many voices included in the analysis as possible.


Don’t Get Berned by Text Scamming

Last week, lots of folks noticed an unusually heavy rainstorm of texts from sources claiming to be associated with the Bernie Sanders for President campaign–although the campaign had only just begun. One responses was from Anne Laurie, a Daily Kos diarist, who says she doesn’t text from her “secondhand Galaxy S6” and doesn’t provide her cell number to anyone. Nevertheless, she received numerous texts from Bernie supporters in the immediate hours after his campaign announcement. She concluded they really were from the campaign or from legitimate supporters — which irritated her even more, because she doesn’t presently support Sanders in the presidential primary.

Bernie supporters, on the other hand, may be particularly vulnerable to texting scams claiming to be affiliated with the Sanders campaign. After all, they’re an enthusiastic bunch and like to know there are like-minded people eager to meet them. The biggest concern with text scamming, or “SMishing” for “phishing” via SMS, is identity theft. Viruses are also a concern. And as online donations surge for political campaigns, avoiding scam links will become more of a challenge.

SMishing may be growing as robocalls decrease in effectiveness (The Atlantic says “telephone culture is disappearing”). It’s true that robocalls have been used to impersonate campaigns (or sometimes do the really nefarious dirty work of racist campaigns) and continue to be used to run scams. While we were working on this article, CNN reported on a group running robocalls impersonating Donald Trump that netted $100,000, got media coverage as a scam and, at the time of this writing, was still going strong. But as New York Magazine’s Jake Swearingen wrote just a couple of weeks ago, we may be done with robocalls as a thing, since carriers now have both the technology and the incentive to block or radically screen calls–although it’s not so clear whether the same would be true for texts.

 

In a brand new study report, “Hamsini Sridharan of MapLight and Samuel Woolley of the Institute for the Future outline more than 30 concrete proposals—all grounded in the democratic principles of transparency, accountability, standards, coordination, adaptability, and inclusivity–to protect the integrity of the future elections, including the pivotal 2020 U.S. presidential election.” The authors argue that both anonymity and automation “have made deceptive digital politics especially harmful.” They locate solutions in public policy and legal liability (including increasing the liability of the platforms themselves, a proposal sure to raise a lot of eyebrows). But they also emphasize routes like public education and ethical guidelines embraced by the media.

Enter the Direct Marketing Association’s code of ethics–not the law, to be sure, but norms that we can hope the political industry will embrace consistently enough that those who do go outside the lines will be seen as exceptional pariahs. More important even than the code’s numerous provisions, compliance with which would end scammy or even opportunistic SMS texts, is the overall spirit of the code, a customer-centric, privacy-embracing document. And, at least two provisions, 1.3 and 3.11 require that your data be cleaned regularly, which data append services will do for you.

In the meantime, we can all take some simple preventative measures. Obviously, don’t open any attachments sent via text. And whatever you do, don’t text back! if you don’t recognize the source of a text— according to the Federal Trade Commission, not answering is the best way to avoid negative consequences to your identity or your smartphone. While scam and false pretense texts are clearly illegal, there are some opportunistic texting schemes that may slip through the cracks of the law. Under federal law, unsolicited messages and emails are illegal, and both textual and phone “robocalls” are too, but there are exceptions for political surveys, fundraising messages from charities, and popular peer-to-peer texting apps–understandable exceptions, but ones that may be easy for smart and crappy people to manipulate.


Looking into 2019, AI and Data Revisited

At the end of 2018 I published a post citing Cynthia Harvey, who had herself cited a Deloitte survey with bleak predictions tabout companies’ use of AI. That survey had indicated that while more companies (in various industries, not just marketing or campaigning) were dipping their toes into AI, the number of companies abandoning AI projects was also high. Left alone, that citation might give readers the impression that AI is floundering.

It’s actually not floundering at all. What should not be lost is that 37 percent of all organizations have implemented some kind of artificial intelligence in their operations, and that’s a 270 percent increase over the past four years. Even if the long-term rise has its hiccups, that’s an astounding level of adaptation in a short increment of time.

There are several reasons AI helps companies use data across the board. AI can be created with “context awareness” that will allow systems to discern when they are most needed, switch their modes around, and more. In terms of organizational processes, new artificial intelligence systems can facilitate decentralization and delegation of tasks in organizations practices that increase profits in a time when very few things can reliably increase profits.

The implications of this technology are staggering. AI can help policymakers solve poverty, help doctors slow the spread of diseases, help scientists address climate change, threats to oceans, and more. Artificial intelligence can also be a powerful tool in deploying the natural intelligence of salespeople and analysts through data analysis that can supplement other data services. In marketing and campaign data analysis, one function of AI is to detect small and subtle changes in consumer or voter behavior, attitudes, beliefs, demographics, and so on. If income has risen ever-so-slightly in some area, you may be able to look for other signs of income profile changes or even gentrification; this could impact your campaign strategy.

And, of course, AI helps in segmentation. In campaign technology, segmentation was critical in Barack Obama’s 2008 campaign, where different videos were shown to audiences based on their own level of commitment to the campaign. Consider that it’s been just over ten years since then and AI has advanced considerably. Now, marketers and retailers are using AI to create personalized customer experiences, analyzing data so that customers may be notified by email, direct mail, or SMS if products arrive that they might like. Campaign data analysts can do the same thing.

The beauty of AI, or at least a very important additional benefit, may be the protection of privacy. For example, the folks at Demografy have designed a market segmentation platform that can give you demographic insights or help you append lists with missing data similar to the smart algorithms we use at Accurate Append to create wealth scores and green scores. It does this without gathering sensitive information like addresses or emails, just using names, and it can do this because of its AI component, using “very scarce and non-sensitive information as input while existing technologies use either large amount of data or sensitive personal information to detect demographics.”


The Problem of Unregistered Voters and the Promise of Consumer Data

Based on the partisan rancor over whether the federal government should create an Election Day holiday, it’s doubtful we will see universal or automatic voter registration soon. Still, it’s heartening to see that voter registration and voter participation increased sharply in the last midterm election cycle–to a fifty-year high, according to NPR.

Here at Accurate Append, we’re always curious about how technology can be used to reach both voters and nonvoters.

The data on unregistered voters send a lot of mixed signals. On the one hand, “43 percent of the unregistered said nothing would motivate them to register” according to the recent Pew Trusts survey and analysis on unregistered voters. This is disturbing news by any metric.  On the other hand, unregistered and “occasional” voters are actually not universally apathetic. The Pew Survey also reported that small but consistent percentages of unregistered and “semifrequent” voters self-report having worked to solve issues in their community, done unpaid volunteer work, and attended community meetings. Some (only six percent) had even donated money to candidates they didn’t end up voting for! Assuming those folks aren’t dogmatic about never voting again, that might mean that as many as 10-15 percent of unregistered voters could be convinced to register in 2020.

There’s at least one artificial intelligence project devoted to finding and registering unregistered voters: the Electronic Registration Information Center has, in the past ten years of its existence, identified upwards of 26 million people eligible to vote but unregistered. They have done so while also “cleaning up voter rolls” (removing dead, relocated, or other no-longer-eligible voters) without “purging” the rolls of people who actually are eligible to vote.  The success of ERIC demonstrates that we can increase voter access and improve voter roll accuracy at the same time, a great thing given recent battles over other purges that have been open to charges of partisanship.

But campaigns may not be able to access such data, and there are less resource-intensive ways to figure out who’s not on the voter rolls who should be. As Adriel Hampton pointed out a couple of years ago, one way to reach the unreachable voter is by comparing official state lists of registered voters with Accurate Append’s lists of adult consumers. Services like ours can provide phone numbers and, in some cases, email addresses for unregistered voters based on those comparisons.

Consumer data is a promising comparative tool to find additional people living in an area who are not registered. It’s compiled from a variety of sources and can paint a more comprehensive picture of people’s behavior and preferences than voter data can–and it’s often grouped according to traits that campaign analysts will find useful, such as individual-level weath scores and green scores.

The Pew Report concludes on a hopeful but cautionary note:

more than 40 percent of the unregistered cared who would win the presidency in 2016, and some indicated that they could be motivated to register in the future, though many also feel that the voting process does not affect the way governing decisions are made.

So some of this is about finding the right tech and datasets, but a lot of it is about making the political process itself more engaging. Who wouldn’t support that?


Demand for Data Management – the Logistics and the Ethics

We know that in 2019, demand for data management work will outstrip supply. We’ve seen those numbers. Dataversity’s analysis of this 2019 trend attributes this to the exponential growth in the need for business data, pushing firms to constantly look for “advanced data collection and storage.” Central to businesses’ needs are data engineers – “architect[s] of organizational data planning” whose job role “will take center stage in 2019. In fact, data teams will not be able to function without these super-techies as they are assumed to have multiple programming skills and advanced technical knowledge to prepare the groundwork for Enterprise Data Management, which can be used by other staff like the Data Analyst or the Data Scientist for specific data-oriented functions.”

Here are some other thoughts behind or beyond the numbers:

First, shifting to an Internet of Things means there will be a need for humans to evaluate the needs of other humans. We’re drawing a lot of the information and data for this post from Cynthia Harvey’s recent set of tech predictions for 2019. On IoT, Cynthia predicts a massive increase in firms’ deployment of and planning around “live production networks” in 2019. This will require the refinement of smart devices, and the “smart” will require an infusion of data analysis and management skills, which circles back to the need for those skills.

Second, Artificial Intelligence isn’t a panacea and doesn’t come close to solving the shortage of human expertise in the short or medium term. Growth in AI will be nowhere near growth in IoT technology. In fact, Cynthia cites a Deloitte survey revealing that while over half of enterprises were currently using integrated AI, and “37% planned to do so within two years,” around twenty percent had stopped current AI projects and around another twenty percent “had decided not to start one because of cybersecurity concerns.”

Those numbers indicate a couple of bumps in the road for AI, which will also spur investment in tools enabling “ordinary people” to use AI to become virtual data scientists, throwing the ball back to businesses doing their own data science.

Third, as the data management field grows, those data users in the field of political technology also want “ethical infrastructure” in place to ensure equity and efficiency in the unique ways we use new technology. One important example of this is the Open Supporter Data Interface (OSDI) coalition, providing like-minded developers with an open standard for interoperability between software applications, reducing integration time and headaches. It’s a great example of cooperation within a relatively competitive field, a sign that this kind of nuanced tech can be socially shared, to the benefit of stakeholders.