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December 10, 2018
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Advertising Tech

Rideshare advertising startup Firefly launches with $21.5M in funding

in Advertising Tech/Delhi/firefly/funding/India/Politics/Startups by

Firefly, a startup that allows rideshare drivers to make money through digital advertising, is officially launching today. It’s also announced that it has raised $21.5 million in seed funding.

The idea of sticking advertising on a cab isn’t new, but Firefly offers drivers what it calls a “digital smart screen,” allowing advertisers to run targeted, geofenced campaigns. The company has apparently run more than 50 ad campaigns already, during a beta testing period in San Francisco and Los Angeles, with hundreds of cars on the road.

“Being the first at building out the IP is going to be the main differentiator,” said co-founder and CEO Kaan Gunay. “Over half our team are engineers, and we have been extremely focused on developing core IP to make sure it’s scalable.”

In addition, Gunay said that thanks to the combination of Firefly’s targeting capabilities with its “strict” advertising policies (it won’t accept ads for strip clubs, tobacco and cannabis companies, among others), “We’re working with a lot of advertisers who might not even have advertised outdoors before. We believe we are expanding the market.”

One of the main goals is to allow drivers for Uber, Lyft and other ride-hailing services to make more money. In fact, Firefly says the average driver in its network makes an additional $300 per month.

Gunay explained that if the driver meets a certain threshold for hours on the road, the company will pay them a flat fee to carry its advertising — but he also said the company is exploring different ways to “maximize the revenue that we share with the drivers and give the maximum benefit to the drivers.”

It’s an issue on regulators’ minds as well, with New York recently approving new rules around driver compensation.

Earlier this year, Uber partnered with a startup called Cargo to allow drivers to make additional income by selling goods like gum, snacks and phone chargers. Firefly doesn’t have an official relationship with the ride-hailing companies, but Gunay said, “In our conversations with these large companies … they’ve said the drivers are free to do what they want to do. This is why it’s a win for everyone.”

Gunay also said these displays will become the foundation for a “smart city data network.” In other words, they will collect data that Firefly plans to share with local governments and nonprofit groups. For example, he said the company has already been sharing air quality data with the Coalition for Clean Air, and it’s also looking to include temperature sensors and accelerometers.

Apparently Gunay doesn’t plan to make money from this side of the business. He told me, “We want to be able to add value to how cities operate … We’re not planning to monetize that.”

Getting back to the funding, $21.5 million is a huge seed round, but Gunay said the company’s success thus far was able to”justify a larger raise and a higher valuation.” The round was led by NfX, Pelion Venture Partners, Decent Capital (founded by Tencent’s Jason Zeng) and Jeffrey Housenbold of SoftBank Vision Fund (yes, that SoftBank Vision Fund).

News Source = techcrunch.com

Acast raises $35M to help podcasters make money

in Acast/Advertising Tech/Delhi/funding/India/Media/podcasts/Politics/Ross Adams/Startups by

Podcasting has grown tremendously in recent years, and a Stockholm-based company called Acast is looking to help all those podcasters make money.

Acast is announcing today that it has raised $35 million in Series C funding, bringing its total funding to more than $67 million. Investors in the round include AP1 (which manages some of the capital in Sweden’s national income pension system), as well as Swedbank Robur funds Ny Teknik and Microcap.

Ross Adams, who became Acast’s CEO last fall, told me that the money will allow Acast to expand, both in terms of its product offerings and the geographies where it operates.

The company has focused on bringing technology to the surprisingly old-fashioned world of podcast advertising. In fact, it pioneered the practice of dynamically inserting ads into podcasts — as opposed to the model where (as Adams put it), “When you listen to a five-year-old podcast, you’ll hear the host read a five-year-old ad.”

Earlier this year, it announced a partnership with the BBC, allowing the BBC’s podcasts to remain ad-free in the United Kingdom while inserting ads everywhere else.

“We don’t mind if your show is absolutely huge or absolutely tiny,” Adams said. “The model we have allows a serious mainstream publisher like the BBC to monetize — or a bedroom podcast hobbyist.”

Ross Adams

At the same time, Adams wants Acast to support other business models. It’s already experimenting with paid, premium content through its Acast+ app, but it sounds like there are more paid podcast products in the works: “We want to be that central point of monetization, [whether] they make money through advertising or they’re looking at premium offerings.”

As for geographic expansion, Acast says it launched in Ireland, New Zealand and Denmark this year. It also plans to grow in the United States, which currently represents 25 percent of all listens on the platform.

Acast is also looking to bring podcast monetization into new hardware — Adams said the company has spent much of the past year focused on the smart speaker market. Those speakers present new opportunities for content (Adams said it’s less about “longer-form storytelling” and more “short-form shows for your daily consumption in the morning”), and new challenges for advertising.

Adams is hoping that if Acast can solve those challenges, it won’t just be monetizing the smart home market, but also moving into cars and anywhere else you might find “voice-enabled technology.”

News Source = techcrunch.com

Google ‘incognito’ search results still vary from person to person, DDG study finds

in Advertising Tech/Delhi/DuckDuckGo/eli pariser/Filter Bubble/Google/google search/India/personalization/Politics/presidential election/privacy/search results/United States by

A study of Google search results by anti-tracking rival DuckDuckGo has suggested that escaping the so-called ‘filter bubble’ of personalized online searches is a perniciously hard problem for the put upon Internet consumer who just wants to carve out a little unbiased space online, free from the suggestive taint of algorithmic fingers.

DDG reckons it’s not possible even for logged out users of Google search, who are also browsing in Incognito mode, to prevent their online activity from being used by Google to program — and thus shape — the results they see.

DDG says it found significant variation in Google search results, with most of the participants in the study seeing results that were unique to them — and some seeing links others simply did not.

Results within news and video infoboxes also varied significantly, it found.

While it says there was very little difference for logged out, incognito browsers.

“It’s simply not possible to use Google search and avoid its filter bubble,” it concludes.

Google has responded by counter-claiming that DuckDuckGo’s research is “flawed”.

Degrees of personalization

DuckDuckGo says it carried out the research to test recent claims by Google to have tweaked its algorithms to reduce personalization.

A CNBC report in September, drawing on access provided by Google, letting the reporter sit in on an internal meeting and speak to employees on its algorithm team, suggested that Mountain View is now using only very little personalization to generate search results.

A query a user comes with usually has so much context that the opportunity for personalization is just very limited,” Google fellow Pandu Nayak, who leads the search ranking team, told CNBC this fall.

On the surface, that would represent a radical reprogramming of Google’s search modus operandi — given the company made “Personalized Search” the default for even logged out users all the way back in 2009.

Announcing the expansion of the feature then Google explained it would ‘customize’ search results for these logged out users via an ‘anonymous cookie’:

This addition enables us to customize search results for you based upon 180 days of search activity linked to an anonymous cookie in your browser. It’s completely separate from your Google Account and Web History (which are only available to signed-in users). You’ll know when we customize results because a “View customizations” link will appear on the top right of the search results page. Clicking the link will let you see how we’ve customized your results and also let you turn off this type of customization.

A couple of years after Google threw the Personalized Search switch, Eli Pariser published his now famous book describing the filter bubble problem. Since then online personalization’s bad press has only grown.

In recent years concern has especially spiked over the horizon-reducing impact of big tech’s subjective funnels on democratic processes, with algorithms carefully engineered to keep serving users more of the same stuff now being widely accused of entrenching partisan opinions, rather than helping broaden people’s horizons.

Especially so where political (and politically charged) topics are concerned. And, well, at the extreme end, algorithmic filter bubbles stand accused of breaking democracy itself — by creating highly effective distribution channels for individually targeted propaganda.

Although there have also been some counter claims floating around academic circles in recent years that imply the echo chamber impact is itself overblown. (Albeit sometimes emanating from institutions that also take funding from tech giants like Google.)

As ever, where the operational opacity of commercial algorithms is concerned, the truth can be a very difficult animal to dig out.

Of course DDG has its own self-interested iron in the fire here — suggesting, as it is, that “Google is influencing what you click” — given it offers an anti-tracking alternative to the eponymous Google search.

But that does not merit an instant dismissal of a finding of major variation in even supposedly ‘incognito’ Google search results.

DDG has also made the data from the study downloadable — and the code it used to analyze the data open source — allowing others to look and draw their own conclusions.

It carried out a similar study in 2012, after the earlier US presidential election — and claimed then to have found that Google’s search had inserted tens of millions of more links for Obama than for Romney in the run-up to that.

It says it wanted to revisit the state of Google search results now, in the wake of the 2016 presidential election that installed Trump in the White House — to see if it could find evidence to back up Google’s claims to have ‘de-personalized’ search.

For the latest study DDG asked 87 volunteers in the US to search for the politically charged topics of “gun control”, “immigration”, and “vaccinations” (in that order) at 9pm ET on Sunday, June 24, 2018 — initially searching in private browsing mode and logged out of Google, and then again without using Incognito mode.

You can read its full write-up of the study results here.

The results ended up being based on 76 users as those searching on mobile were excluded to control for significant variation in the number of displayed infoboxes.

Here’s the topline of what DDG found:

Private browsing mode (and logged out):

  • “gun control”: 62 variations with 52/76 participants (68%) seeing unique results.
  • “immigration”: 57 variations with 43/76 participants (57%) seeing unique results.
  • “vaccinations”: 73 variations with 70/76 participants (92%) seeing unique results.

‘Normal’ mode:

  • “gun control”: 58 variations with 45/76 participants (59%) seeing unique results.
  • “immigration”: 59 variations with 48/76 participants (63%) seeing unique results.
  • “vaccinations”: 73 variations with 70/76 participants (92%) seeing unique results.

DDG’s contention is that truly ‘unbiased’ search results should produce largely the same results.

Yet, by contrast, the search results its volunteers got served were — in the majority — unique. (Ranging from 57% at the low end to a full 92% at the upper end.)

“With no filter bubble, one would expect to see very little variation of search result pages — nearly everyone would see the same single set of results,” it writes. “Instead, most people saw results unique to them. We also found about the same variation in private browsing mode and logged out of Google vs. in normal mode.”

“We often hear of confusion that private browsing mode enables anonymity on the web, but this finding demonstrates that Google tailors search results regardless of browsing mode. People should not be lulled into a false sense of security that so-called “incognito” mode makes them anonymous,” DDG adds.

Google initially declined to provide a statement responding to the study, telling us instead that several factors can contribute to variations in search results — flagging time and location differences among them.

It even suggested results could vary depending on the data center a user query was connected with — potentially introducing some crawler-based micro-lag.

Google also claimed it does not personalize the results of logged out users browsing in Incognito mode based on their signed-in search history.

However the company admited it uses contextual signals to rank results even for logged out users (as that 2009 blog post described) — such as when trying to clarify an ambiguous query.

In which case it said a recent search might be used for disambiguation purposes. (Although it also described this type of contextualization in search as extremely limited, saying it would not account for dramatically different results.)

But with so much variation evident in the DDG volunteer data, there seems little question that Google’s approach very often results in individualized — and sometimes highly individualized — search results.

Some Google users were even served with more or fewer unique domains than others.

Lots of questions naturally flow from this.

Such as: Does Google applying a little ‘ranking contextualization’ sound like an adequately ‘de-personalized’ approach — if the name of the game is popping the filter bubble?

Does it make the served results even marginally less clickable, biased and/or influential?

Or indeed any less ‘rank’ from a privacy perspective… ?

You tell me.

Even the same bunch of links served up in a slightly different configuration has the potential to be majorly significant since the top search link always gets a disproportionate chunk of clicks. (DDG says the no.1 link gets circa 40%.)

And if the topics being Google-searched are especially politically charged even small variations in search results could — at least in theory — contribute to some major democratic impacts.

There is much to chew on.

DDG says it controlled for time- and location-based variation in the served search results by having all participants in the study carry out the search from the US and do so at the very same time.

While it says it controlled for the inclusion of local links (i.e to cancel out any localization-based variation) by bundling such results with a localdomain.com placeholder (and ‘Local Source’ for infoboxes).

Yet even taking steps to control for space-time based variations it still found the majority of Google search results to be unique to the individual.

“These editorialized results are informed by the personal information Google has on you (like your search, browsing, and purchase history), and puts you in a bubble based on what Google’s algorithms think you’re most likely to click on,” it argues.

Google would counter argue that’s ‘contextualizing’, not editorializing.

And that any ‘slight variation’ in results is a natural property of the dynamic nature of its Internet-crawling search response business.

Albeit, as noted above, DDG found some volunteers did not get served certain links (when others did), which sounds rather more significant than ‘slight difference’.

In the statement Google later sent us it describes DDG’s attempts to control for time and location differences as ineffective — and the study as a whole as “flawed” — asserting:

This study’s methodology and conclusions are flawed since they are based on the assumption that any difference in search results are based on personalization. That is simply not true. In fact, there are a number of factors that can lead to slight differences, including time and location, which this study doesn’t appear to have controlled for effectively.

One thing is crystal clear: Google is — and always has been — making decisions that affect what people see.

This capacity is undoubtedly influential, given the majority marketshare captured by Google search. (And the major role Google still plays in shaping what Internet users are exposed to.)

That’s clear even without knowing every detail of how personalized and/or customized these individual Google search results were.

Google’s programming formula remains locked up in a proprietary algorithm box — so we can’t easily (and independently) unpick that.

And this unfortunate ‘techno-opacity’ habit offers convenient cover for all sorts of claim and counter-claim — which can’t really now be detached from the filter bubble problem.

Unless and until we can know exactly how the algorithms work to properly track and quantify impacts.

Also true: Algorithmic accountability is a topic of increasing public and political concern.

Lastly, ‘trust us’ isn’t the great brand mantra for Google it once was.

So the devil may yet get (manually) unchained from all these fuzzy details.

News Source = techcrunch.com

Uber Eats test lets restaurants trade discounts for ranking boost

in Advertising Tech/Apps/Delhi/eCommerce/Food/food delivery/India/Logistics/mobile/Payments/Politics/Startups/TC/Uber/Uber Eats by

Uber Eats has effectively invented its own native ad unit. Uber confirmed to TechCrunch that a test quietly running in markets around India allows restaurants to bundle several food items together and sell them at a discounted price in exchange for promoted placement by Uber Eats in a featured section of local “Specials”. In some cases, restaurants foot the cost of the discount, while in others Uber pays for the discounts.

The Uber Specials feature demonstrates the massive leverage awarded to food delivery apps that aggregate restaurants. Users often come to Uber Eats and its competitors without a specific restaurant in mind. Uber can then point those customers to whichever food supplier it prefers. The suppliers in turn will increasingly compete for the favor the aggregators — not just in terms of food quality, speed, and review scores, but also in terms of discounts. The aggregators will win users if they offer the best deals, creating a network effect makes restaurants more keen to play ball.

TechCrunch first learned of Uber’s ambitions in the space from a mock-up of the Promoted Items Value Section feature spotted in its app by mobile researcher and frequent TC tipster Jane Manchun Wong. The fictional food items included “Best Beer” that “is made from only the finest gutter swill” and “Weird Fries” that “will so utterly decimate your sense of good food that you will be permanently reduced to a whimpering shell of your former self!” This jokey text that seemingly was never meant for public viewing also noted that the fries are so good you should “throw all your other food in the garbage right now!” Uber assured us these weren’t real.

But what it did confirm is that the discounts for promoted placement test is live in India. “We’re always experimenting with ways to make it easier to find your favorite foods on Uber Eats”, according to a statement provided by an Uber spokesperson.

The feature allows restaurants to create a bundled meal at certain price point, such as a chicken sandwich, french fries, and a drink at a price that’s less than the sum of its parts. The company tells me the goal is to take the friction out of ordering by giving people pre-set meals at a better price prominently available in the app. Attracting more customers that have plenty of other options could offset the discount. Businesses could also use it to bundle high margin items like soft drinks in with meals, or to get rid of overstock.

Ben Thompson’s aggregation theory describes how power accrues to aggregators that match supply with demand

It’s already common for restaurants to make ‘specials’ out of food they have too much of. That butternut squash ravioli might only be featured because they can’t get rid of it. In that sense, you could think of Uber Specials as the inverse of surge pricing. When supply is too high, restaurants can offer discounts to gain more demand. It’s also not far off from Google Search’s keyword ads where business pay for more visibility.

Uber wouldn’t discuss whether it plans to bring the strategy to other markets, but it makes sense to assume it’s considering expansion. Done wrong, it could look a bit like Uber Eats is pressuring restaurants to surrender discounts if they want to be discoverable inside its app. If restaurants within Uber Eats get into heated competition to offer discounts, it could drive down their profits. But done right, Specials could look like a triple-win. Restaurants can offload surplus and bundle in high margin items while scoring new customers from enhanced placement, customers get cheaper food options, and Uber Eats becomes people’s go-to app for easy-to-order discounted meals.

News Source = techcrunch.com

The economics and tradeoffs of ad-funded smart city tech

in Advertising Tech/big data/cities/civic tech/Cloud/community/data governance/Delhi/Europe/Finance/funding/Government/govtech/Hardware/ike/India/Infrastructure/Intersection/kiosks/linknyc/Media/New York Enterprise/Opinion/Policy/Politics/smart cities/smart city/Startups/TC/Urban Tech/volta by

In order to have innovative smart city applications, cities first need to build out the connected infrastructure, which can be a costly, lengthy, and politicized process. Third-parties are helping build infrastructure at no cost to cities by paying for projects entirely through advertising placements on the new equipment. I try to dig into the economics of ad-funded smart city projects to better understand what types of infrastructure can be built under an ad-funded model, the benefits the strategy provides to cities, and the non-obvious costs cities have to consider.

Consider this an ongoing discussion about Urban Tech, its intersection with regulation, issues of public service, and other complexities that people have full PHDs on. I’m just a bitter, born-and-bred New Yorker trying to figure out why I’ve been stuck in between subway stops for the last 15 minutes, so please reach out with your take on any of these thoughts: @Arman.Tabatabai@techcrunch.com.

Using ads to fund smart city infrastructure at no cost to cities

When we talk about “Smart Cities”, we tend to focus on these long-term utopian visions of perfectly clean, efficient, IoT-connected cities that adjust to our environment, our movements, and our every desire. Anyone who spent hours waiting for transit the last time the weather turned south can tell you that we’ve got a long way to go.

But before cities can have the snazzy applications that do things like adjust infrastructure based on real-time conditions, cities first need to build out the platform and technology-base that applications can be built on, as McKinsey’s Global Institute explained in an in-depth report released earlier this summer. This means building out the network of sensors, connected devices and infrastructure needed to track city data. 

However, reaching the technological base needed for data gathering and smart communication means building out hard physical infrastructure, which can cost cities a ton and can take forever when dealing with politics and government processes.

Many cities are also dealing with well-documented infrastructure crises. And with limited budgets, local governments need to spend public funds on important things like roads, schools, healthcare and nonsensical sports stadiums which are pretty much never profitable for cities (I’m a huge fan of baseball but I’m not a fan of how we fund stadiums here in the states).

As city infrastructure has become increasingly tech-enabled and digitized, an interesting financing solution has opened up in which smart city infrastructure projects are built by third-parties at no cost to the city and are instead paid for entirely through digital advertising placed on the new infrastructure. 

I know – the idea of a city built on ad-revenue brings back soul-sucking Orwellian images of corporate overlords and logo-paved streets straight out of Blade Runner or Wall-E. Luckily for us, based on our discussions with developers of ad-funded smart city projects, it seems clear that the economics of an ad-funded model only really work for certain types of hard infrastructure with specific attributes – meaning we may be spared from fire hydrants brought to us by Mountain Dew.

While many factors influence the viability of a project, smart infrastructure projects seem to need two attributes in particular for an ad-funded model to make sense. First, the infrastructure has to be something that citizens will engage – and engage a lot – with. You can’t throw a screen onto any object and expect that people will interact with it for more than 3 seconds or that brands will be willing to pay to throw their taglines on it. The infrastructure has to support effective advertising.  

Second, the investment has to be cost-effective, meaning the infrastructure can only cost so much. A third-party that’s willing to build the infrastructure has to believe they have a realistic chance of generating enough ad-revenue to cover the costs of the projects, and likely an amount above that which could lead to a reasonable return. For example, it seems unlikely you’d find someone willing to build a new bridge, front all the costs, and try to fund it through ad-revenue.

When is ad-funding feasible? A case study on kiosks and LinkNYC

A LinkNYC kiosk enabling access to the internet in New York on Saturday, February 20, 2016. Over 7500 kiosks are to be installed replacing stand alone pay phone kiosks providing free wi-fi, internet access via a touch screen, phone charging and free phone calls. The system is to be supported by advertising running on the sides of the kiosks. ( Richard B. Levine) (Photo by Richard Levine/Corbis via Getty Images)

To get a better understanding of the types of smart city hardware that might actually make sense for an ad-funded model, we can look at the engagement levels and cost structures of smart kiosks, and in particular, the LinkNYC project. Smart kiosks – which provide free WiFi, connectivity and real-time services to citizens – have been leading examples of ad-funded smart city projects. Innovative companies like Intersection (developers of the LinkNYC project), SmartLink, IKE, Soofa, and others have been helping cities build out kiosk networks at little-to-no cost to local governments.

LinkNYC provides public access to much of its data on the New York City Open-Data website. Using some back-of-the-envelope math and a hefty number of assumptions, we can try to get to a very rough range of where cost and engagement metrics generally have to fall for an ad-funded model to make sense.

To try and retrace considerations for the developers’ investment decision, let’s first look at the terms of the deal signed with New York back in 2014. The agreement called for a 12-year franchise period, during which at least 7,500 Link kiosks would be deployed across the city in the first eight years at an expected project cost of more than $200 million. As part of its solicitation, the city also required the developers to pay the greater of either a minimum annual payment of at least $17.5 million or 50 percent of gross revenues.

Let’s start with the cost side – based on an estimated project cost of around $200 million for at least 7,500 Links, we can get to an estimated cost per unit of $25,000 – $30,000. It’s important to note that this only accounts for the install costs, as we don’t have data around the other cost buckets that the developers would also be on the hook for, such as maintenance, utility and financing costs.

Source: LinkNYC, NYC.gov, NYCOpenData

Turning to engagement and ad-revenue – let’s assume that the developers signed the deal with the expectations that they could at least breakeven – covering the install costs of the project and minimum payments to the city. And for simplicity, let’s assume that the 7,500 links were going to be deployed at a steady pace of 937-938 units per year (though in actuality the install cadence has been different). In order for the project to breakeven over the 12-year deal period, developers would have to believe each kiosk could generate around $6,400 in annual ad-revenue (undiscounted). 

Source: LinkNYC, NYC.gov, NYCOpenData

The reason the kiosks can generate this revenue (and in reality a lot more) is because they have significant engagement from users. There are currently around 1,750 Links currently deployed across New York. As of November 18th, LinkNYC had over 720,000 weekly subscribers or around 410 weekly subscribers per Link. The kiosks also saw an average of 18 million sessions per week, or 20-25 weekly sessions per subscriber, or around 10,200 weekly sessions per kiosk (seasonality might even make this estimate too low). 

And when citizens do use the kiosks, they use it for a long time! The average session for each Link unit was four minutes and six seconds. The level of engagement makes sense since city-dwellers use these kiosks in time or attention-intensive ways, such making phone calls, getting directions, finding information about the city, or charging their phones.   

The analysis here isn’t perfect, but now we at least have a (very) rough idea of how much smart kiosks cost, how much engagement they see, and the amount of ad-revenue developers would have to believe they could realize at each unit in order to ultimately move forward with deployment. We can use these metrics to help identify what types of infrastructure have similar profiles and where an ad-funded project may make sense.

Bus stations, for example, may cost about $10,000 – $15,000, which is in a similar cost range as smart kiosks. According to the MTA, the NYC bus system sees over 11.2 million riders per week or nearly 700 riders per station per week. Rider wait times can often be five-to-ten minutes in length if not longer. Not to mention bus stations already have experience utilizing advertising to a certain degree.  Projects like bike-share docking stations and EV charging stations also seem to fit similar cost profiles while having high engagement.

And interactions with these types of infrastructure are ones where users may be more receptive to ads, such as an EV charging station where someone is both physically engaging with the equipment and idly looking to kill up sometimes up to 30 minutes of time as they charge up. As a result, more companies are using advertising models to fund projects that fit this mold, like Volta, who uses advertising to offer charging stations free to citizens.

The benefits of ad-funding come with tradeoffs for cities

When it makes sense for cities and third-party developers, advertising-funded smart city infrastructure projects can unlock a tremendous amount of value for a city. The benefits are clear – cities pay nothing, citizens are offered free connectivity and real-time information on local conditions, and smart infrastructure is built and can possibly be used for other smart city applications down the road, such as using locational data tracking to improve city zoning and congestion. 

Yes, ads are usually annoying – but maybe understanding that advertising models only work for specific types of smart city projects may help quell fears that future cities will be covered inch-to-inch in mascots. And ads on projects like LinkNYC promote local businesses and can tap into idiosyncratic conditions and preferences of regional communities – LinkNYC previously used real-time local transit data to display beer ads to subway riders that were facing heavy delays and were probably in need of a drink. 

Like everyone’s family photos from Thanksgiving, the picture here is not all roses, however, and there are a lot of deep-rooted issues that exist under the surface. Third-party developed, advertising-funded infrastructure comes with externalities and less obvious costs that have been fairly criticized and debated at length. 

When infrastructure funding is derived from advertising, concerns arise over whether services will be provided equitably across communities. Many fear that low-income or less-trafficked communities that generate less advertising demand could end up having poor infrastructure and maintenance. 

Even bigger points of contention as of late have been issues around data consent and treatment. I won’t go into much detail on the issue since it’s incredibly complex and warrants its own lengthy dissertation (and many have already been written). 

But some of the major uncertainties and questions cities are trying to answer include: If third-parties pay for, manage and operate smart city projects, who should own data on citizens’ living behavior? How will citizens give consent to provide data when tracking systems are built into the environment around them? How can the data be used? How granular can the data get? How can we assure citizens’ information is secure, especially given the spotty track records some of the major backers of smart city projects have when it comes to keeping our data safe?

The issue of data treatment is one that no one has really figured out yet and many developers are doing their best to work with cities and users to find a reasonable solution. For example, LinkNYC is currently limited by the city in the types of data they can collect. Outside of email addresses, LinkNYC doesn’t ask for or collect personal information and doesn’t sell or share personal data without a court order. The project owners also make much of its collected data publicly accessible online and through annually published transparency reports. As Intersection has deployed similar smart kiosks across new cities, the company has been willing to work through slower launches and pilot programs to create more comfortable policies for local governments.

But consequential decisions related to third-party owned smart infrastructure are only going to become more frequent as cities become increasingly digitized and connected. By having third-parties pay for projects through advertising revenue or otherwise, city budgets can be focused on other vital public services while still building the efficient, adaptive and innovative infrastructure that can help solve some of the largest problems facing civil society. But if that means giving up full control of city infrastructure and information, cities and citizens have to consider whether the benefits are worth the tradeoffs that could come with them. There is a clear price to pay here, even when someone else is footing the bill.

And lastly, some reading while in transit:

News Source = techcrunch.com

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