Technology

Electric scooter startups Bird and Skip have landed permits to operate in Portland under a new pilot program that aims to gauge how the controversial form of micro-transportation will fit in the city. And already therea bit of drama, or call it skeptical-scooter feelings, scuttling about.

The permits issued by Portland Bureau of Transportation will run until November 20, when the pilot program is set to end. Scooters could be available for rent as soon as this week, PBOT officials said.

The PBOT will conduct an evaluation of the program and survey Portlandersto determine whether scooters are compatible with the safe, efficient and equitable operation of Portlandtransportation system, the department said.

And while the official line from PBOT is neutral, thereat least one staffer whose snarky tweet suggests that the scooters are something more repugnant: just another toy for tech bros.

It all started after PBOT tweeted a PSA about the rules for scooters. In response, one person wrote, &Instead of preemptively shaming and chastising e-scooter users PBOT should be bending over backwards to encourage this alternative. I would like @PBOTinfo staff to reread the climate action plan, bike plan, and comp plan to come to grips with the magnitude of their failure.&

A staffer within PBOT wasn&t too pleased and posted this retort.

&Or maybe they&re toys that tech bros leave strewn about, blocking corner ramps needed for people with disabilities. Also, people need to know the helmet laws for scooters are different than for bicycles. We&ll see how it goes during this pilot period!&

And then later, another tweet. This time the PBOT staffer tries to walk back the previous comments. Another 15 minutes later and it looks like that staffertweeting privileges have been taken away.

The PBOT scooter skeptic, and the initial tweet that prompted the snippy response, is a symptom of a wider controversy bubbling up in densely populated cities throughout the U.S. as traditional car ownership — and the traffic congestion that comes with it — collides with public transit and newer forms of mobility such as ride-hailing, bike sharing and scooters.

The scooters have had a polarizing effect on residents living in cities. Some love the dockless scooter services because they provide a fast and cheap means of traveling short distances. Others loathe them, or more accurately, the misuse of them. (Scooters are supposed to be used in the bike lane, not on sidewalks.)

Still, the wave of scooters doesn&t appear to be slowing. Bird, for instance, launched in Portland and Cincinnati on Thursday. The company has launched in about 30 U.S. cities to date. Although not all of those have gone smoothly.

For instance, after Bird entered into Milwaukee on June 27, the city attorney issueda cease-and-desist letter and sued the scooter-share startup. The Milwaukee City Council is now considering a ban of all electric scooters.

Meanwhile, the streets of San Francisco remain scooter-less while theSan Francisco Municipal Transportation Agency continues its review of the 12 applications from companies to operate electric scooters in the city. Bird, Lime, Lyft, Uber and others have applied for permits to operate electric scooter-share services in San Francisco. The ban, and subsequent permit process, was the result of several startupsdeploying their electric scooters without permission.

Meanwhile, back in Portland, the number of scooters will be capped at 2,500, with each permitted company receiving a portion of the total.PBOT says it will continue to issue permits to companies that qualify under the pilot rules. In other words, Bird and Skip may soon have competition.

The PBOT is limiting the rollout, as well.Companies are allowed to deploy up to 200 scooters during its first week of operation. The department is also requiring that eachcompany deploy a portion of their fleets in East Portland.

State law requires scooter riders to wear a helmet and prohibits use on sidewalks.Riders will be required to park scooters on the sidewalk close to the curb, so that scooters do not interfere with pedestrians, according to PBOT rules.

HipChat, the workplace chat app that held the throne before Slack was Slack, is being discontinued. Also being discontinued is Atlassian own would-be HipChat replacement, Stride.

News of the discontinuation comes first not from Atlassian, but instead from a somewhat surprising source: Slack CEO Stewart Butterfield. In a series of tweets, Butterfield says that Slack is purchasing the IP for both products to &better support those users who choose to migrate& to its platform.

Butterfield also notes that Atlassian will be making a &small but symbolically important investment& in Slack — likely a good move, given that rumors of a Slack IPO have been swirling (though Butterfield says it won&t happen this year). Getting a pre-IPO investment into Slack might end up paying off for Atlassian better than trying to continue competing.

Atlassian VP of Product Management, Joff Redfern, confirmed the news in a blog post, calling it the &best way forward& for its existing customers. Itabout as real of an example of &if you can&t beat &em, join &em& as you can get; even Atlassianown employees will be moved over to using Slack.

According to an FAQ about the change, Stride and HipChatlast day will be February 15th, 2019 — or a bit shy of seven months from the date of the announcement. So if you&re a customer on either one of those platforms, you&ve got time to figure things out.

It doesn&t sound like any of Atlassianother products will be affected here; Bitbucket, Jira, etc. will carry on, with only the companyreal-time communications platforms being shuttered.

Hipchat was launched in beta form back in 2009, long before Slackdebut in 2013. It mostly ruled its space in the time in between, leading Atlassian to acquire it in March of 2012. Slack quickly outgrew it in popularity though, for myriad reasons — be it a bigger suite of third-party integrations, a better reputation for uptime, or… well, better marketing. By September of 2017, Atlassian overhauled its chat platform and rebranded it as as &Stride&, but it was never able to quite catch up with Slackmomentum.

While artificial intelligence was once heralded as the key to unlocking anewera of economic prosperity, policymakers today face a wave of calls to ensure AI is fair, ethical and safe. NewYork City Mayor de Blasio recently announced the formation of thenationfirst task forceto monitor and assess the use of algorithms. Days later, the European Union enacted sweepingnew data protection rules that require companies be able to explain to consumers anyautomated decisions. And high-profile critics, likeElon Musk, have called on policymakers to do more to regulate AI.

Unfortunately, the two most popular ideas — requiring companies to disclose the source code to their algorithms and explain how they make decisions — would cause more harm than good by regulating the business models and the inner workings of the algorithms of companies using AI, rather than holding these companies accountable for outcomes.

The first idea — &algorithmic transparency& — would require companies to disclose the source code and data used in their AI systems. Beyond its simplicity, this idea lacks any real merits as a wide-scale solution. Many AI systems are too complex to fully understand by looking at source code alone. Some AI systems rely on millions of data points and thousands of lines of code, and decision models can change over time as they encounternewdata. It is unrealistic to expect even the most motivated, resource-flush regulators or concerned citizens to be able to spot all potential malfeasance when that systemdevelopersmay be unable to do soeither.

Additionally, not all companies have an open-source business model. Requiring them to disclose their source code reduces their incentive to invest in developingnewalgorithms, because it invites competitors to copy them. Bad actors in China, which is fiercely competing with the United States for AI dominance but routinely flouts intellectual property rights, would likely use transparency requirements to steal source code.

The other idea — &algorithmic explainability& — would require companies to explain to consumers how their algorithms make decisions. The problem with this proposal is that there is often an inescapable trade-off between explainability and accuracy in AI systems. An algorithmaccuracy typically scales with its complexity, so the more complex an algorithm is, the more difficult it is to explain. While this could change in the future as research into explainable AI matures — DARPA devoted $75 million in 2017to this problem — for now, requirements for explainability would come at the cost of accuracy. This is enormously dangerous. With autonomous vehicles, for example, is it more important to be able to explain an accident or avoid one The cases where explanations are more important than accuracy are rare.

Rather than demanding companies reveal their source code or limiting the types of algorithms they can use, policymakers should instead insist onalgorithmic accountability— the principle that an algorithmic system should employ a variety of controls to ensure the operator (i.e. the party responsible for deploying the algorithm) can verify it acts as intended, and identify and rectify harmful outcomes should they occur.

A policy framework built around algorithmic accountability would have several important benefits. First, it would make operators responsible for any harms their algorithms might cause, not developers. Not only do operators have the most influence over how algorithms impact society, but they already have to comply with a variety of laws designed to make sure their decisions don&t cause harm. For example, employers must comply with anti-discrimination laws in hiring, regardless of whether they use algorithms to make those decisions.

Second, holding operators accountable for outcomes rather than the inner workings of algorithms would free them to focus on the best methods to ensure their algorithms do not cause harm, such as confidence measures, impact assessments or procedural regularity, where appropriate. For example, a university could conduct an impact assessment before deploying an AI system designed to predict which students are likely to drop out to ensure it is effective and equitable. Unlike transparency or explainability requirements, this would enable the university to effectively identify any potential flaws without prohibiting the use of complex, proprietary algorithms.

This is not to say that transparency and explanations do not have their place. Transparency requirements, for example, make sense forrisk-assessment algorithms in the criminal justice system. After all, there is along-standing public interestin requiring the judicial system be exposed to the highest degree of scrutiny possible, even if this transparency may not shed much light on how advanced machine-learning systems work.

Similarly, laws like the Equal Credit Opportunity Act require companies to provide consumers anadequate explanationfor denying them credit. Consumers will still have a right to these explanations regardless of whether a company uses AI to make its decisions.

The debate about how to make AI safe has ignored the need for a nuanced, targeted approach to regulation, treating algorithmic transparency and explainability like silver bullets without considering their many downsides. There is nothing wrong with wanting to mitigate the potential harms AI poses, but the oversimplified, overbroad solutions put forth so far would be largely ineffective and likely do more harm than good. Algorithmic accountability offers a better path toward ensuring organizations use AI responsibly so that it can truly be a boon to society.