Tag Archives: algorithms

The United States of Uber: Data, Money, and Power

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A consistent mistake we make in the era of the internet is to focus on the user experience and ignore the systemic effects. We love to gaze at the shiny object, but we don’t take enough time to step back and understand the big picture. The rise of Uber is a great example of this.

Uber is not a transportation or logistics company. For a long time I liked to argue that Uber was a media company that specialized in algorithms and data. However now I’m starting to see Uber as even larger than that, not just a conglomerate, but an actual sovereign entity.

The United States of Uber is an emerging federation of co-operating entities that uses data, to make money, and accumulate power.

At the heart of this rising state are the algorithms that sort data, and rank subjects. While we do not have a full picture of how these algorithms operate, we are getting glimpses of their purpose and effects via various initiatives and disputes.

For example, Judge Jed Rakoff of the United States (of America) District Court in Manhattan is currently hearing an antitrust case against Uber that argues the company conspires to fix prices (in the form of their surge pricing policy). In denying a motion to dismiss the case, Judge Rakoff acknowledges the power of the algorithm, citing its genius in his argument:

Defendant argues, however, that plaintiff’s alleged conspiracy is “wildly implausible” and “physically impossible,” since it involves agreement “among hundreds of thousands of independent transportation providers all across the United States.” Yet as plaintiff’s counsel pointed out at oral argument, the capacity to orchestrate such an agreement is the “genius” of Mr. Kalanick and his company, which, through the magic of smartphone technology, can invite hundreds of thousands of drivers in far-flung locations to agree to Uber’s terms. The advancement of technological means for the orchestration of large-scale price-fixing conspiracies need not leave antitrust law behind.

Judge Rakoff then proceeds to cite the case of the Silk Road and how the design of such systems, while facilitating automation, do not absolve the creator of agency or responsibility:

Cf. Ulbricht, 31 F. Supp. 3d at 559 (“if there were an automated telephone line that 15 Case 1:15-cv-09796-JSR Document 37 Filed 03/31/16 Page 15 of 27 offered others the opportunity to gather together to engage in narcotics trafficking by pressing "l,” this would surely be powerful evidence of the button-pusher’s agreement to enter the conspiracy. Automation is effected through a human design; here, Ulbricht is alleged to have been the designer of Silk Road .“). The fact that Uber goes to such lengths to portray itself – one might even say disguise itself – as the mere purveyor of an "app” cannot shield it from the consequences of its operating as much more.

Meanwhile a federal court in San Francisco will be hearing a case in June that argues Uber drivers should receive all the benefits and protections of employees, rather than be merely designated as contractors who are “sharing” their vehicles.

There’s interesting research by Alex Rosenblat and Luke Stark that contributes to the argument that Uber drivers are not contractors or at least not in a position to negotiate a proper contractual relationship. Their argument focuses on the persuasive if not coercive role of the Uber algorithm:

This empirical study explores labor in the on-demand economy using the rideshare service Uber as a case study. By conducting sustained monitoring of online driver forums and interviewing Uber drivers, we explore worker experiences within the on-demand economy. We argue that Uber’s digitally and algorithmically mediated system of flexible employment builds new forms of surveillance and control into the experience of using the system, which result in asymmetries around information and power for workers. In Uber’s system, algorithms, CSRs, passengers, semiautomated performance evaluations, and the rating system all act as a combined substitute for direct managerial control over drivers, but distributed responsibility for remote worker management also exacerbates power asymmetries between Uber and its drivers. Our study of the Uber driver experience points to the need for greater attention to the role of platform disintermediation in shaping power relations and communications between employers and workers.

Alex elaborates on this research in a recent post for the Harvard Business Review titled “The Truth About How Uber’s App Manages Drivers”, as well as her thoughts on the price-fixing trial.

The Guardian in January reported that Uber is using driver’s smartphones to monitor their driving habits, without their knowledge or explicit consent.

Research conducted on human subjects is something that raises significant ethical considerations, and yet this is a clear example of that, and suggests the company is probably conducting other research and analysis of their drives and passengers.

Which is why the issue of Uber’s power is so important. The data that the company is able to collect and correlate is substantive. On a surface level this includes data about traffic, about urban usage patterns, and about personal preferences, whether where you go (home, work, entertainment) or what you eat (UberEats). However it can also include much more valuable data, especially when it comes to facilitating or enabling marketplaces.

Take for example the company’s latest initiative, UberPITCH. Described as “a collaborative project that facilitates innovation within local startup communities” the service involves matching up a prospective investor with an entrepreneur who wants to pitch them an investment idea.

For several hours on a specific day in specific cities, Uber users are invited to try and catch a ride with a notable investor or venture capitalist who is open to hearing their pitch. You’re given 15 minutes, 7 to pitch, and 7 to discuss (1 minute to say hi I guess), before you’re dropped off and the next person gets in to make their pitch.

While UberPITCH is arguably a marketing stunt to get people signing up and using the service (not to mention talking about Uber as I am doing now) it also speaks to a potential diversification of Uber’s services based on data profiling. After all, Uber knows who uses the service, and they have rankings for all those users based both on driver/user reviews but also any other data they can get their digital hands on.

For example what if Uber got into the financial matchmaking service and was able to secure a small commission from any deal that they helped facilitate. What if Uber paid certain investors just to hear pitches, who after all are always looking for the next thing to invest in. What if they matched them up while they went about their usual commutes? What about dating in general? Too risky? Or can the algorithms adapt to that kind of sorting and ranking as well?

Given Uber’s growing knowledge of our urban environments and the people who move through them, there’s considerable potential for the company to facilitate all sorts of social structures, relationships, and transactions that potentially give them a sort of power that neither corporate conglomerates nor political parties or associations have traditionally possessed.

Let’s also not forget Uber’s track record of asking for forgiveness rather than asking for permission. Instead of working with public policy planners and elected representatives the company has consistently invaded a market, defied rules, and waited until their power and presence was unquestionable before sitting down to negotiate terms.

Philip Napoli, a Professor and Associate Dean for Research at the School of Communication and Information at Rutgers University, argues that institutional theory can help us understand and appreciate the role and power that algorithms play in our society.

With that in mind, what if Uber, as a collection of algorithms, is itself a new kind of growing institution? An emerging state that challenges the power and ability of existing nation states? A new society growing within the shell of the old?

Something to consider as Uber continues to grow and the lawsuits against it offer insights into the company’s practices and impact. What is their end game? Why are they going? What services and markets can they expand into?

Advice for humans who wish to govern

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I’d like to offer some advice if you want to get elected, or you’re in the fortunate position of wanting to stay elected. Maybe you’re a career politician, or you’d like to run for office for the first time. Perhaps you live in a democracy, able to vote for your government, or maybe you don’t, and you’re wondering why not. Either way, I offer some perspective, a different way of looking at the problem of government, that I encourage you to consider.

At the Academy of the Impossible I operated the Campaign School, which invited successful politicians, campaigners, strategists, and pollsters, to share their knowledge and wisdom about electoral politics. While the focus was primarily directed toward the needs of people running for office for the first time, there was valuable knowledge for anyone interested in democratic processes.

A Crisis of Legitimacy

Specifically one of the recurring themes we tried to address, was the crisis of legitimacy in contemporary democratic politics. Governments, political parties, and especially politicians are generally regarded with cynicism and disdain. People decreasingly trust their elected leaders, and the institutions they are associated with.

It is not so much an issue of apathy, but of relevance, contact, and broader issues of representation in an era of direct interaction. Even our notions of community have radically transformed, from geographically specific constructs, to fluid and flexible configurations based on interests, ethnography, demography, or whatever cool meme or trend is playing out.

Given that our configuration of community has changed, so too has our conception of what a representative is, and what that representative should be. A general consensus among Campaign School participants was that electoral reform was necessary and overdue. Our notions of representation have changed, and existing systems have not kept up at all.

However adjacent to the issue of electoral reform, an interesting insight around relevance emerges.

Specifically I’m observing that ideology is no longer relevant to contemporary electoral democracy. We are no longer electing humans to run a government, we are electing humans to operate a machine. A growing, and rather complex machine, driven by data, and connected to a global machine, whether global village, or global market.

Therefore if you’re a politician driven by ideas, organized with comrades around an ideology, I question whether you will find success. Circumstances may still provide opportunities to share your ideology on the stage, but will you be able to deploy and carry out your ideas? We can see this happening today in Greece, as a government elected on a specific ideological platform is compelled to bend to the needs of the larger regional and global machine.

Algorithms replace Ideology

This is why I argue that the era of ideology has been replaced by the era of algorithms. Where the twentieth century was all about ideas, the twenty first century is all about code, and in particular, the algorithm.

We don’t seem to have the time for ideas when living in the era of complex systems fuelled by massive databases and surveillance streams. Rather we just react to the flows. We respond to the trending topics and whatever flares up in front of us.

Ideology is about beliefs, whereas algorithms are about methods. Ideology requires a grand vision, whereas algorithms require applied practice.

Algorithms are how we process the firehose of information. How we process living in an era of information overload. Without exaggeration we depend upon algorithms to process and describe our reality. We’re relying less and less upon ideas and imagination, but instead upon digitally constructed realities that claim an authority we’re not (yet) able to argue with.

For an ideologue, the end justifies the means. For an algorithm, the process is the purpose, there is no end. The algorithm focuses on the process, whereas ideology focuses on that end. Perhaps there’s a warning here for democracy, as the algorithmic government, devoid of any constraints or controls, governs without an end.

In my lifetime this shift from ideology to algorithm has been simultaneously subtle, and pronounced. While we cling to the language and appearances of the old regime, the new regime has rapidly emerged and replaced the old.

The cold war became the cyber war. Nuclear Armageddon has been replaced by the singularity, skynet, and the robot apocalypse. Hackers are terrorists, terrorists are hackers, and we’re all freedom fighters in the battles for our mind.

What is your Algorithm?

Therefore, to the aspiring or successful politician, I ask you, what is your algorithm? What method do you bring to the table to manage the machines? Or at least to help us understand their commands?

The politics of the twentieth century were about grand ideas. Perhaps the politics of the twenty first century needs to be about small but effective solutions? Better methods instead of steadfast beliefs? Better practices instead of rigid visions?

As a politician, think about coming to the party with a practice, a method, and solutions for the problems that plague society. People don’t want to trust you, but they may give you the chance to help them to try and fix stuff.

To be clear I’m not suggesting algorithms are better than ideologies. I’m just observing a clear shift from one to the other.

For example I don’t see the Chinese Communist Party wanting to relinquish control of the Chinese Government anytime soon. Yet are they really communist or even ideological? Rather they act as an example of a regime that focuses on algorithms instead of ideology. They could expand their political process to allow greater elections and participation, not according to ideology, but based on algorithms. This would allow for good governance while also enabling the stability that is so coveted by centuries of Chinese politicians.

An Ideology for Algorithms?

What about embedding ideology into an algorithm? That’s certainly possible, though an example of new media using old media as its content.

Emit Snake-Beings argues something similar in their paper “From Ideology to Algorithm: the Opaque Politics of the Internet”. Specifically that algorithm has replaced ideology as a method of control, the former absorbing the latter, with a focus on the role of media. The power of the mainstream media being replaced by the power of search engines and social media.

Astrid Mager also argues that search engines have ideology. That capitalism has been baked into search engines, which certainly suggests algorithms could be programed with other ideologies? What would a communist, or anarchist search engine involve?

Manuel Schaeffer argues that ideology has ended in the face of big data:

“the interconnectedness of social, economic and political problems does not allow politicians to force their agenda upon reality anymore.”

Schaeffer and I both argue that this is tied to the erosion of trust and confidence in elected politicians, however he and I disagree on whether “the era of individual leaders with big visions is likely to be over.”

We Still Need Vision and Narrative

This is where you come in. I’m suggesting that you need to combine vision with your algorithm. You’ll need to develop some analytics that give you and your electorate an empirical view of reality and using that engine to offer vision and narrative. These are not mutually exclusive, in fact they are dependent upon each other.

Left to their own devices, the technocrats will do away with politicians, in the same manner that ideology is being discarded or made irrelevant. This is why the politician needs to evolve, needs to develop new skills, in particular combine algorithmic literacy with old school storytelling. The leaders we need are the ones who can make sense of our world, so together we might be in a position to do something about it.

We should not give up trying to make the world better, or to imagine a different world altogether. Rather you have to articulate the means and not the end. The plan and the policies rather than the vision and the promises.

It’s not where you’re going, it’s how you’re able to get us there.

As for myself, I require your help with the algorithms that govern attention. If my words have helped your understanding or provided any enjoyment please help me by sharing them with others. Amplify the article via your networks triggering certain algorithms to carry these words even further.

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