Category: smart cities

On his occasional blog Speedbird, Adam Greenfield has written an entertaining and incisive blogpost about the ‘mobility brokers’ Uber – the software-sorted unlicensed alt-taxi providers.

The post is worth a read for the trenchant dissection of how Uber is a kind of sigil of some of the questionable politics arising from the so-called ‘smart city’. For example:

– Interpersonal exchanges are more appropriately mediated by algorithms than by one’s own competence.
This conception of good experience is not the only thing suggesting that Uber, its ridership or both are somewhat afraid of actual, unfiltered urbanity. Among the most vexing challenges residents and other users of any large urban place ever confront is that of trust: absent familiarity, or the prospect of developing it over a pattern of repeated interactions, how are people placed (however temporarily) in a position of vulnerability expected to determine who is reliable?

Like other contemporary services, Uber outsources judgments of this type to a trust mechanic: at the conclusion of every trip, passengers are asked to explicitly rate their driver. These ratings are averaged into a score that is made visible to users in the application interface: “John (4.9 stars) will pick you up in 2 minutes.” The implicit belief is that reputation can be quantified and distilled to a single salient metric, and that this metric can be acted upon objectively.

Drivers are, essentially, graded on a curve: their rolling tally, aggregated over the previous 500 passenger engagements, must remain above average not in absolute terms, but against the competitive set. Drivers whose scores drop beneath this threshold may not receive ride requests, and it therefore functions as an effective disciplinary mechanism. Judging from conversations among drivers, further, the criteria on which this all-important performance metric is assessed are subjective and highly variable, meaning that the driver has no choice but to model what they believe riders are looking for in the proverbial “good driver,” internalize that model and adjust their behavior accordingly.

What riders are not told by Uber – though, in this age of ubiquitous peer-to- peer media, it is becoming evident to many that this has in fact been the case for some time – is that they too are rated by drivers, on a similar five-point scale. This rating, too, is not without consequence. Drivers have a certain degree of discretion in choosing to accept or deny ride requests, and to judge from publicly-accessible online conversations, many simply refuse to pick up riders with scores below a certain threshold, typically in the high 3’s.
This is strongly reminiscent of the process that I have elsewhere called “differential permissioning,” in which physical access to everyday spaces and functions becomes ever-more widely apportioned on the basis of such computational scores, by direct analogy with the access control paradigm prevalent in the information security community. Such determinations are opaque to those affected, while those denied access are offered few or no effective means of recourse. For prospective Uber patrons, differential permissioning means that they can be blackballed, and never know why.

Uber certainly has this feature in comment with algorithmic reputation-scoring services like Klout. But all such measures stumble in their bizarre insistence that trust can be distilled to a unitary value. This belies the common-sense understanding that reputation is a contingent and relational thing – that actions a given audience may regard as markers of reliability are unlikely to read that way to all potential audiences. More broadly, it also means that Uber constructs the development of trust between driver and passenger as a circumstance in which algorithmic determinations should supplant rather than rely upon (let alone strengthen) our existing competences for situational awareness, negotiation and the detection of verbal and nonverbal social cues.

Interestingly, despite its deployment of mechanisms intended to assess driver and passenger reliability, the company goes to unusual lengths to prevent itself from being brought to accountability. Following the December 2014 Delhi rape incident, police investigators were stunned to realize that while Uber had been operating in India for some time, neither the .in website nor any other document they had access to listed a local office. They were forced to register for the app themselves (as well as download a third-party payment application) simply so they could hire an Uber car and have the driver bring them to the place where he believed his employers worked. Here we see William Gibson’s science-fictional characterization of 21st-century enterprise (“small, fast, ruthless. An atavism”¦all edge”) brought to pungent life.

Read the whole article.