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Why Uber & Ola do not care about riders & drivers?

Global market for ride hailing is pegged at anything above USD 85 billion dollar annually. The market is not only huge but also growing at breathtaking pace of around 17% . To put things in perspective at this pace of growth the market is set to double every 4.5 years. Compare that with your bank deposit which will take 15–20 years to double if you are living in developed countries or developing economies like India and China.

The market has been evolving over the decades with few innovations here and few there. However, advent of category defining company Uber has upended the industry in unprecedented manner. The beauty of Uber platform lies in innovative plumbing of technologies developed before it but after early 2000s. Chief among them are smart phones & Google maps . Such has been the success of Uber’s model that it has assumed an envying place in english language i.e. “Uberization” . The entire shared economy as we see today is inspired by Uber.

And many copycats have also emerged in the same market as Uber salivating the prospects built on the size and growth of the industry.

So What’s the problem?

The very solution which distrupted the market is becoming the key problem and it seems that the market is ready for yet another decadal change.

A little background will help before we move on. Uber relied on efficient matching of drivers and riders by signaling power of prices or fares. It has. utilized its prediction engine combined with real time data to change the prices to match supply with demand or vice versa. The engine increases price to attract drivers to pockets of high demand & reduces the price where the demand is muted. The trick has enabled it to provide more business to its drivers and increased utilization of their vehicles. On the other hand it has successfully provided reliable (really?) vehicle availability to the riders.

But the engine has created problems of its own principally those related to ethics & fair dealing.

I have not understood. Please explain!

The model in which Uber works relies on platform effect. Simply put the higher the number of users on its platform higher will be the value of the participants. For example, an additional driver will ensure more choice and increased competition thereby reducing tariffs to riders or open up new routes. Also, a new rider will increase the earning potential for drivers thus attracting ever more drivers. So once the flywheel started moving it will gather momentum oon its own.

The downside of this mechanism is that the model makes the market winner-takes-it-all. So market will only have 1–2 players to have enough scale to provide value to riders ( choice or fare) or to drivers ( higher business) . Now the winner or couple of winners will have control of market. This is evident in today’s market where drivers pay high commissions and riders accept surge pricing for convenience ( not that they want to). The fares do not reflect economic costs but the level of dependency riders have on Uber and likes. The fares are based on “Willingness-to-pay” which is a euphemism for gouging money as much as can be exracted based on the desperation of riders. A few of you may know that fares not only acccount for distance or demand-supply mismatch but also what is the battery level of your phone which may make you desperate to accept fares.

Moreover, the algorithms used by the incubments use what is called Machine Learning ( ML)) . The programs built on this technology are useful in many situations but inherently biased. The ML models are built by feeding lots of data and finding a pattern which can then be utilized for predictive purposes.

However, many of the readers would know that these models perpetuate the bias in data. For example , many studies have discovered that crime prevention models based on ML have shown bias against minorities and backward section of the society . This has led it further supression of these sections .

Similarly , ML models used by ride hailing app are fed on non-representative data of many situations. For example , on a rainy day couple of riders have accepted very high fares. This will be fed back to model which will show yet higher fares to subsequent riders. It may lead to complete breakdown of demand supply matching framework apart from raising ethical questions.

The unencumbered use of technology is not beneficial for even the drivers who may miss out on business due to high fares.

What’s the solution?

This article does not, in any way, deprecate the use of technology. But strongly backs to augment human capabilities with the use of technology . The decision making ought not be left to machines but it must be enhanced by efficient processing of information.

So can we expect some changes?

Definitely, the market is big & growing and perhaps the users will also want to try out the alternatives to the incumbent.

Source: This article has been originally published on Hawp