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Data ethics, Done right.

Ethical Data Usage policies of Uber.


Uber Technologies, widely known as Uber- is the world’s largest ridesharing company of the decade. Based out of California, Uber has almost 6 million drivers across the world as of 2024. From helping customers reach their destination, delivering the customer their ordered food to many other ventures, Uber has been doing it all. Uber also has been the point of controversy over the years. From unethical data usage accusations to false collection of data, Uber has been involved in every other shady news in the past. However, the company has lately been very wary of how they use data and setting an example of using ethical data mining to improve its operations and leverage customer usage experience. 

In this article, we will see about the different methods  that Uber uses in Data Science to maintain ethical standards and improve the overall ridesharing experience for both drivers and riders.

Understanding Uber’s algorithms

Uber's framework is truly curious and complicated. They have a lot of drivers, over 6 million around the world as of 2024.

To analyse data for requesting rides and fixing fares, Uber makes use of different algorithms to ensure smooth run of operations. One of such many algorithms is Dijkstra,which is used to find the shortest distance between two given points.

The Dijkstra algorithm is a widely known algorithm used to solve the shortest distance problem. It works in the way of starting the vertex to all the nodes in between till the end point, precisely picking the shortest route with respect to the vertex . At that point, it iteratively chooses the vertex with the smallest distance and upgrades the distances of its adjacent until the specified destination is reached.

Uber also uses another algorithm called the shortest path algorithm. This algorithm is useful in the driver taking the shortest yet efficient route to be taken while picking up passengers on time. It takes various points like traffic congestion, live status of road maintenance to optimise the route taken, thus minimising the overall time taken.

By using these algorithms, Uber ensures that its drivers reach their destinations quickly and effectively, improving customer experience and dependability. Moreover, these algorithms offer assistance in reducing wait times and give a seamless and productive ride-hailing experience for the users

How Uber uses Data Science

Data driven decision making

One way that Uber utilises data science ethically is by using data to make important decisions. For example, while fixing the price for a ride,the company considers factors like time of the day, location, traffic and peak times in the first place. This makes Uber take a stance in being responsible and transparent to both customers and drivers.

In addition, Uber also uses data to monitor whether the driver is complying to the company's code of conduct in various manners like safety, hospitality and timeliness. This includes monitoring various legal and general aspects like speed, traffic rules, lane discipline and hospitality etc,. By doing this, Uber easily identifies the potential safety concerns, taking actions to curb them and improving the customer experience further.

Driver Ratings

In addition to the driver rating framework, Uber also employs machine learning to analyze the feedback provided by riders and drivers. The machine learning algorithm helps in identifying patterns and insights that can aid in improving the overall ride-hailing experience for both riders and drivers.

Uber has also collaborated with researchers at Princeton University to identify and eliminate instances of discrimination in the driver rating system. The study found that riders rated drivers from minority backgrounds significantly lower than drivers from non-minority backgrounds, indicating potential bias in the rating system. Based on the findings, Uber made changes to its rating system to eliminate discrimination and protect against potential bias, such as no longer showing riders the driver's rating until after the ride is complete.

Uber's approach to the driver rating framework demonstrates its ethical commitment to creating a fair and balanced rating system for both riders and drivers. By leveraging the power of machine learning and collaborating with external researchers, Uber is continuously refining and improving its algorithms to better serve its users while also avoiding biases and ensuring a seamless ride-hailing experience.

Surge Pricing 

Surge Pricing is an algorithmically fuelled technique that Uber (and now a lot of other on-demand companies) use when there is a demand-supply imbalance. A demand-supply imbalance occurs when there is a downward shift in both the rider’s demand and driver’s availability.

The reasons for surge pricing are normal peak-hours, bad weather conditions (rain, snow, etc), events (concerts, movie-premiere), traffic conditions, unseen emergencies and so on.

During such a time of the rise in demand for rides, fares tend to usually soar high to make sure that those who need a ride can get one reliably and not rely on luck or the driver’s choice. At the core it is closely related to the principle of a free market economy i.e. it helps ensure that the consumers who really want the thing they are looking for, get it.

Is surge pricing essential?

Surge pricing is essential in a way that it helps in matching the drivers' efforts with the demand from consumers. It ensures that drivers are not idle or roaming around the city searching for a potential customer. Surge pricing brings about three changes to the market:

  • Reduces the demand for cars 

  • Creates a new stream of supply

  • Shifts the supply of drivers to areas of high demand.

Surge pricing for any trip is based on riders’ location. As in, a driver might get a ride request when he is in a surging area but it is possible that the rider is in a non-surging area.

How is surge pricing calculated?

This calculation is from Uber’s original website - An ideal scenario is when the number of riders and drivers are equal. So if there are M drivers and N riders then they can be easily mapped to one another. The crisis situation arises when the number of riders is M and the number of available drivers is M-N.

As Uber’s surge pricing algorithm increases the prices, this, in turn, motivates N more drives to hit the roads. As the utilisation rates increase, Uber drives the cost down to normalcy.

Promoting Diversity and Inclusion

In addition to making changes to its rating system and collaborating with researchers, Uber has also taken steps to ensure that they operate in compliance with regulations, including the GDPR (General Data Protection Regulation).

The GDPR is a European Union regulation that sets strict rules on how companies collect, store, and process personal data. Uber has adapted its data policies to meet GDPR requirements, reflecting the company's commitment to responsible data handling and protecting user privacy. They have implemented strict data security measures, including using encryption technologies and limiting access to user data.

Uber's prioritisation of data science and compliance with regulations like GDPR reflect its commitment to building a culture of responsible and ethical use of data. By using data science, Uber can tackle issues like rider and driver discrimination and ensure that its services are accessible to all while protecting user privacy and data security.

For example, Uber has implemented different initiatives to empower more drivers from underrepresented groups to join the platform. The company has moreover analysed its data to recognize areas where there may be disparities, such as in pay or driver appraisals, and has taken action to address these issues.

Additionally, Uber has worked to make a more comprehensive culture inside the company by using data to identify predispositions and promote diversity and inclusion in hiring and promotion choices.

Protecting User Privacy

Finally uber gives first preference to ethical usage of data by prioritising data privacy. The company takes incredible care to secure user data and guarantee that it isn't abused or misused in any way.

For example, Uber has implemented various security measures to avoid data breaches, such as encrypting delicate user data and closely monitoring access to it. Furthermore, the company only uses user data for particular purposes, such as to facilitate rides and improve the general and overall ride sharing experience.


As we have seen, Uber has made noteworthy strides in utilising data science ethically. From data-driven decision making and driver appraisals to promoting diversity and protecting user privacy, the company has executed different measures to guarantee that it operates in an ethical and dependable way.

Whereas no company is perfect, Uber's use of data science illustrates a commitment to improving the overall ridesharing experience whereas maintaining moral measures. By proceeding to prioritise data ethics, Uber can continue to build trust with both its drivers and riders and further cement its position as a pioneer within the ridesharing industry.


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