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In the modern world, Artificial Intelligence (AI) is being leveraged across various industries to tackle issues as diverse as inventory management in retail and route optimization in navigation. Due to its immense potential, AI is increasingly being used in pertinent areas such as finance, marketing, and human resources – which raises the question: will the use of AI in these (and other fields) remedy or amplify problems that lend themselves to flawed decision-making? This article will delve into the matter of ‘fairness’ in AI systems, elaborate on real-world instances of AI-based discrimination, discuss existing approaches towards mitigating AI bias, and more.  
 

In July 2021, Google announced that it would update the data safety section applicable to apps on their Play Store. The company stated that these policies would come into effect in late April 2022, and it followed through on that declaration. Last month, numerous apps were removed from the Play Store for breaching Google’s updated data privacy and data processing policies.  

Similar to other industries, financial services have been leveraging mobile location data to improve its operations and services.

Investment firms analyse mobility data in conjunction with POI data to forecast revenues for various retail outlets. This enables these companies to determine which businesses will deliver a good ROI.

Retail banks utilise location data in a number of ways. An important application includes limiting or preventing fraudalent transactions. Retail banks compare the IP address of a Point-of-Sale (POS) with the GPS coordinates provided by a customer's phone to prevent credit card fraud.

Banks rely on location intelligence to drastically improve the experiences they provide their customers. Here are three examples of how retail banks can use mobility data to enhance customer service and increase competitiveness. 

In each example, anonymized mobility data around Downtown Los Angeles between 1 October 2016 and 31 October 2016 was used.

Location data is collected from multiple sources of varying quality GPS signals from mobile devices, beacons, and WIFI connections, the notorious Bidstream, and more. In most cases, even genuine location data cannot represent the entire population of the region. This discrepancy can be attributed to smartphone penetration in the country, app-specific demographic variations, hardware inconsistencies, and sources of location data.

To perform meaningful analysis that accounts for mobility patterns and other trends in a larger region, data scientists use projection models to make an accurate estimation of a region’s population and normalise data counts to fit the use case. This is called data extrapolation.

Image of people crossing outside buildings

To truly map human behaviour to effective mobile marketing, online data is not always the best data for brands to base their advertising campaigns on.