Authorities in Beijing have released the latest version of the country’s draft Personal Information Protection Law, affirming that it further improves rules on the processing of personal information, targeting in particular the collection of personal information and “big data killing.”
Big data killing refers to the phenomenon whereby a product’s price, when seen by returning customers, is set much higher than new customers for the same goods or services.
According to a survey conducted by the Beijing Consumers Association, over 80% of respondents believed that big data killing was quite common, and over 50% of the respondents said that they had experienced big data killing practices personally.
At the beginning of this year, the China Consumers Association said that some firms used algorithms to discriminate in pricing practices. For example, different prices are set by companies for new and old users, but the price that members will see is higher than that of ordinary users. Other companies may set different prices for consumers located in different regions. Users who browse web pages many times may face price increases. Some enterprises even use complicated promotion rules and algorithms to implement price confusion settings to attract certain consumers who have difficulty in calculating real prices.
A reporter from Economic Information Daily conducted an experiment on a online ride-hailing platform and found that the fare displayed to users who often used the platform at the same time and at the same place was estimated at 69.03 yuan ($10.65). However, to users who didn’t often use the platform, the fare was estimated to be just 51.18 yuan, including 17.05 yuan of a deducted coupon. However, the reporter found that even if the user didn’t use coupons to deduct the fare, the users who didn’t often use the platform still received a quote of 68.23 yuan – cheaper than the fare displayed by common users by 0.8 yuan.
Experts believe that the deeper problem of “big data killing” lies in the improper protection and utilization of user data by large tech firms. In addition to enhancing the supervision intensity and increasing the legal penalties for related behavior, they contend it is necessary to continuously strengthen institutional constraints.