Over the past few years, the way people make purchases in China has been changing rapidly. In 2018, most people living in major Chinese cities now use their smartphones to pay for pretty much all of their purchases. Digital wallets in the form of from and from are the main cashless payment options available in China and are used in a diverse range of situations including paying in restaurants, shopping malls, street vendors, marketplaces and even as a way to donate to who put up boards displaying QR codes so that passers-by can quickly scan and pay on the move. The technology is helping small business holders and market stall traders keep costs down by using QR codes instead of expensive card readers. WeChat Tencent Alipay Alibaba buskers QR codes for mobile payments via Wechat and Alipay are seen on a seafood stall in a market in Beijing, China, 09 August 2017. Image Source: nationmultimedia.com On the face of it, it seems like the cashless revolution happening in China is a great example of how technology can provide more convenience and give us more control over our finances, providing us with easy to read reports which highlight what we are buying and how often. But, I can’t help being reminded of the conclusion that we came to at the end of our recent inspiration session on big data — “big data can no doubt be a force for good, but the question is can we trust ourselves to do the right things with the intelligence it brings?” Back in 2015, Ant Financial, an affiliate of Alibaba, plugged the gap in the Chinese credit market by launching its own credit-rating system. scoring service (known as Sesame Credit outside China) is linked to Alipay and uses five categories of user data to calculate a credit score: . Users can easily see how well they have done through the Alipay app and are encouraged to share their score with family and friends and even post it on social media. Zhima Credit purchases made using Alipay, personal information, timely payments on bills, timely payment on credit cards, and the number of friends on Alipay Alipay credit score screens. Image Source: qz.com The system works by awarding users a credit score between 350 and 950. The higher the score the more benefits a user is entitled to. This leads some people with lower scores describing being made to feel like they are part of a . Users with a ‘poor’ rating of say 550 find themselves having to pay higher deposits when booking hotels or renting bikes, whereas users with higher scores can experience the opposite. It’s perhaps not too different from our own credit rating systems in the West, perhaps more user friendly and accessible even, but the algorithms which generate the score still remain a corporate secret, and when you keep in mind that your score isn’t just driven by what you do yourself and is also affected by those within your social circle and that Baihe.com, China’s largest matchmaking company already uses Sesame Credit data as part of its service, what might initially seem like style customer value, suddenly starts to take on a darker more Orwellian hue. digital underclass Monzo Users are encouraged to share their Alipay credit score on social media. Image Source: techinasia.com Back in 2014, the Chinese government outlined an ambitious plan in a , to introduce a government-mandated social credit system by 2020. The government SCS seems to be quite separate from the WePay and AliPay ratings already in operation and whilst it isn’t entirely clear how the government will score its citizens and what indicators will be used, a recent suggests that the mandatory ‘trust score’ won’t be based on finances alone, and will also focus on rewarding ‘sincerity’ throughout society, essentially becoming a nationwide tracking system to rate the reputations of individuals, businesses, and even government officials. State Council planning document Wired article We perhaps shouldn’t be too surprised when the speculative, near-future scenarios that form the narratives of TV shows like Black Mirror start to filter into our day to day lives. Critical design and design fiction are after all based on direct observations of current behaviours and trends, which enable such provocations to feel possible. But it is perhaps because the blueprint for China’s proposed social credit system is so similar to ‘Nosedive’ ( ) that it’s hard to view the Chinese plans through anything other than the hue of an Orwellian filter. episode 1 Series 3 of Black Mirror Nosedive, ep1 Se3, Black Mirror — Lacie lives in a world where everything she does is rated and everyone has a score — Lacie’s is currently a 4.2 out of 5. Image Source: nerdist.com It’s worth considering those social observations that made Nosedive such a compelling provocation in the first place. The way we make up our minds about each other has also been evolving rapidly over the past decade or so. We no longer rely solely on observing body language to make unconscious decisions about a person’s character. Google allows us to peer into each other’s carefully curated lives, making judgments based on Instagram pictures, checking out Strava activity, weighing up career decisions on Linkedin or critiquing the tone of Twitter commentary. It might not be state sponsored, but most of us are running our own social credit systems and issuing our own scores on a daily basis. Whilst it might not be happening in quite the same way as China propose, governments around the world are also working out ways to make big data work for them. In the UK, HMRC have created their own ‘ ’ called Connect, so as we all continue to transfer more of our lives online, what happens in China should be of interest to us all, wherever we live. With our ability to glean knowledge from the huge amounts of data we are sharing not only publicly, but also through the agreements we hold with our service providers, it is perhaps important that we all keep one eye on speculative design in order to give us the occasional wake-up call we need to help us ensure that we end up with the kind of future we want. social network analysis software data mining computer Comment Originally published at www.bromfordlab.com .
