Voici les 3 news dont vous trouverez le résumé au format bullet points plus bas :
Citizens in the ‘good credit’ region (600–650 and above), which is roughly the average score at the moment, can unlock a range of benefits that can be bucketed into 3 categories:
1. deposit free access,
2. discounted access,
3. priority access to services.
In many schemes, more about carrots than sticks, citizens can lose points but receive no direct penalty for a low score.
The carrots in this case take up added importance as being denied access to carrots become the only punishment when there are no sticks for citizens with low scores.
Much has been written on China’s Social Credit System (SCS) in global media, including often inaccurate portrayals attributing the denial of some citizens’ ability to buy high-speed rail and flight tickets to a ‘low social credit score’.
For example, Fuzhou’s platform collects data from over 630 such entities. The data broadly is classified into includes 4 types:
basic data: education, employment status, occupation, marriage status, professional qualifications,
positive credit: government conferred honors, contributions to public welfare,
notices: over-due loans, utility payments (water, electricity, etc),
bad credit: legal violations (civil, administrative, criminal).
in these 2 cities, people’s scores are not impacted at all by data from the private sector. So for example people’s online purchases or social media posts, has no impact on the score.
Some of the key areas that would boost scores into the outstanding credit includes: a citizen’s timely contribution to the city social security or insurance fund; activities such as volunteering, donating blood, using public transport, separating waste; working in areas of public interest such as teachers or doctors
Xiamen uses the FICO score model — used in the United States by mainstream credit rating agencies to assess financial credit worthiness, but remixed with a different set of variables.
Neither of these models use machine learning based technologies such as predictive scoring. Therefore the specific question of AI blackbox decision making, a highly controversial issue associated with the application of AI across several industries, is not applicable. Both governments claim to be working on bringing in machine learning (ML) and see Alipay’s Sesame Credit, which uses ML in its scoring model, as the the industry benchmark which they are looking to emulate
According to data shared with us, Fuzhou has 1.7 million registered Moli score users (roughly 21% of the city population) and Xiamen has just 210,059 (roughly 5% of city population).
In its present iteration the scores seem more like a government version of a loyalty scheme — all citizens get access to the basic service however some can opt-in for fringe benefits for convenience and comfort. Initial data suggests a very low level of awareness about the score in both cities
In Xiamen, outside of the library, we did not meet anyone that even knew about the score. There was no advertising or government propaganda around the city either. When asked about this, officials in both cities emphasized a word-of-mouth strategy rather than a concerted top-down propaganda effort that tends to accompany major policy efforts, further reflecting the early-stage experimentation nature of the initiative.
With no data from private sector to call upon, where a large amount of a citizen’s digital footprint is generated, it remains to be seen how successful the models built by city governments can be a proxy of an individuals ‘trustworthiness’, and whether the broader system of rewards built around it lead to citizens becoming more law abiding in any meaningful way. As one representative from a company that build credit scoring applications in Guizhou shared at a conference in Beijing, there are no new algorithms and not useful enough data sets
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