ChinAI #75: What Trash Are You? - 46 Chinese Cities will invest 20b (RMB) in waste sorting
Plus, notes on the leaks re: Xinjiang's mass surveillance and the unclear role of AI in the story
|Jeffrey Ding||Nov 27, 2019|| 2|
Welcome to the ChinAI Newsletter!
At ChinAI we’re trying to find the treasure amidst the trash, and I especially like stories that look at what affects people in their daily lives, so this week’s translation was especially fun to prepare. This past July, Shanghai implemented a regulation that makes household waste sorting compulsory, 22,000 tons of daily household waste must be sorted into four buckets: dry, wet, recyclable, and hazardous. SCMP’s Inside China podcast did an amazing episode that gives great context on this. Many apps have popped up including one that lets users query which bin certain items should be sorted into:
The ranking of the ten most queried items:
1. dog poop wrapped in a napkin
2. dog poop wrapped in a sack
3. cat poop wrapped in a sack
4. dog poop wrapped in a newspaper
6. takeaway bags
7. bubble tea cups
8. soiled toilet paper (h/t to Ryan Soh, past ChinAI contributor for correcting this -- I had it as heated toilet paper before)
9. cray fish (only one labeled as wet waste)
10. nose booger wrapped in a napkin
*More takeaways in the feature translation section
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Full Translation: 46 Chinese Cities Will Invest 20 billion (RMB) to promote waste sorting. Where are the opportunities for AI companies?
A cool longform piece by Wei Pang and Tai Lang of jiqizhixin on the opportunities for AI companies amidst China’s push to have 46 major cities (including Shanghai) recycle 35 percent of waste by 2020. What I like about this piece is it takes the concept of AI + recycling and breaks it down into the front-end, medium, and back-end process of a city’s waste management process and then analyzes where AI may play a role in each. The piece’s finding of middling returns to opportunities for AI applications matches with the SCMP’s Inside China podcast take on narratives of hi-tech solutions, eco-dictatorships, and social credit punishments for incorrect trash sorting.
Why is there such an imperative for more waste sorting in Shanghai?
1) Land is getting more and more expensive and landfill storage (where 70% of Shanghai’s waste goes) takes up a lot of land
2) The landfill capacity squeeze has caused some high-profile scandals (e.g. the Taihu garbage dumping incident in 2016 when more than 20,000 tons of construction waste were transported from Shanghai and secretly dumped on an island in a neighboring province)
3) Shanghai is very different from other large cities in Europe and the U.S. since the proportion of household waste in SH is much higher. It’s very similar to Tokyo in many respects except only 3% of household waste in Tokyo went to landfills, and incineration as a method for waste reduction was as high as 75%. But NIMBY tensions have complicated the building of waste incineration plants in China, so the authors make a final comparison between Shanghai’s approach and Taiwan’s experience where landfills are also overwhelmed and waste incineration plants are also continuously being protested. Taiwan has focused on front-end reductions of waste, such as waste sorting, recycling, and reuse.
The numbers breakdown of why sorting is important and where companies are trying to carve out a niche
Front-end recycling process: Some rough calculations in Beijing context — for one ton of waste if there is no sorting at the frontend, the processing cost is about 2,000 RMB. With sorting, the cost can be reduced by about half or more. Some companies have designed essentially delivery apps for waste, but the labor costs are relatively high (6RMB to collect at the door when average customer’s waste is on average only worth 4RMB). Some companies have just put smart trash cans in the community, so residents who deliver their waste receive rewards from the platform, but many smart waste bins have already gone bankrupt. If AI is used for these smart bins, it’s mainly used for identity authentication via face scan. The big players are also involved. Alibaba’s e-commerce site Taobao has an AI waste recognition function. Alibaba, Xiaomi, and Jingdong have invested in electronic waste recycling companies.
Middle of recycling process: after waste is collected it has to be transported to a specialized sorting center. According to 2015 data, Beijing produces more than 20,000 tons of waste a day, and the daily costs of transporting this waste is three or four hundred million. In the authors’ view, there have been fewer setbacks in this segment, as many companies are working in the smart dispatch market to reduce waste transportation costs, though they note more prominent examples such as US company Rubicon Global and Swedish efforts in waste management.
Back-end of recycling process: at the waste processing plant, workers do labor-intensive, hard (150 RMB to go through 150-200kg of waste per day and find recyclables mixed in with the solid waste or filter out impurities not conducive to incineration), smelly work to sort waste. While this seems like an ideal application case for robots, interviews by the authors reveal that many robot startups have given up and most of the use cases come from abroad (e.g. Baidu Ventures invested in an American startup that uses machine vision and robotic arms for accurate waste identification and sorting)
ChinAI Links (Four to Forward)
Must Read: Exposed: China’s Operating Manuals for Mass Internment and Arrest by Algorithm by the International Consortium of Investigative Journalists
More than 75 journalists from the International Consortium of Investigative Journalists and 17 media partner organizations have revealed in the Chinese government’s own words how mass detention camps in Xinjiang are run. The leaked classified Chinese government documents include a November 2017 operations manual with detailed guidelines for managing the camps and four shorter intelligence briefings (“bulletins” for managing the Integrated Joint Operation Platform [IJOP], a mass-surveillance and predictive-policing program that analyzes data from Xinjiang)
Key points (Reported in Bethany Allen-Ebrahimian’s piece “Exposed”) — later notes will draw from other pieces collected in link above:
This is the largest mass internment of an ethnic-religious minority since WWII - “Bulletin No. 14” notes that in a seven-day period in June 2017, security officials rounded up 15,683 Xinjiang residents flagged by IJOP and placed them in internment camps (in addition to 706 formally arrested).
The manual reflects the dehumanizing treatment of inmates in the camps: despite instructions to ensure health and safety, eyewitness accounts have testified to torture, beatings, and death of prisoners. The manual reveals a points-based system that classifies inmates by degree of security required and rewards them for ideological transformation and compliance.
The ICIJ investigation emphasizes the government’s “artificial-intelligence-powered policing platform” (IJOP): this system amasses vast amounts of personal data via warrantless manual searches, facial recognition cameras, app usage to identify those who need to be detained.
Longer Reflections on how the role of AI in this story. I want to make clear that this is in no way a criticism of the incredible reporting by ICIJ nor should it detract from the main thrust of the piece or the notes above. This is more my personal reflection on what the significance of AI is to a story like this. What I’m trying to get my head around is the degree to which it is useful to characterize IJOP as an AI-powered policing platform as well as the degree to which drawing distinctions on these issues even matters.
James Mulvenon, who directs an intelligence contractor for several U.S. agencies, describes IJOP as “machine-learning, artificial intelligence, command and control” platform that substitutes artificial intelligence for human judgment and a “cybernetic brain” central to China’s most advanced police and military strategies.
But what does this “cybernetic brain” consist of? Allen-Ebrahimian writes, “IJOP then uses an as-yet-unknown algorithm to create lists of people deemed suspicious.” Based on my read of the four bulletins and other reporting, I would describe IJOP as a big data platform that probably uses very basic algorithms that you could perform on an excel sheet (e.g. counting up the number of red flags, such as foreign nationality or use of certain apps, to identify targets). It might even be a stretch to think that logistic regression is even being used let alone deep learning-based algorithms.
Don’t get me wrong. This type of granular data collection is concerning. Scilla Alecci’s piece highlights how users of the mobile file-sharing application called Zapya, which the Chinese gov. has flagged as a vehicle to disseminate religious material, have been targeted for further investigation. And the Human Rights Watch’s original report on the IJOP system highlights a range of other sources the platform collects data from (facial-recognition through CCTV cameras, wifi sniffers to collect identifying addresses of computers and smartphones).
I just want to tug on what exactly this "unknown algorithm” is, and why we are calling it AI. It ties into some of my previous posts about the AI abstraction problem and whether AI has become a meaningless concept like ICT. The use of facial-recognition (pretty clear application of AI) to identify people is one component of data collection, but that doesn’t amount to an AI brain that produces lists of people deemed suspicious. Anyways these semantics don’t mean that much to the people facing persecution and separation from their families, and that should be the main takeaway here. But I think there’s something useful to pull out here about how we assign causal power to technologies and whether differentiating between the specific types of technologies that are enabling a certain situation to occur matters at all.
Should Read: Hong Kong Holds a Protest Election but Will it Produce a Protest Result? by Suzanne Pepper
The answer was a resounding yes as pro-democracy candidates now control 17 of the 18 District Councils. Suzanne Pepper’s piece on the eve of the election gives excellent context for:
the function of the District Councils (neighborhood-level problem solving and implementation of gov policies)
why they matter (feed into more democratic influence in choosing Chief Executive and seats in Legislative Council)
why pro-Beijing candidates had dominated the councils before the recent election (better organization and splintering of pro-democracy forces).
Very relevant to this week’s translation: Richard Brubaker, long-term Shanghai resident and founder and managing director of NGO Collective Responsibility, talks about his company’s work to map the pre-existing network of informal recyclers and trash pickers, and how the new four bin system has affected that.
Brubaker also wades into media reports dominated by stories of hi-tech surveillance and social credit punishments for incorrect trash sorting and talks of the day-to-day realities for Shanghai residents he’s witnessed.
By Jenny J. Lee and John P. Haupt from the University of Arizona, published in Higher Education
Summary: Based on a scientometric study of Scopus science co-publications between the U.S. and China over the past 5 years, this study found that US research article publications would have declined without co-authorship with China, whereas China’s publication rate would have risen without the USA, challenging US political rhetoric and attempts to curb international research engagement with China and demonstrating the benefits of US scientific collaboration with China for both the US nation-state and global science.
Thank you for reading and engaging.
These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is a Rhodes Scholar at Oxford, PhD candidate in International Relations, Researcher at GovAI/Future of Humanity Institute, and Research Fellow at the Center for Security and Emerging Technology.
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