Welcome to the ChinAI Newsletter!

Thanks for reading the latest edition in our series of making AI as boring and unsexy as possible…

…as always, the archive of all past issues is here and please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: China’s Position in the RPA Market

This week we’re taking a peek into the landscape of Robotic Process Automation (RPA) — a process that involves using software to automate repetitive, rule-based processes (e.g. entering data into a form and transferring it to a CRM). The article comes from Yiou Intelligence, a think tank/consulting shop that operates as a “innovation service platform” that connects entrepreneurs, hosts summits, and gets funding from VCs.

Key Takeaways:

• The RPA market is small now but has great potential: RPA software revenues are the fastest growing part of the global enterprise software market, and the Asia-Pacific region’s expected growth rate in RPA of 181% in 2021 will be 3x the global rate. Plus, the integration of AI and RPA technology (think: using natural language processing to automate the writing of reports) will grow the AI market.

• The Chinese market, specifically, has a lot of space to grow: Close to 50% of Chinese companies are bystanders amidst the overall drive toward digital transformation. Total spending on IT in China in 2018 was about 1/5 of that in the United States.

• American firms dominate the RPA market: Yiou compiled an inventory of 40 firms competing in the global market — though there were a good number of Chinese companies on the list, the top 5 companies were all U.S. companies and they accounted for 47% of the global market (UiPath, Automation Anywhere, Blue Prism, NICE, and Pegasystems).

• What will the diffusion pathways of automated systems look like? Many small and medium-sized enterprises may not want to make “earth-shaking changes” to their business processes and are highly sensitive to short-term returns. Thus, RPA may be “their first stop in ‘testing the waters’ of digital transformation.”

FULL TRANSLATION: Five US RPA companies have grabbed half of the global market, does China still have a chance?

ChinAI Links (Four to Forward)

Must-read: The Question of Comparative Advantage in AI

By CSET researchers Andrew Imbrie, Elsa B. Kania, and Lorand Laskai — a superbly well-researched and comprehensive effort to tackle the state of play in AI between the United States and China. I really liked the framework that separated core elements of AI capabilities, critical enablers of AI development, and systemic drivers of national competitiveness in science and technology.

Should-read: The Design and Implementation of XiaoIce, an Empathetic Social Chatbot

Microsoft researchers present the development of Microsoft XiaoIce, the most popular social chatbot in the world. Dives into the design of the entire chatbot system, some evaluation metrics, as well as ethical concerns. Really worth a read — if someone wants to do a more bite-sized summary of the paper, would be happy to feature it in a ChinAI issue.

Should-read: Translation of Qianzhan Chanye Report on China’s AI Industry

In a November issue of ChinAI, I highlighted a cool overview-style 50-page slide deck on China’s AI industry by Qianzhan and covered a few translated slides. The translation team at CSET has translated the full thing (link goes to CSET’s translation page where you can download the translated PPT).

Should-read: Douyin’s 2019 user trends report translated

Katherine Wu has translated Bytedance’s 2019 data report on user behaviors and trends on Douyin (Chinese counterpart to TikTok). Her analysis highlights some of the unique subcultures on the app. H/t to my fellow Iowan Joseph Nelson for sharing this with me - his Des-Moines based company (Roboflow) is doing some cool work on computer vision apps.

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 PhD candidate in International Relations at the University of Oxford, Researcher at GovAI/Future of Humanity Institute, and non-resident Research Fellow at the Center for Security and Emerging Technology.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Welcome to the ChinAI Newsletter!

An early Happy Chinese New Year to all — if you haven’t called your mom or daughter recently, this week’s piece will really make you want to do that.

As always, the archive of all past issues is here and please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: A Mother Who Lost Her Only Daughter Decides to Maker Her into an AI

This week’s translation, of a piece in人物 (a Chinese magazine magazine that profiles celebrities and contemporary figures), tells the story of Li Yang and her daughter Chen Jin, who passed away due to T-lymphoblastic lymphona at age 14. If there was a way to have your loved ones stay by your side forever, what choice would you make? This week’s piece follows Li Yang as she tries to restore Chen Jin in the form of an AI companion.

Some excerpts follow:

In Li Yang's imagination, she can take AI Chen Jin anywhere: go to the cafe, to Jeju Island to look at the sea, to Australia, where there are lazy koalas and bouncing kangaroos, or Turkey, to take a ride in a hot air balloon to look at the scenery... they can travel together, talk and laugh together and share food, just like before.

According to a research by the Chinese Academy of Social Sciences, China currently has at least one million families whose only child has passed away. According to data from the Ministry of Health, this number is increasing at an annual rate of 76,000.

In September, Alibaba AI Labs received a private letter from Li Yang asking for help: "Hello, I have something that I hope you can help me with. My daughter has died, but I miss her so much. Can I send photos and videos of her to you so that you can make it into software that interacts with me in her likeness? "

On the synthesized recording of Chen Jin’s voice: The recording was of an essay written by Chen Jin, which recounted the story of her going hiking with her mother. When the girl climbed halfway up the mountain, she was exhausted and wanted to give up and go down the mountain. At this moment, "Mom smiled and answered meaningfully:" Child, remember that a famous person once said that success is persistence, and success depends not on the size of your strength but on how long you can persists. As long as you persist, you will be able to climb to the top of the mountain in no time! "After listening to her mother's encouragement, she, “pulled her mother's hand and rushed upwards, her fighting spirit re-ignited. Repeatedly gritting her teeth in persistence, exhausting her body’s chaotic energy. Finally, I finally climbed to the top of the mountain. I was so excited that danced for joy, jumping up and down, just like a general who won the battle.”

FULL TRANSLATION: A Mother Who Lost Her Only Daughter Decides to Make Her Into an AI

ChinAI Links (Four to Forward)

An All-GovAI week of links, featuring some work I didn’t get to cover in previous issues:

Must-read: GovAI 2019 Annual Report

It’s been an incredibly fruitful year from the team here at GovAI. This annual report provides a summary of what we got up to in this past year, expertly compiled by our head of ops and policy engagement, Markus Anderljung. As our Director, Allan Dafoe, writes in his note, “As part of our growth ambitions for the field and GovAI, we are always looking to help new talent get into the field of AI governance, be that through our Governance of AI Fellowship, hiring researchers, finding collaborators, or hosting senior visitors. If you’re interested, visit www.governance.ai for updates on our latest opportunities, or consider reaching out to Markus Anderljung (markus.anderljung@philosophy.ox.ac.uk).”

Should-Read: Who Will Govern AI? Learning from the history of strategic politics in emerging technologies

Jade Leung’s D.Phil thesis examines how the control over previous strategic general purpose technologies – aerospace technology, biotechnology, and cryptography – changed over the technology’s lifecycle. Specifically, she highlights out the relationships among the state, private actors, and researchers has evolved as the technology matured, highlighting key implications for how political dynamics may play out in the AI space.

Should-Read: Near term versus long term AI risk framings

Unpacks the divide between near-term and long-term AI risks into four different dimensions, including: what kinds of technological capabilities issues relate to, the immediate impacts of AI or possible impacts much further into the future, how well-understood or speculative issues are; whether to focus on impacts at all scales or to prioritize those that may be particularly large in scale. Interestingly, they note that proejcts focused on the intermediate scale of AI impacts may be receiving relatively less attention.

This paper by Carina Prunkl, a senior research scholar at FHI, and Jess Whittlestone (Centre for the Study of Existential Risk, Cambridge) was accepted to the AAAI AI Ethics & Society Conference taking place in Feb 2020.

Should-Read: The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse

Toby Shevlane and Allan Dafoe’s paper, also accepted in the AIES conference, examines publication norms in AI through an offense-defense framework. Crucially, they show that the existing conversation around AI has heavily borrowed concepts and conclusions from one particular field: vulnerability disclosure in computer security, concluding, We caution against AI researchers treating these lessons as immediately applicable. There are important differences between vulnerabilities in software and the types of vulnerabilities exploited by AI. It is therefore important to explore analogies with multiple fields and to consider any properties that may make AI unique. Ultimately, we suggest that the security benefits of openness are likely weaker within AI than in computer security.

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.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Welcome to the ChinAI Newsletter!

Happy 2020 to all — my vision hasn’t been this good since the Nintendo DS came out. Hope everyone’s holiday season was swell! As always, the archive of all past issues is here and please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: 大国智造谁来造 (China as a) Major Manufacturing Power — Who Will Do the Manufacturing?)

Context: Last November, a few representatives from the Alibaba ecosystem rang the gong to celebrate the company’s IPO for Hong Kong-based institutional investors. Yuan Wenkai, representing one of those partners (4PX Express logistics warehouse), stood third from the right. A former tally clerk who graduated from a run-of-the-mill Guangdong vocational school, Yuan is now an expert in automation management who increased the sorting capacity of the 4PX logistics warehouse by 20,000 orders per hour.

The core argument in this week’s longform article from jiqizhineng (synced): China still lacks a lot of technical staff like Yuan Wenkai (20 million senior technicans) if it wants to achieve a manufacturing transformation.

Key Takeaways:

• “The quality of the technicians will limit the effectiveness of not just automation technology but even those artificial intelligence technologies that can currently be implemented.” Supporting anecdotes: robotics startups can’t even give away free robots as part of product promos to manufacturing companies because they don’t have the technicians that can implement and debug the robot, let alone compile robot programs. In a shiitake mushroom sorting line, the help of experienced technicians and the factory manager were crucial to improve the algorithm’s recognition rate.

• Regarding China’s efforts to raise its industrial manufacturing level, the US-China binary is worn out. As was the case with a previous ChinAI issue on machine vision in quality inspection in the production chain, this piece frames China’s main competitors as Germany and Japan: On the proportion of senior technicians in the entire industrial workforce: Japan - 40%; Germany - 50%. China - 5%. On the diffusion rate of welding robots: Japan - 70%; Germany - 70%. China - 20-30%.

• Most importantly, this piece contests and complicates the notion of what talent matters most when it comes to realizing the potential of AI technologies, especially if we take the diffusion, maintenance, and large-scale adoption of technology as our focus rather than invention, innovation, and R&D: For instance, the recent CSET report on AI talent (which I’ll heap praise on in four links to forward section) takes a) AI PhD graduates and b) personnel with AI skills who work at AI employers as its proxies for AI talent. To be sure, the Ilya Sutskevers of the world are important but so are the Yuan Wenkais, the Ma Menglis (ChinAI #41) who label ladders at a data annotation company, and the CNC machine tool operators who are not PhD graduates but will play crucial roles in applying machine learning to train machine tools. In fact, in many contexts, they may be more important than the star AI researchers. Based on published catalogues of shortages in skilled trades, this jiqizhineng article claims that for many Chinese companies and regions seeking to leverage robotics and automation technology to transform their manufacturing industry, “the demand for CNC machine tools operators and equipment maintenance electricians ranked ahead of AI engineers.”

• Along the same vein, yes the Tsinghuas and Beidas of the world are important but so is Shenzhen Technology University (China’s first university of applied technology) and technical colleges in Jiangsu and Guangdong that will not appear on the rankings of elite universities: the last section of this week’s translation highlights how Chinese companies, educational institutions, and local governments seek to imitate the German “dual education system,” of vocational education and company apprenticeships.

DISCUSSED IN THE FULL TRANSLATION (in the style of Believer magazine): the first intelligent shiitake mushroom sorting line in China, monthly salaries of full-time Didi drivers, the Lewis Turning Point and other causes of the talent shortage

FULL TRANSLATION: China has a Shortage of as many as 20 Million Senior Technicians. (China as a) Major Manufacturing Power  -- Who Will Make it (大国智造谁来造)?

ChinAI Links (Four to Forward)

Must-listen: Heartland Mainland Podcast

Holly He and Matt Sheehan, of MacroPolo, spent the past year trekking around Iowa to dig into U.S.-China ties at the grassroots. Episode 1 of the podcast looks at the impact Chinese students have had at Iowa's largest universities. A highlight of 2019 was getting to show Holly and Matt around my hometown (Iowa City) and rep my alma mater (University of Iowa). At around the 12:40 mark, you’ll hear a little bit about how I changed my perspective on the challenges faced by Chinese students. Give it a listen and rating!

Should Read: CSET Report on Keeping Top AI Talent in the United States

Remco Zwetsloot, James Dunham, Zachary Arnold, and Tina Huang have published the most comprehensive, data-backed, and careful assessment of the U.S. AI talent landscape to date. The finding that stay rates among international graduates in AI are persistently high is particularly important: “Around 90 percent of international AI PhD students take a job in the United States after graduating, and more than 80 percent stay in the country for at least five years” AND “Stay rates are highest—exceeding 90 percent—among students from Taiwan, India, Iran, and China, and lower—around 75 percent—among students from European countries.” (p. iv)

This has implications for on the weight that U.S. policymakers put on technology transfer concerns, according to the authors:

A prominent 2018 report by the Defense Innovation Unit notes that 25 percent of graduate students in STEM fields are Chinese and that “nearly all [of them] will take their knowledge and skills back to China” because they “do not have visas to remain in the U.S.,” the implication being that U.S. universities are educating the country’s competitors without much benefit to the United States. As this report shows, that is not the case—with the vast majority of Chinese graduate students in fact staying in the United States— despite longstanding efforts by the Chinese government to draw them back.

Should Read: CSET Report on AI Safety, Security, and Stability Among Great Powers

Based on the authors’ own experiences participating in a number of Track 1.5 and Track 2 dialogues involving issues related to AI and U.S.-China relations, Elsa Kania and Andrew Imbrie provide an extremely sensible, thoughtful, and pragmatic roadmap for engagement on AI safety and security among the U.S., China, and Russia.

Specifically, they “present and evaluate several measures in AI safety and security that could prove feasible and mutually beneficial for future bilateral and multilateral interactions. These measures are intended to prevent or correct misperceptions, enhance mutual transparency on policies and capabilities, and contribute to providing safeguards against inadvertent escalation. By pursuing such initiatives in the near term, the United States can improve its capacity to leverage the benefits of AI, while mitigating the risks and managing the shifting terrain in today’s geopolitics, particularly among the United States, China, and Russia.”

Should Read: Alibaba’s Hong Kong Listing Offers Valuable Beijing Goodwill

Josh Horwitz of Reuters gives really useful context for the political reasons behind Alibaba’s strategy of dual listing in Hong Kong. This quote by a former Alibaba senior executive who declined to be named was a particularly insightful nugget: “Investors in the Hong Kong stock market are less influenced by the political atmosphere and have a more objective view of the richness of the Alibaba economy.”

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.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Welcome to the third episode of the ChinAI podcast, hosted by Jeff Ding. Our guest today is Valentin Weber, a DPhil Candidate in Cyber Security at the Centre for Doctoral Training in Cyber Security, University of Oxford. He joins the ChinAI Pod to discuss his latest report, “The Worldwide Web of Chinese and Russian Information Controls,” supported by the Open Technology Fund. It presents a typology of information controls (ranging from propaganda to surveillance), compares Chinese and Russian models of information control, and analyzes the possible causes and impacts of the expanding reach of Chinese and Russian information controls. Valentin’s findings are based on his own meticulous sourcing as well as his painstaking synthesis of secondary sources, documenting the diffusion of Chinese and Russian information controls to over 100 countries over the course of thirteen years.

More broadly, Valentin is interested in how the cyber domain is changing conflicts and state strategies. His current research focuses on the integration of cyber and grand strategy, as well as on the role of information controls in state strategies. He previously worked for the International Security Department at Chatham House.

*****Timestamps: Briefing Checklist (0:43); Debate the Guest (16:00); Footnote Fever (42:40); Trust the Process (51:45)

Welcome to the ChinAI Newsletter!

I like to think that there are objective criteria for how I choose each the feature translation for each issue of ChinAI. This week’s feature translation certainly meets many of those in spades: it’s well-argued and deeply researched, presents a fresh take on an important topic related to China’s AI scene, and it’s written by a smart Chinese scholar on her personal blog, which means many in the English-language audience may not have otherwise read it.

But sometimes I choose a certain translation to feature partly because those are the stories and narratives about China’s AI development that I especially want to share— one of those being that Chinese people are concerned about AI ethics, which was one of the five takeaways in my Year in Review post. That’s why I was especially drawn to this week’s critique of facial recognition, one that directly calls out the misuse of facial recognition by public authorities (not just commercial organizations). It’s not a piece representative of all discussions of the ethics of facial recognition in China. It doesn’t cover all the implications of facial recognition, such as the disproportionate targeting minorities.

But it does go farther than any other piece by a Chinese scholar I’ve seen in its strong opposition to facial recognition technology, framed in the broader diagnosis of “how the hysterical pursuit of security has brought to society not security at all, but complete suppression and panic.” It wasn’t an objective choice of a feature translation by a neutral observer. It’s a piece that presents a vision of public discourse in China about technology that I want to believe in and that I want for others to know is, at the very least, possible.

As always, the archive of all past issues is here and please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: Tsinghua Professor Lao Dongyan: The hidden worries of facial recognition technology

THE CONTEXT: Last month, after learning that the Beijing Subway will apply facial recognition technology to carry out screening security checks on passengers, Tsinghua Professor Lao Dongyan (劳东燕) posted an article on her public Wechat account expressing her worries about facial recognition technology. She has called for stricter regulations on facial recognition and was one of nearly 300 faculty and students at Tsinghua who signed a letter in support of Tsinghua Professor Xu Zhangrun (suspended for criticisms of the Communist Party).

THE ESSENTIALS:

• The essay is structured into four arguments against the use of facial recognition in the Beijing Subway as well as rebuttals to four possible counterarguments. The four arguments:

1. The relevant organizations and institutions have not proven the legitimacy of their collection method for sensitive personal information

2. The legitimacy of the new facial recognition measure is undercut without a hearing of the public’s views (e.g. the Beijing subway undertook a broad solicitation of the public’s views on a fare adjustment a few years earlier)

3. The standards for how the Beijing subway will conduct screenings are not transparent, could be arbitrarily set, and could be discriminatory.

4. There is not enough evidence to show that the use of facial recognition in subways can improve transport efficiency; even if there is evidence to prove this, efficiency itself is not a sufficient basis for implementation.

• She also rebuts four counterarguments that others have brought up in the context of this case:

1. Re: the counterargument that “some people may think that I am overthinking it, and I cannot appreciate and thank the government, as a father figure, for its protection and kindness,” Professor Law writes, “I can only say: forgive me, but I cannot accept this type of kindness…We must know that in our society, any personal data, as long as it is controlled by enterprises or other institutions, is also controlled by the government. Because this huge organization is run by specific people, this is equivalent to saying that all personal data, including highly recognizable biometric data, are controlled by a few people in that group…The people who control our data are obviously not God. They have their own selfish desires and weak points. Therefore, it is unknown how they will use our personal data and how they will manipulate our lives. Not to mention, such data may be leaked or hacked due to improper storage, leading to harmful results that may be exploited by criminals.

2. In response to some people who say that as long as you don't do bad things, you don't have to worry about the government controlling your personal data, Lao writes, “In a normal society, individuals should have the right to oppose any organization's arbitrary access to their personal biometric data. The law’s protection of an individual’s privacy and property rights and freedoms gives individuals a space for self-government, which cannot be infringed upon by others...If the biometric data of an individual can be obtained without consent in the name of security, do the legal protections of privacy and freedom of residence mean anything? Without privacy there is no freedom.

3. While some people point out that they are not important people which means that others presumably have no interest in learning about our personal information, Lao argues, “I can only say that when you put your personal safety issue on the neglect of others, you basically live like a dead gambler. And, you are not only betting on your luck, but you are also betting that the person who controls the data is an angel. To those who wishfully think they can win this bet, while I admire your ostrich-like character, I secretly think you probably need to pay some intelligence tax.”

4. Lastly, she calls out people who argue that even if this type of technology promotion has some issues, opposing it does not have any use, and they are too lazy to spend energy opposing it. “For issues that concern our own important rights and interests, I can only say that if we do not stand up and express our opposition and make our due efforts, it is naturally impossible to expect others to help call this out. How do you know that opposition is ineffective before you make the minimum effort? Even if opposition is ultimately invalid, it is better than tamely putting on your own shackles. At least we put in the work and struggled.

   As those who have had their rights violated, if we just endure this in silence and do not even dare to express our opposition, it is tantamount to helping the other party to scheme and hurts yourself…Because this is not a problem that can be solved simply by stubbornly tolerating it. Watching us go step by step towards the abyss, this was at least partly caused by our own stubborn forbearance.”

• She also shares her own personal experience living in an increasingly securitized China: “You need to show your ID when entering or leaving the university campus, have your ID card checked when you mail something, scan your face to check in at a hotel…Living in this society, I often feel that I am not trusted. Whether it is reimbursement for scientific research expenses, or constantly escalating security, what I can sense is an atmosphere of unlimited alert.”

• Her conclusion sticks the landing: “If this society has not yet fallen into a state of persecution and paranoia, it is time to say enough on security issues. The hysterical pursuit of security has brought to society not security at all, but complete suppression and panic.

  In the end, I solemnly recommend that the National People's Congress Standing Committee conduct a fundamental legitimacy review for the Beijing Metro’s measure to employ facial recognition for security screening. At the same time, it should consider initiating corresponding legislative procedures for a legal approach to regulating the arbitrary use of facial recognition technology.”

FULL TRANSLATION: Tsinghua Professor Lao Dongyan: The hidden worries of facial recognition technology

ChinAI Links (Four to Forward)

Must Read: Chinese Public AI R&D Spending: CSET’s Provisional Findings by Ashwin Acharya and Zachary Arnold

The authors conclude: “We assess with low to moderate confidence that China’s public investment in AI R&D was on the order of a few billion dollars in 2018. With higher confidence, we assess that China’s government is not investing tens of billions of dollars annually in AI R&D, as some have suggested.”

The footnotes (especially footnote 2) give a nice picture about how the meme of how China is outspending the U.S. on AI has been propagated. The author’s find that “China’s government probably isn’t dramatically outspending the U.S. government on AI R&D.”

Additional findings include: Chinese public AI R&D spending probably tilts heavily toward applied research and experimental development, not basic research. This is consistent with China’s overall public R&D spending; China’s government may be investing a few billion dollars a year (at most) in private-sector AI activity through guidance funds— essentially, state-backed venture capital funds. However, guidance fund spending is not properly considered R&D spending and is likely overstated.

Should Read: The Chinese Approach to Artificial Intelligence: An Analysis of Policy and Regulation

A new paper on the Chinese approach to AI from our neighbors at the Oxford Internet Institute: In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence (AI), entitled ‘New Generation Artificial Intelligence Development Plan’ (新一代人工智能发展规划). This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan ($150 billion) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. Through focusing on the policy backdrop, we seek to provide a more comprehensive understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.

Pages 15-17 on medical ethics were especially informative for me.

Should Read: Second Report of the Axon AI & Policing Technology Ethics Board: Automated License Plate Readers

In October 2019, Axon’s AI and Policing Technology Ethics Board, an independent advisory board created in 2018 to advise Axon (formerly Taser) on ethical issues related to artificial intelligence (AI)-powered policing technologies, released a report in response to Axon’s announcement of its intention to begin producing automated license plate readers (ALPRs), computer-controlled camera systems that read and record license plates. As the report explains, ALPRs are already one of the most widely used surveillance systems in existence, but they are severely under-regulated. A combination of rapid growth and lack of regulation has created an industry with little public accountability and has led to a variety of concerning practices, including the creation of massive databases with information on millions of innocent individuals. The Board’s report calls for comprehensive regulation of ALPR technology, and offers vendors (including Axon) a number of recommendations to make ALPR design more transparent and ethical.

Should Read: Can America and China be Stakeholders?

A wider-lens perspective from Robert Zoellick at the US-China Business Council’s Gala 2019 on the need to urge China to assume responsibilities as a “responsible stakeholder” in the international system.

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.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99