What are recommendation algorithms that China is cracking down on, and how do they affect internet users?

Cyberspace Administration of China has issued guidelines under which service providers must abide by business ethics and principles of fairness and should not set up algorithm models that entice users to spend large amounts of money or spend money in a way that may disrupt public order.

The draft is open for public feedback until September 26, China said, adding that the algorithms should not be used to create fake user accounts and users should be given the option to easily turn off algorithm recommendation services.

So what are internet recommendation algorithms?

Internet recommendation algorithms help users discover new products and services.

Internet companies globally use algorithms to predict user preferences and make recommendations.

In China, this would include firms such as e-commerce giant Alibaba Group, ride-hailing firm Didi Global and TikTok owner ByteDance.

Earlier this year, the Chinese Consumer Association criticised internet companies for misusing personal data and "bullying" people into purchases and promotions. State media have since issued multiple calls for regulating the use of such algorithms.

China recently passed a data security law that will go into effect on September 1 to protect internet users' rights in cyberspace and impose curbs on its fast-growing internet industry.

Also read| From education to bitcoin, China's season of regulatory crackdown

Impact on users

Internet platforms assert that they use automated recommendation systems to enhance users’ experiences on their platforms through personalised and relevant recommendations. Of course, in doing this, internet platforms -- from film streaming platforms to news sites and e-commerce portals -- also seek to retain user attention on their services.

This translates to significant financial benefits for the companies, as they can target users with advertisements and recommend further content to consume or items to purchase.

Definitions of “relevance” vary across platforms, and are largely based on what a platform believes a user is interested in through its data collection and inference practices.

In some cases, however, the use of algorithmic decision-making tools results in the recommendation of harmful content, such as misinformation, conspiracy theories, and extremist propaganda.

In some instances, these recommender systems yield discriminatory outcomes that reinforce societal biases.

Internet platforms that deploy automated recommendation systems do not currently provide meaningful transparency and accountability around how these systems are created and operated.

There is little visibility into how platforms have developed and crafted these recommendation systems, how they operate, and how they make decisions.

This makes it very difficult to analyse and combat the problematic recommendations that come from these systems.

Big Tech companies are facing allegations of lack of transparency and accountability from internet platforms.

This is especially concerning given that recommender systems have a significant amount of influence over how users engage with, and are influenced by, the online sphere.

Not only can recommender systems influence product purchases, but they also can determine what content, such as news articles, a user will even see.

China's regulatory crackdown

With market-trembling new rules and investigations, Beijing's crackdown on its most prominent companies has seeped into nearly every aspect of modern life, wiping billions of dollars from Chinese and Hong Kong-listed stocks and bamboozling investment sages.

From after-school tutoring to music streaming apps, and shopping to bike-sharing, stellar firms have been hit as Beijing tightens the leash on corporations, citing national security and antitrust concerns.

Whether motivated by the control reflexes of the Communist Party, or to avoid market contortions hurting the pockets and safety of the Chinese public, few expect this to be the end of the crackdown.

(With inputs from agencies)