Analysis from the study by Anya Schiffrin, director of the Technology, Media and Communication specialization at the School of International and Public Affairs of Columbia University in the USA.
When concerns about online misinformation spread after the 2016 US election, there was hope that tech giants would use artificial intelligence (AI) to clean up the mess they created. The hope was that platforms could use AI and Natural Language Processing (NLP) to automatically block or take down false, illegal, or inflammatory content online without governments having to regulate.
Free speech organizations and human rights activists, among others, were concerned about corporate censorship and unaccountable entities making decisions about what is shared online, but to many, this seemed like a convenient way to limit some of the damage caused by false information online.
Now, it is clear that while AI has a crucial role to play in controlling what is seen online, government regulations can play an important role in determining how such methods will be used.
There are several startups that use AI, NLP, and Pattern Recognition with Machine/Deep Learning training algorithms to simulate human learning, identify actor networks, and analyze traffic patterns to spot accounts that behave as if they are using a high level of automation, or may be bots. We found that there was much less of a market for the services offered by these companies than they had initially hoped, and that Google and Facebook generally do not hire small firms to do this type of monitoring.
It has also become clear that the problem of online misinformation/disinformation is not one that can be solved by the market or technology alone. In 2017, I proposed that we look at online misinformation/disinformation regulations as supply-side and demand-side solutions.
Some focus on audience responsibility, while others look at the supply of bad/misinformation online. Using AI to screen and remove or reduce online misinformation is a supply-side solution. But our latest research confirms what many others have found: that algorithms and AI alone simply won’t solve the problem.
For one thing, the financial incentives to produce and/or circulate false information online are enormous. For another, there are many reasons why people believe or act on information that is false or inflammatory.
“Disinformation and disinformation have been treated as technical issues. That’s the agenda of the big tech players. But increasingly, the elements are not technical. They are political, economic and legal,” said Alejandro Romero, chief operating officer and co-founder of Constella Intelligence, which monitors online disinformation.
When it comes to regulation, Europe is well ahead of the United States. The European Union’s Digital Services Act, passed in April 2022, and the UK’s cybersecurity bill require platforms to conduct risk assessments and explain to regulators how they plan to mitigate the impact of harmful content. The EU’s DSA focuses on risks to society, while the UK bill focuses on risks to individuals. Germany’s NetzDG, introduced in 2017 and revised in 2021, imposes fines on tech companies that show a pattern of distributing false content.
French regulators say the EU's Digital Services Act is similar to banking regulation because instead of overseeing every transaction, it requires companies to build systems to mitigate risk.
Hopefully, the new regulations will help companies find the right balance between curbing harmful content and protecting freedom of expression, and the laws can also spur innovation. But firms developing AI/deep learning should keep in mind that authoritarian regimes are also likely to use their technologies to prevent the flow of legitimate information rather than improve content security. So the search for solutions continues.
(Article published in Thomson Reuters Foundation News)