ARTICLE
17 January 2025

Content And Character: AI As An Asset And Adversary In UGC Moderation

G
Gamma Law

Contributor

Gamma Law is a specialty law firm providing premium support to select clients in cutting-edge media/tech industry sectors. We have deep expertise in video games and esports, VR/AR/XR, digital media and entertainment, cryptocurrencies and blockchain. Our clients range from founders of emerging businesses to multinational enterprises.
As we stand on the cusp of a new technological era, rapid developments in AI are not just reshaping business, recreation, communication, and social interaction; they fundamentally redefine digital transformation.
United States Technology

As we stand on the cusp of a new technological era, rapid developments in AI are not just reshaping business, recreation, communication, and social interaction; they fundamentally redefine digital transformation. As the media constantly reminds us, AI's advanced capabilities and expanding applications herald both a blessing and a potential curse, introducing a new set of complexities and threats we must address head-on.

Businesses, governments, and other organizations that use and encourage user-generated content are particularly vulnerable. In an era of viral marketing and social influencers, UGC has become an integral part of many business strategies. From social media platforms to online marketplaces to "traditional" commercial endeavors, UGC can significantly enhance customer engagement, brand loyalty, and revenue. However, it also provides new tools that devious characters can use to spread malicious content.

Malicious actors constantly evolve and refine their tactics and leverage advanced technologies to manipulate UGC and exploit online platforms. AI has allowed them to extend their reach and amplify their impact.

Stealthy, Mass-Produced Disinformation

AI empowers rapid-fire, mass-produced, hard-to-detect misinformation, inflammatory content, and other attacks on our platforms.

In the past, a coordinated disinformation campaign might have required a team of people working around the clock to create and spread false narratives — writers, artists, printers, and others. Nowadays, one person with even rudimentary communication and technical skills can access AI tools that match or exceed that output.

The digital age enables even more widespread and coordinated disinformation. The 2016 US presidential election saw Russian operatives use social media to disseminate lies and sow discord among American voters. More recently, users shared false news stories, deep-fake videos, misleading memes, and pseudoscience, influencing public opinion about the COVID-19 virus and vaccines and potentially undermining public health.

The sheer scale and speed at which malicious content can now be generated and disseminated presents one of AI's most immediate and visible impacts on the threat radar. It is not hard to imagine a disgruntled employee, trailing political candidate, or bigoted loudmouth generating thousands of unique pieces of harmful content — each directed at impressionable targets — in just a few hours.

AI Moderation vs. AI Malfeasance

While villains use AI as a spear to penetrate social media, news sites, and digital platforms, heroes can employ similar technology as a shield to deflect these underhanded efforts. AI also provides powerful solutions to detect and counteract the spread of misinformation and maintain trust in information ecosystems:

  • Content Verification:AI's natural language processing (NLP) algorithms can analyze the semantic structure of text to identify patterns consistent with fake news. Fact-checking AI tools cross-reference claims with reliable databases and flag discrepancies in real-time. Moreover, AI can quickly scan large volumes of data, enabling rapid identification of suspicious content that might otherwise slip through human scrutiny.
  • Deepfake Detection:AI-informed detection tools can analyze the subtle imperfections ingrained in AI-generated and manipulated videos that are imperceptible to the human eye. These tools examine facial movements, eye-blink patterns, and audio inconsistencies to differentiate between genuine and deepfakes created to embarrass and slander people or slant the way we perceive events.
  • Network Analysis:AI excels at analyzing complex information flow. By mapping how false information spreads through social networks, AI can identify its origins and compromised nodes. Machine learning detects abnormal patterns in sharing and interactions, flagging potential sources of misinformation for platform early intervention.
  • Behavioral Analysis:Users who propagate false information often exhibit common characteristics and tactics. AI can analyze users' posting habits, interaction routines, and network connections to find accounts that exhibit suspicious activity. This helps platforms uncover bot accounts and foil coordinated disinformation campaigns for monitoring or removal.
  • Speed and Accuracy:AI-driven content moderation tools can find and filter out harmful content faster and more reliably than their human counterparts. As machine learning becomes more sophisticated, AI can consistently categorize and assess content based on predefined guidelines. Automating the moderation process lets platforms manage a massive influx of content more efficiently.

Moderating the Moderators

This AI automation makes UGC content moderation faster and more efficient, but it also introduces technical, legal, and ethical concerns:

  • Free Speech and Expression:Platforms should seek legal advice to balance content safety with users' rights to express themselves freely. AI systems can be overly restrictive, flagging legitimate speech simply because it contains sensitive keywords or matches certain patterns. For example, AI might incorrectly flag posts about Nazism awareness or gynecological health as inappropriate.
  • Data Privacy:AI moderation systems train on vast data sets and ongoing content analysis. Platforms must develop and enforce policies governing how they collect, store, and use personally identifiable data to train their systems. This includes posting clear notifications about whether and how they vet private messages, how long they retain data, and what safeguards they use to prevent misuse of sensitive information.
  • Bias:AI systems can perpetuate or amplify existing societal biases that contaminate their training data. Systems trained primarily on English-language content may fail to comprehend cultural norms, foreign language nuances, or regionally specific expressions.
  • Accountability:Users have the right to know when and how AI systems evaluate and moderate their content. Platforms should communicate the reasons behind their decisions to flag or remove content. Transparency builds trust and allows users to adapt behaviors that conform to the rules. Platforms should also be accountable for their systems' decisions and provide clear mechanisms for appeal.

Best Practices in AI Content Moderation

Web3 and digital content attorneys can assist organizations that invite and host UGC in establishing a clear architectural framework overseen by a team of human moderators with diverse backgrounds and cultural expertise. These moderators should receive comprehensive training in cultural sensitivity, trauma management, and emerging online threats. Organizations should vest this team with the authority to enforce, interpret, and override content policies. Complex cases that AI deems questionable and user appeals of moderation steps should route to these human moderators.

Lawyers can draft end user license agreements and terms of service that protect websites and their users by transparently describing comprehensive content policies:

  • Which content categories are prohibited
  • How AI and humans determine if content violates policy
  • Consequences for first, repeated, and egregious violations
  • How to report offensive or misleading content
  • Appeals processes and timelines

Quality control measures to detect and correct false positives and negatives and uncover consistent AI mistakes ensure continuous improvement. Audits of AI system performance across all content types and users will show whether inherent bias squelches viewpoints, allows unfair depictions of demographic groups, or overly aggressive scrutiny of the content they post.

Conclusion

Comprehensive, multilayered practices enable organizations to create more effective, fair, and transparent content moderation systems that protect users while supporting vibrant online communities. Success requires ongoing commitment to improvement and active engagement with users and stakeholders.

By staying ahead of technological advancements, adapting legal frameworks, and prioritizing ethical considerations, emerging technology companies can transparently and accountably promote human values while mitigating legal risks.

As the online landscape evolves and bad actors develop new tactics to evade moderation, organizations must stay vigilant and adaptable. Ongoing monitoring, policy refinements, and collaboration with industry peers and legal experts will be key.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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