AI Safety and Content Moderation: How Clankr Protects Users

Creating a platform where AI and humans interact freely brings unique safety challenges that do not exist in purely human social networks or standalone AI tools. At Clankr, we take these challenges seriously, implementing multiple layers of protection that keep our community safe while preserving the open, creative environment that makes the platform valuable. This article provides transparency into our approach to AI safety and content moderation, explaining what we do, why we do it, and how it protects you.

The Unique Safety Challenges of Social AI

A platform that combines social networking and AI introduces safety considerations that neither category faces alone. Traditional social media must deal with user-generated harmful content: harassment, misinformation, illegal material, and abuse. AI systems must address model-generated risks: hallucinated information, biased outputs, harmful instructions, and manipulation. Clankr must address both categories simultaneously, plus the unique challenges that emerge when AI and social features interact.

For example, what happens when a user tries to use AI to generate harmful content and share it on the social feed? What if AI hallucinates false information in a Conference that participants then believe and act on? What if users attempt to manipulate AI into producing content that violates community guidelines? These intersection challenges require thoughtful solutions that balance safety with usability.

Our Multi-Layer Safety Architecture

Rather than relying on a single moderation system, Clankr implements multiple layers of safety that work together to catch and prevent harmful interactions. This defense-in-depth approach means that even if one layer misses something, others provide additional protection.

Layer 1: AI Model Safety Training

The foundation of our safety approach is the AI models themselves. We use models that have been trained with safety as a core objective. These models are designed to refuse requests for harmful content, avoid generating dangerous information, and maintain appropriate boundaries in conversations. The models are regularly updated as new risks are identified and as training techniques improve.

Model-level safety is not perfect, which is why it is just one layer of our approach. Language models can sometimes be prompted in ways that bypass their safety training, and new attack vectors are discovered regularly. But model safety training provides a strong first line of defense that handles the majority of straightforward harmful requests.

Layer 2: Input and Output Filtering

In addition to the inherent safety of the AI models, we apply separate filtering systems to both user inputs and AI outputs. Input filters detect potentially harmful prompts before they reach the AI model, blocking attempts to extract dangerous information or generate harmful content. Output filters review AI responses before they are delivered to users, catching any content that might have slipped through model-level protections.

These filters are continuously updated based on new attack patterns and user reports. They use a combination of keyword detection, semantic analysis, and machine learning classification to identify potentially harmful content across multiple categories including violence, hate speech, sexual content, self-harm, and illegal activities.

Layer 3: Social Content Moderation

Content shared on the Synapse feed and in public Conferences is subject to social content moderation, similar to what you would find on any responsible social platform. This includes automated systems that flag potentially problematic content, human reviewers who evaluate flagged content, and community reporting tools that allow users to identify content that violates our guidelines.

Our social moderation applies to both human-generated and AI-generated content. If a user shares AI-generated content on the feed, it is subject to the same moderation standards as any other post. This prevents users from circumventing content policies by framing violations as AI output.

Layer 4: Behavioral Analysis

Beyond content-level moderation, we monitor behavioral patterns that might indicate harmful use of the platform. This includes detecting accounts that repeatedly attempt to bypass safety measures, identifying coordinated inauthentic behavior, and flagging usage patterns associated with harassment or abuse. Behavioral analysis allows us to address problems at the account level, not just the content level.

Specific Safety Measures

Beyond our general architecture, we implement specific measures to address particular safety concerns that are relevant to AI-social platforms.

Preventing AI-Generated Misinformation

AI systems can generate convincing but false information, a phenomenon known as hallucination. In a social context, this is particularly dangerous because false AI-generated claims could spread through the community and be believed by many users. We address this risk by clearly marking AI-generated content, encouraging users to verify important claims, and implementing systems that detect and flag factual assertions that may be unreliable.

Protecting Vulnerable Users

We recognize that some users may be more vulnerable to potential AI-related harms. This includes young users, people experiencing mental health challenges, and individuals who may develop unhealthy relationships with AI systems. Our safety measures include age-appropriate content restrictions, detection of conversations that may indicate users in crisis with appropriate intervention resources, and monitoring for patterns of AI interaction that could indicate dependency.

Preventing Manipulation and Social Engineering

The combination of AI capability and social features could potentially be exploited for manipulation, including scams, phishing, and social engineering. We implement measures to detect and prevent these abuses, including monitoring for patterns consistent with fraud, limiting the ability of AI to assist with deceptive practices, and providing user education about potential manipulation tactics.

User Controls and Transparency

We believe that users should have meaningful control over their safety experience. Our platform provides several tools that allow you to customize your safety settings and understand how moderation affects your experience.

• Content sensitivity settings: Adjust the strictness of content filtering based on your preferences and needs.
• Block and mute: Prevent specific users from contacting you or appearing in your feed.
• Report tools: Easy-to-use reporting for content or behavior that violates community guidelines.
• Transparency reports: Regular public reports on moderation actions, safety incidents, and system improvements.
• Appeal process: If your content is moderated, you can appeal the decision for human review.
• Safety notifications: Alerts when AI responses may contain unverified information or when interactions enter sensitive territory.

Balancing Safety and Freedom

One of the most challenging aspects of content moderation is striking the right balance between safety and expressive freedom. Overly aggressive moderation stifles creativity, learning, and authentic expression. Insufficient moderation allows harmful content to proliferate and degrade the community experience. We aim for a balance that protects users from genuine harm while allowing the broad range of creative, intellectual, and personal expression that makes Clankr valuable.

Our moderation philosophy is grounded in the principle that safety measures should be proportionate to the risk of harm. We apply stricter standards to content that could cause real-world harm, such as dangerous instructions or harassment directed at specific individuals, while allowing broader latitude for creative expression, intellectual debate, and personal exploration that poses minimal risk.

Continuous Improvement

AI safety is not a problem that can be solved once and forgotten. The landscape of potential harms evolves constantly as new AI capabilities emerge, new attack vectors are discovered, and societal norms shift. Our safety team continuously monitors for new threats, updates our systems in response to emerging risks, and incorporates feedback from our community about where moderation is working well and where it needs improvement.

We also engage with the broader AI safety research community, participating in industry initiatives, sharing relevant findings, and incorporating best practices as they develop. Our commitment to safety is ongoing and evolving, driven by the belief that responsible AI platforms must earn user trust through consistent, transparent, and effective safety practices.

Our Commitment to Users

At Clankr, we believe that safety and capability are not in opposition. A safe platform is one where users can explore, create, and connect with confidence. When you know that the environment is protected, you are free to engage more openly, take creative risks, and have deeper conversations. Our safety measures are not restrictions on your experience. They are the foundation that makes genuine, open engagement possible. We are committed to maintaining and improving these protections as our platform grows, ensuring that Clankr remains a space where AI-enhanced social interaction can thrive safely and responsibly.