What is Interpersonal Harm?
Interpersonal harm is a specific type of social harm caused by AI systems that affects individual relationships, groups, or communities through acts like defamation, harassment, erosion of trust, manipulation, or discrimination.
This category of harm is distinct from Allocative Harm (which affects resource distribution) as it focuses on psychological, social, or relational damage. Interpersonal harm can manifest when AI generates offensive, biased, or harassing content; when Deep fakes are created to ruin a person’s reputation; or when an AI-driven chatbot is deployed without sufficient empathy or cultural sensitivity, leading to offense and a breakdown in communication. Because community organizations rely entirely on relationships and trust, managing this specific risk is paramount to maintaining a positive public image and organizational integrity.
Think of it this way: Interpersonal harm is the damage done when the AI makes things personal, in a bad way. It’s the digital equivalent of someone yelling an offensive joke at a town hall meeting. It breaks relationships, destroys trust, and makes people feel excluded. For example, if an AI is asked to generate marketing copy but uses a stereotypical image or offensive language, the resulting public backlash creates interpersonal harm by alienating members and damaging the organization’s reputation for inclusivity, eh.
Why Interpersonal Harm Matters for Your Organization
For a community organization leader, interpersonal harm is a direct threat to your membership and mission effectiveness.
Your organization’s success is predicated on building and maintaining strong, inclusive, and trusting relationships with every member of your community. AI is now a tool for mass communication and, if unchecked, can scale up small biases into massive public relations disasters. A single piece of AI-generated content that is deemed insensitive or discriminatory can lead to loss of membership, public shaming, and an atmosphere of distrust that is almost impossible to repair. Your AI Policy must have a specific clause detailing the ethical review process to prevent this relational damage.
Example
A Destination Marketing Organization (DMO) uses an AI tool to generate personalized email subject lines for an upcoming cultural festival.
Weak Approach (Ignoring Interpersonal Harm): The team uses the AI with a prompt that emphasizes high click-through rates. The AI, having been trained on a wide range of internet data, generates subject lines that are culturally insensitive or uses language that excludes non-local populations. The DMO sends the email, causing immediate offense and a public apology is required.
Strong Approach (Mitigating Interpersonal Harm): The DMO uses the Human−in−the−loop (HITL) Approach. The AI generates 20 subject lines, but the human communications manager, who understands the local cultural context, reviews the options. They filter out any high-clickbait but potentially offensive language, ensuring the final message is welcoming, inclusive, and true to the DMO’s community-focused values.
Key Takeaways
- Relational Damage: Harm targets relationships, trust, and community cohesion.
- Social Impact: Manifests as harassment, discrimination, or reputation damage (e.g., via Deep fakes).
- Mitigation is Vetting: Requires rigorous human review of all AI output before public use.
- Trust is the Victim: This risk directly undermines the core function of a community organization.
Go Deeper
- The Counterpart: Contrast this with the economic and resource-based damage defined in Allocative Harm.
- The Solution: See the mandatory safety protocol for preventing this in our guide on the Human−in−the−loop (HITL) Approach.
- The Root Cause: Learn how flawed input leads to poor relational output in our definition of Data Bias.