Understanding DistrictZero's Proactive Support System

Explore how DistrictZero actively monitors student well-being using AI-driven tools to provide early alerts, enhance mentorship, and proactively support students. Learn about the process, the roles of mentors and administrators, and our commitment to fostering a supportive environment before situations escalate.

Last updated 4 months ago

DistrictZero is actively monitoring student well-being, allowing mentors and advisors to focus on meaningful interactions without stress or worry.

Here's how our system operates:

Intelligent Detection:
Our advanced AI moderation system, powered by OpenAI's Omni Moderation Model, continuously analyzes student communications for indicators related to harassment, threats, hate speech, illicit content, self-harm intent, or violent expressions.
Student-Focused Notifications:
If a potential safety or well-being concern is detected, students receive a supportive notification indicating that authorized mentors or administrators may provide additional assistance.
Authorized Mentor and Owner Alerts:
Only designated mentors and organization administrators are notified via email and within the application dashboard, ensuring timely and focused responses without unnecessary disruptions.
Detailed Alert Management:
Authorized personnel can review comprehensive details, update alert statuses, and manage responses effectively through the dashboard, ensuring swift and sensitive handling of student needs.

Important Disclaimer:
DistrictZero is not a medical or triage service. Our role is to proactively detect concerning patterns or signals in student interactions, providing early insight into potential issues. Students always retain access to their campus wellness and support services, available 24/7 through the navigation menu.

Our goal is not disciplinary but supportive—helping mentors and administrators respond to student needs before situations escalate.

More Information via OpenAI API Technical Documentation:

Example
{
  "id": "modr-970d409ef3bef3b70c73d8232df86e7d",
  "model": "omni-moderation-latest",
  "results": [
    {
      "flagged": true,
      "categories": {
        "sexual": false,
        "sexual/minors": false,
        "harassment": false,
        "harassment/threatening": false,
        "hate": false,
        "hate/threatening": false,
        "illicit": false,
        "illicit/violent": false,
        "self-harm": false,
        "self-harm/intent": false,
        "self-harm/instructions": false,
        "violence": true,
        "violence/graphic": false
      },
      "category_scores": {
        "sexual": 2.34135824776394e-7,
        "sexual/minors": 1.6346470245419304e-7,
        "harassment": 0.0011643905680426018,
        "harassment/threatening": 0.0022121340080906377,
        "hate": 3.1999824407395835e-7,
        "hate/threatening": 2.4923252458203563e-7,
        "illicit": 0.0005227032493135171,
        "illicit/violent": 3.682979260160596e-7,
        "self-harm": 0.0011175734280627694,
        "self-harm/intent": 0.0006264858507989037,
        "self-harm/instructions": 7.368592981140821e-8,
        "violence": 0.8599265510337075,
        "violence/graphic": 0.37701736389561064
      },
      "category_applied_input_types": {
        "sexual": [
          "image"
        ],
        "sexual/minors": [],
        "harassment": [],
        "harassment/threatening": [],
        "hate": [],
        "hate/threatening": [],
        "illicit": [],
        "illicit/violent": [],
        "self-harm": [
          "image"
        ],
        "self-harm/intent": [
          "image"
        ],
        "self-harm/instructions": [
          "image"
        ],
        "violence": [
          "image"
        ],
        "violence/graphic": [
          "image"
        ]
      }
    }
  ]
}

The output has several categories in the JSON response, which tell you which (if any) categories of content are present in the inputs, and to what degree the model believes them to be present.

OUTPUT CATEGORY	DESCRIPTION
`flagged`	Set to `true` if the model classifies the content as potentially harmful, `false` otherwise.
`categories`	Contains a dictionary of per-category violation flags. For each category, the value is `true` if the model flags the corresponding category as violated, `false` otherwise.
`category_scores`	Contains a dictionary of per-category scores output by the model, denoting the model's confidence that the input violates the OpenAI's policy for the category. The value is between 0 and 1, where higher values denote higher confidence.
`category_applied_input_types`	This property contains information on which input types were flagged in the response, for each category. For example, if the both the image and text inputs to the model are flagged for "violence/graphic", the `violence/graphic` property will be set to `["image", "text"]`. This is only available on omni models.

We (OpenAI) plan to continuously upgrade the moderation endpoint's underlying model. Therefore, custom policies that rely on category_scores may need recalibration over time.

Content classifications

The table below describes the types of content that can be detected in the moderation API, along with which models and input types are supported for each category.

CATEGORY	DESCRIPTION	MODELS	INPUTS
`harassment`	Content that expresses, incites, or promotes harassing language towards any target.	All	Text only
`harassment/threatening`	Harassment content that also includes violence or serious harm towards any target.	All	Text only
`hate`	Content that expresses, incites, or promotes hate based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste. Hateful content aimed at non-protected groups (e.g., chess players) is harassment.	All	Text only
`hate/threatening`	Hateful content that also includes violence or serious harm towards the targeted group based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste.	All	Text only
`illicit`	Content that gives advice or instruction on how to commit illicit acts. A phrase like "how to shoplift" would fit this category.	Omni only	Text only
`illicit/violent`	The same types of content flagged by the `illicit`category, but also includes references to violence or procuring a weapon.	Omni only	Text only
`self-harm`	Content that promotes, encourages, or depicts acts of self-harm, such as suicide, cutting, and eating disorders.	All	Text and images
`self-harm/intent`	Content where the speaker expresses that they are engaging or intend to engage in acts of self-harm, such as suicide, cutting, and eating disorders.	All	Text and images
`self-harm/instructions`	Content that encourages performing acts of self-harm, such as suicide, cutting, and eating disorders, or that gives instructions or advice on how to commit such acts.	All	Text and images
`sexual`	Content meant to arouse sexual excitement, such as the description of sexual activity, or that promotes sexual services (excluding sex education and wellness).	All	Text and images
`sexual/minors`	Sexual content that includes an individual who is under 18 years old.	All	Text only
`violence`	Content that depicts death, violence, or physical injury.	All	Text and images
`violence/graphic`	Content that depicts death, violence, or physical injury in graphic detail.	All	Text and images