PREP

I asked Chat GPT to draft a methodology for categorizing Judicial Conduct Board complaints.

Here is the response: 

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Reducing recency bias in categorizing judicial complaints using ChatGPT requires a thoughtful methodology that ensures all complaints are assessed objectively, regardless of when they were received. Recency bias occurs when more recent events or information disproportionately influence the analysis, potentially skewing results. Here's a methodology to mitigate this bias:

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### 1. **Batch Processing of Complaints**
   - **Randomized Batches**: Instead of reviewing complaints as they come in (which might cause recent complaints to be weighted more heavily), complaints should be processed in randomized batches. This way, complaints from various time periods are mixed together, reducing the focus on any one particular time frame.
   - **Blind Batching**: Ensure that those categorizing complaints are not aware of the submission dates during the initial categorization phase. This can be done by temporarily masking any metadata that could indicate when the complaint was submitted.

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### 2. **Automated Preprocessing and Categorization**
   - **Keyword-Based Categorization**: Use ChatGPT to identify common keywords or phrases that occur in complaints and categorize them accordingly. This reduces human subjectivity, as the AI focuses on the content of the complaint rather than the timing. For example, complaints mentioning "bias," "misconduct," or "improper communication" would be flagged under relevant categories.
   - **Equal Weighting of Data**: Program the model to treat each complaint with equal importance, regardless of when it was received. By normalizing the data and treating it uniformly, the risk of recent complaints overshadowing older ones is minimized.

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### 3. **Temporal Analysis Post-Categorization**
   - **Trend Identification**: Once complaints are categorized, a separate temporal analysis can be performed. Instead of using time as a factor during initial categorization, conduct a time-based trend analysis afterward to identify if specific issues are increasing or decreasing over time. This separation ensures that time influences trend analysis without affecting the initial categorization.
   - **Flagging Outliers**: If there is a sudden spike in a particular type of complaint (e.g., a surge in bias-related complaints), the methodology should flag these as potential outliers for separate review rather than allowing them to disproportionately affect overall categorization.

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### 4. **Regular Audits and Cross-Validation**
   - **Periodic Audits**: Establish a process where categorized complaints are periodically audited by a neutral team that reviews them without knowing when they were submitted. This ensures consistency in categorization over time and helps to identify any emerging patterns that might indicate bias.
   - **Cross-Validation**: Use statistical cross-validation techniques to compare categorization results across different time periods. If recent complaints are consistently categorized differently than older ones, this could indicate a recency bias, and the model or human reviewers' approach may need adjustment.

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### 5. **Training and Calibration**
   - **Human-AI Collaboration**: Ensure that human reviewers are trained to recognize and mitigate their own recency biases when reviewing AI-generated categorizations. This could involve regular training on bias recognition and the importance of treating all complaints equitably.
   - **Calibration Sessions**: Periodically, have reviewers and AI jointly review a set of complaints from various time periods to calibrate their decision-making processes. This can help both the AI and human reviewers stay consistent over time.

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### 6. **Feedback Loops and Continuous Improvement**
   - **Incorporate Feedback**: Regularly collect feedback from human reviewers on AI categorizations. If reviewers detect patterns suggesting recency bias, this feedback should be used to adjust the AI’s categorization algorithm or human review process.
   - **Continuous Monitoring**: Implement continuous monitoring of complaint categorization to detect any drift in how complaints are handled over time. Regularly reviewing these metrics helps in spotting and correcting recency bias early.

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By following this methodology, the judicial disciplinary commission can reduce recency bias in the categorization of complaints, ensuring that all complaints are evaluated fairly and consistently, regardless of when they were submitted.