ML model to detect malicious use of TakeTwo crowdsourced labeling

### Background on the problem the feature will solve/improved user experience
As an open-source, data crowdsourced solution, there is potential for malicious use/contributions

### Describe the solution you'd like
Develop an ML model(s) that detects malicious use such as:
- seeking to spam
- alter what is considered by racist, by making offensive racist terms seem less racist or identifying non-racist, benign terms as racist with the intent of making the api useless (by classifying everything as racist)
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### Tasks
Description of the development tasks needed to complete this issue, including tests,

### Acceptance Criteria
Standards we believe this issue must reach to be considered complete and ready for a pull request. E.g precisely all the user should be able to do with this update, performance requirements, security requirements, etc as appropriate.

Non sono indicati file, punti di ingresso, test o criteri di accettazione. Inizia esaminando i Jupyter notebooks e il workflow di data science esistente del repository, quindi definisci i segnali di uso malevolo, i dati di valutazione e criteri di completamento misurabili prima dell'implementazione.

Scritto dal modello di indicizzazione a partire dal testo della issue.