Digital screening platform with precision threshold adjustment

A machine learning (ML)-based digital filtering system for providing quality data for a survey while maintaining a level of participant diversity, said system comprising computer executable code stored in non- transitory memory and run on a processor, said system further comprising:

• an interactive survey screener module that tests a data quality level;
• and
• an anti-fraud module that tests for fraudulent activity;
• wherein:
• the screener module is configured to generate a pre-survey screen web page that is transmitted to a computing device associated with a participant;
• the system is configured to receive a response input by the participant to the pre-survey screen web page;
• and
• when the response fails to achieve a predetermined threshold response, the system is configured to reject and/or flag the participant from the survey;
• and
• wherein a difficulty level of the pre-survey screen web page and/or the predetermined threshold response is determined based on a target level of data quality;
• and
• wherein the pre-survey screen comprises an association screener web page that tests an association ability level of the participant, and the screener web page module is configured to:
• compile and/or access a corpus of data, said corpus comprising a set of words and/or sentences;
• create and/or access a word association model, said word association model being an ML engine that is trained to derive an association value for a given word;
• feed one or more words from the corpus to the word association model to derive an association value for each of the one or more words;
• and
• automatically generate one or more questions for the association screener web page module based at least in part on feeding the one or more words from the corpus to the word association model, wherein each of the one or more questions tests the participant on an association between a base word and a plurality of comparison words or between the plurality of comparison words.

A machine learning (ML)-based method for configuring a survey screen to provide a valid and diverse online survey, the method executed by computer code stored in non-transitory memory and run on a processor, the method comprising:

• determining a target level of participant attentiveness associated with accurate completion of the survey;
• automatically determining a participant screening threshold based on the target level of participant attentiveness;
• automatically generating a survey screen web page based on the participant screening threshold, wherein the survey screen web page is generated based at least in part on an output from an ML engine;
• transmitting the survey screen web page to a computing device associated with a participant;
• receiving a response of the participant to the survey screen web page on the computing device;
• and
• when the response fails to achieve a predetermined threshold response, rejecting the participant from the survey.