Could AI be used to detect when a human is picking survey response options randomly? Context: I am a clinical psych researcher dabbling in machine learning.
Humans cannot be truly random. Therefore, could machine learning be used to analyze a string of numbers and determine the probability that said string was generated by a human or by a computer? Taking it a step further, could machine learning be used to look at survey responses and determine the likelihood as to whether the participant was answering questions honestly, or whether they &quot;Christmas-tree'd&quot; either part of or all of the survey? If so, would this be dependent on the specific questions that each survey item asked, or simply on the type of survey question (e.g., Likert-type, sliding scale, ruler, yes/no or true/false questions, free response, etc.)?
AI is probably not the best tool for this job.  Several classical techniques in survey design include:

Consistency check: ask the same question in several ways, spread out across the survey, and check if they've answered consistently.

Open-ended questions: ask an open-ended question, see if they write nonsense or the bare minimum.

Attention check questions: e.g., &quot;What is your favorite color?  Regardless of your favorite color, please pick the second option.&quot;  Beware that these are controversial and have pros and cons.

Recruitment: recruit subjects who are less likely to try to cheat you.  Pay them a reasonable wage for their time.


My experience is that it is easy to be overly worried about respondents answering dishonestly; if you follow basic good practices, the overwhelming majority of respondents will try to be helpful and won't try to cheat you.