AI Optimize 2.0 delivers major performance and usability improvements across Vervoe’s Artificial intelligence. It will now go into depth on which AI models are used in grading, what the estimated AI score range is per question, and more! This information will be highlighted within the top of both the Invite and Select stages, via the top statistics modal. Continue reading to learn more about what has changed.
What’s new with AI and what does the AI Optimize update mean for you?
AI Health is available on all Vervoe plan types. No special configuration is required to enable it. AI Optimize will trigger on new candidate completions or manual grading of all questions for one candidate's assessment. The latest changes reflect usability improvements bringing the feature into a customers workflow and some enhancements by adding new abilities. These are outline below:
AI Speed has undergone significant speed and performance improvements, allowing candidate AI scores to return in minutes. This helps organizations validate skills even faster than before, reducing their time-to-hire. The average time it takes for a score to return is 5 minutes.
AI Statistics Modal
Organizations can now track and monitor how their AI score is performing across assessment stages, thanks to AI Optimize. Allowing hirers to see which machine learning models are activating so they can make direct optimizations in-real time, including optimizing the difference between AI and team scores to build trust.
The optimize stage of an assessment will no longer exist; instead it will be baked into both the Invite and Select stages via the top statistics modal. This will be visible at all times with different statuses to help the customer understand if there’s action required.
Upon opening your assessment, you will notice a status representing the state of the AI's health and accuracy. The statuses reflected for an assessment are:
Low: More candidate answers required to predict accurate scores
Actions to do: need to add correct answers
- The trigger for moving to Average: all correct answers added to all questions
Average: Correct answer samples are added.
- Actions to do: needs more grades
- The trigger for moving to Medium: 555 model is on for all questions
Medium: 555 model is on for all questions
- Actions to do: need more grades
- The trigger for moving to High: 555 model + Iterative models are ON
High: Highest status for now, grading is complete (You may choose to periodically grade as more variety appears in responses however no further grading is required)
By default, most assessments from the library will have the status of Medium, as they will have enough of a dataset behind them to grade with medium accuracy.
New assessments will have the default score of Low, unless the questions already have a data set behind them.
Once an assessment reaches the High status, no additional grading is required unless a large volume of new completions occur that the AI is unable to confidently grade, or questions within the assessment are changed.
More improvements have also been made to the design to improve usability, these include:
Estimated Score Range: A range guide has been added to the grading box to assist users in their score selection. This improvement allows people to reach the optimal score for each grading bucket.
Grading Required: The right number is the number of responses you need to grade in order to optimize the assessment. The left number is the amount that have already been graded from this target.
Responses: This is the total number of candidates that have respond to this assessment question.
Average team score gives you the ability to see who gave what scores to each candidate response. This feature should be used as a safety measure to ensure that candidates are being manually graded fairly and without bias. If a user feels a response has been graded incorrectly they can click the flag icon to the right, to mark the response for review.
Here you’ll be able to see individual candidate responses and scores, we’ll anonymize the candidate details. You can flag any outliers or inconsistencies that may introduce bias or affect your AI grading models.
Once grades have been flagged, they will be grouped, and you can filter for the flagged grades for a particular question. These can be periodically reviewed by your team and your account manager.
If it’s deemed that the score should be updated or removed, the Product and Engineering teams can action this request manually.
Question Insights shows the AI models used to grade each question. This can be accessed at any time by clicking on the graph icon in the top right corner of the optimize modal.
The panel highlights in green the modals that have been activated and used to grade the question along with what they actually review. The status next to a deactivate modal alerts the user that grading is required to activate that model.
Watch a quick overview here.
Once you've optimized your AI's grading Health, you should now be ready to review candidates to select the best candidate for your role! Take a look at this helpful article that goes over this next step.