LIVE! Candidate Feedback:

Thank you for this positive information. Keep up your good work!

Wow!. I was so amazed by it. I have no words to explain. You have just described me like you know me. Wow!

It was incredible to receive feedback this early in the recruitment process. Seeing my strengths and weaknesses and given recommendations on development for the future, based on my pre-interview answers is something I have never seen before, and was super super cool. As someone who works in the recruitment space currently, this would be an amazing tool to use with graduates and students

Helped me understand myself in a great depth from another person's perspective. Learning is always something I'm key about but never have I focused on learning about myself. Thank you for this :)

I had found this useful because it lest u know how we are as a person and if we are sutured for the job or not

Thank you for the candid tips. I will put these into a positive for my future endeavors Regards Marika

Good explanation given

I found the analysis to be interesting and quite revealing. I feel that nearly all of it to be an accurate assessment, but not quite convinced on a couple of points. I'll need to ponder on those points and may come to the same conclusion as your system has determined.

Helped me identity the areas I could focus on and develop in order to be s better team worker.

It helped me to get to know myself even better and also helped in ways of how I could handle some situation

I am able to know my weak points and strong point. This can help to get a better understanding of my actions and also what I can do to improve the communication between me and other people

I did not find this useful. I'm not convinced that your system is valuable, 100% of the time. Some insights didn't reflect my true self. I have shown your insights to former colleagues and they agreed it didn't sound like me. I'm hopeful this doesn't jeopardise my future opportunities. It would be interesting for me to see how your insights came about. Perhaps an explanation is something that you could provide along with the insights when you send it through?

This was really accurate and A lovely confidence booster

Thank you for your coaching tips! There is always room for improvement!much appreciated

Insights were great

I do like to think that my results reflect me but I most certainly liked the coaching tips! Very handy

Good to see areas of improvement and areas identified in need of improvement.

I am very impressed with accuracy of your ‘insights’ I have found this whole process very innovative and valuable

This fed back will help me for future employment if I don't get accepted as part of your team but it would be nice to be given a chance to come and work for you

Nice to know what other views are.

I enjoyed the feedback, was quite interesting 🧐

Yes I found it useful as it is quite true It give me something to think about

I found it useful because it shows me my strengths, ans shows me what to work on.

the feedback will help me to boost my confidence and adapt change in personal and professional life.

Thank you, this was very useful and was a true reflection of my personality. Thank you for the feedback.

This is great. The majority of that is definitely accurate. I enjoyed reading it.

This is a very true picture of the person I am sincerely.

It is intresting to know more about my strengths and passion

This was useful and constructive feedback

I found it very interesting and I think it is a great tool to get an idea on someone, however I believe an interview should be done face to face so you can understand the person and there emotions in person

It has made me gain more confidence in myself and be more positive 🙂

It’s always good to hear feedback

Thank you for the feed back just another thing to look back on and to work to further improve myself. Much appreciated!

It made me feel like my application is being taken seriously and taken under consideration and gave good advice

Allowed me to find out what I need to focus on improving.

It’s good to know more about me 😁😁 I am very happy to hear that I will improve more myself.

This made me confident as a person , and made me self aware of my strengths , thank you

It shows that I have many strengths and ways of approaching a job which I can be aware of when entering the workforce. I am glad that it can be pointed out and benefits me as a person looking for an opportunity to work.

I found the information useful,because it helped me see were i can improve myself.

very personal application and it think it’s important information to know when applying for jobs/ working

Any sort of opinion or feedback about me is of interest to me, especially from a company that Im quite interested in. I`ve found value in your say & look forward to hearing more from you soon.

Thankyou for this information. It is important to understand your personality and realise your strengths and eeaknesses

  • Resources
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  • How Victoria defeated COVID with individual action and data

Hiring with AI, fairer, faster and better

How Victoria defeated COVID with individual action and data

BY Team PredictiveHire


Action and data

COVID has taught us that on reflection the focus on individual action with a community benefit as a goal is really a focus that leads to the greater good.

In our home state of Victoria, Australia now 7 straight days with ZERO new cases. It has been an effort founded on facts and science over misinformation.

In Victoria, many sacrificed a lot for their well-being for ALL. If anything, there is now proof, thanks to Victorians, that when we see facts, listen to science and let data show you how to lead that change, you can make it happen.

We’re using this approach to build a new vision for inclusive hiring.

AI, especially predictive machine learning models, are an outcome of a scientific process, it’s no different to any other scientific theory, where a hypothesis is being tested using data.

The beauty of the scientific method is that every scientific theory needs to be falsifiable, a condition first brought to light by the philosopher of science Karl Popper.

In other words, a theory has to have the capacity to be contradicted with evidence.

It is how science is able to progress without bias.

There are three decisions that are made by a human in building that scientific experiment.

  1. Forming a meaningful hypothesis
  2. Data collection methodology (experiment set up)
  3. The data you rely on to test the hypothesis

One can argue 2 and 3 are the same as if the methodology is not sound the data collection wouldn’t be either. That’s why there is so much challenge and curiosity as there should be about the data that goes into an algorithm.

Think of an analogy in a different field of science: the science of climate change.

A scientist comes up with a hypothesis that certain factors drive an increase in objective measures of climate warming, eg CO2 emissions, cars on the road, etc.

That’s a hypothesis and then she tests it using statistical analysis to prove or disprove that her hypothesis holds beyond random chance.

The best way to make sure you are following a sound scientific approach is to share your findings with the broader scientific community. In other words, publish in peer-reviewed mediums such as journals or conferences so that you are open to scrutiny and arguments against your findings.

Or to put it another way,  be open for your hypothesis to be falsified.

In AI especially, it is also important to keep testing whether your hypothesis holds over time as new data may show patterns that lead to disproving your initial hypothesis.

This can be due to limitations in your initial dataset or assumptions made that are no longer valid. For example, assuming the only information in a resume related to gender are name and explicit mention of gender or a certain predictive pattern such as detecting facial expressions are consistent across race or gender groups. Both of these have been proven wrong*.

The only way to improve our ability to predict, be it climate change or employee performance, is to start applying the scientific method and be open to adjusting your models to better explain new evidence.

Therefore the idea that a human can encode their own biases in the AI — well it’s just not true if the right science is followed.


* Amazon scraps secret AI recruiting tool that showed bias against women (

* Researchers find evidence of bias in facial expression data sets (

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