LIVE! Candidate Feedback:

Very helpful and agree 100%

Its usuful because its truth as much i dont mind the way i do things but some couching and critics are important and are welcomed.

This was useful as it made me feel confident and aware of my positive attributes and characteristics!

it gave me a better insight into my abilities

wow! this was extremely useful and accurate. it highlights my own strengths I haven't seen and defined my workstyle in a way I've struggled to articulate.

It's good to have feed back on you self

Repetition, routine and following a defined process, are traits of work in general, personally in my line of work these are the principles that make everyone happier and the job easier. Thanks for the tips.

yes very usefull i can take that feedback onboard for future interveiws and life learning lessons.

I found it incredibly helpful that you would give me such nice feedback. Thank you ever so much.

I read the comments and actually I agree with them.

It’s good to get reminded on what you need to improve ,

It’s nice to get information back from the person doing the job interviews makes you feel good about yourself

This information will help me in the future and currently to know know what I need to work on

Very useful information, thank you.

im being able to know myself better than ihad known

To show my strengths and possible actions to improve on!

Thank you for sending me your insights into my personality. I found it very interesting. I will endeavour to use this information in the future . 😀

I really appreciate the insights. Lots to feel good about and some things to reflect on. Thanks so much.

What I found useful, is how I can work with people in the future. Thank You.

I found this very interesting some I would agree with others bits I wouldn’t

its always good to get insight from other people to better improve yourself

It is absolutely straight to the point. All these sight described me exactly my personality. I give you 100%

Great feedback

The feedback felt very personal and super accurate, when I reflect on myself & my qualities.

I actually didn't find it useful. I found it quite incorrect in its assessment of me

This is a nice personal touch. I am very curious regading personality assements and find them interesting. I hadn't really considered that I don't assert my opinions publically, because I am a confident public speaker. however, it is true that I don't share opinions that may be unfavourable. I will reflect on this.

It's an amazing method to recognize people's characteristics. Thank you

I found this very interesting on so little words how we can come across and yes found it useful on so many levels.thank for the experience and look forward to hearing from you denise Mcleod

This was a surprise. I really liked it. I would like to know more about how the AI can do this! You have opened my eyes, haha.

This is the first time i have done one of these. And as have not had and interview in years. It's shown me a lot about myself and how others may see me and what I can work on. Thank you

Dear Sir/Madam, Regarding the results to my online interview,I did find them useful because they are a very accurate description of myself. I will definitely take the coaching tips on board. Thank You Marco Espinoza

This gives me some feedback on how people perceive me based on my career experience. Have not had to apply for a job for eons (!) so this is a daunting experience. It makes me feel a little more positive. Thanks.

This has helped me take time out to review myself as an employee, highlight my strengths which as a result has made me more confident as a person and will help me when dealing with both colleagues and customers in my future role.

This made me reflect and think deeply about the answers I gave and how much they reflect me as a person. These insights can also be used in interviews as well as on my resume to help describe be as a person and a worker.

I appreciate the information however there are statements made that are incorrect such as saying I am closed minded nothing could be further from the truth I always listen to others and adopt their suggestions there are other statements not accurate but overall the assessment was on target

It was very helpful to get feedback on my answers. I liked how positive feedback was included as well as areas I could work on.

It is always good to have feedback on how you can improve. I found this really useful thank you

I found this useful because now i know where i can better myself and where i need to be more confident.

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  • Why Machines make better decisions than humans (oh and why I hate Simon Sinek )

Hiring with AI, fairer, faster and better

Why Machines make better decisions than humans (oh and why I hate Simon Sinek )

BY Team PredictiveHire

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This came up in my feed last week prompting me to share my own 2 cents on why machines are better at hiring decisions than humans.

Did you know that the Wikipedia list of cognitive biases contains 185 entries? This somewhat exhausting article lays out in excruciating detail biases I didn’t know could exist and arrives at the conclusion that they are mostly unalterable and fixed regardless of how much unconscious bias training you attend in your lifetime.

I get asked A LOT about how I can work for a company that sells technology that relies on ‘machines’ to make people decisions.

I will keep it simple … 2 reasons

Because as per above, our biases are so embedded and invisible mostly we just can’t check ourselves in the moment to manage those biases. (I would rather hire women, ideally, mums, who like the same podcast series as me and straight through to offer stage if they like Larry David humour )

And Machines can be ‘trained’ …humans can’t, as easily or efficiently

But the myriad and ever-present news articles about ‘algorithmic bias’ has lumped all machine learning into one massive alphabet soup of ‘don’t trust the machine!

Really? Are we also biased against machines now? I saw Terminator 2 as well and worry about machines taking over the world ….but that’s a massive leap from the practice of bringing data, objective data into the most critical decision you will make as a people leader, who to hire. The divorce rate is for me the proof point that humans suck at making critical people decisions.

I’ve been in the People space for a while. I was lucky enough to work with 2 organisations BCG and the REA Group that value their people above all else. They also value making money and having your engineers and consultants sucked up in recruiting days and campaigns is a massive investment of your scarce and valuable capacity. I have found most companies don’t even know how much it costs to hire one person because no one is tracking the time investment.

We are all time poor and so we often default on hiring based on ‘pedigree’ . Someone has GE on their CV, they must be great as GE only hires great people. That’s a pretty loose /random data point for making a hiring decision

So here is a non data scientist view of why you should trust machine learning to find the right people and when you shouldn’t

First credit to this post which helped me put this into non tech speak .

https://medium.com/mit-media-lab/the-algorithms-arent-biased-we-are-a691f5f6f6f2

Why use Machine Learning at all for decision-making ? Because it underwrites making repeatable, objectively valid (ie data based) decisions at scale.

Value to the organisation:

• Use less resources to hire
• Every applicant gets a fair go at the role
• Every applicant is interviewed
• Hire the person who will succeed vs someone your gut tells you will succeed

How do you ensure there is no or limited bias in the machine learning ?

Take a look at:

– what’s the data being used to build the model
– what are you doing to that data to build the model

If you build models off the profile of your own talent and that talent is homogenous and monochromatic, then so will be the data model and you are back to self reinforcing hiring

If you are using data which looks at age, gender, ethnicity and all those visible markers of bias , then sure enough, you will amplify that bias in your machine learning

Relying on internal performance data to make people decisions, that’s like layering bias upon bias. The same as building a sentencing algorithm with sentencing data from the US court system, which is already biased against black men.

Reality is that machine learning is by its very definition aiming to bias decisions, and removing bias is driven by what bits of training data you use to feed the machine. This means you can make sure the data you train with has no bias.

Machine learning outcomes are testable and corrective measures remain consistent, unlike in humans. The ability to test both training data and outcome data, continuously, allows you to detect and correct the slightest bias if it ever occurs.

Tick to objective data which has no bio data (that means a big NO to CV and social media scraping )

Tick to using multiple machine learning models to continuously triangulate the model vs rely on one version of truth

So instead of lumping all AI and ML into one big bucket of ‘bias’ , look beneath the surface to really understand what’s going into the machine as that’s where amplification risks looms large

Oh and the reason why I hate Simon Sinek …

I don’t actually at all, but if a candidate said that to me in an interview I’d probably hire them for it because I would make some superficial extrapolation about their personality based on it:-

• first it would tell me they watch ted talks and so that eeks of cleverness and learning appetite

• second it would tell me they are confident to be contrarian and that would make me believe that they are better leaders

• third I would infer they are not sucked into the vortex of thinking that culture is the panacea to every people problem.

See how easy it is to make an unbiased hiring decision?

Soon (maybe already) you will be putting yours and your loved ones lives in the hands of algorithms when you ride in that self driven car. Algorithms are extensions to our cognitive ability helping us make better decisions, faster and consistently based on data. Even in hiring.

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