LIVE! Candidate Feedback:

Really spoke to me, I feel like you described me perfectly.

Iam confident, I lead conversations and i do express my point of view.

I like it because it teaches about professionalism at the workplace. How a employee balance his personal and professional life. It teaches us how to build collaborative relationships with other peers and allow collective success of all team members.

I did find it useful. It helps me to introspect more and develop my skills so that I can be a person with whom people will be comfortable and work as a team to yield better results. I hope integrating the feedbacks will help to widen my perspective and learn new things.

I found this extremely eye opening and positive.

It was great. I'm amazed that these insights could be gained from those questions and answers.

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

I didn’t it’s not me at all lol I tell everybody my business and make friends ASAP I meet ppl I take part in all outside occasions I don’t argue my case I do as I’m told by my supervisor Totally wrong I’m aftaid 😢

I am very impressed with your summary of my personality. It took some years to get to a point where I became matured in managing my time effectively. I had learn that accuracy is much more important than speed, but also how the two goes hand in hand once you've mastered both. Working for a company few years ago I had failed in getting my certificate. I then got the opportunity to redo it and then became the best student. At the night of the graduation I had not receive my certificate cause I thought I didn't make it. And the next day I was called in to say that I should have gone on stage too to get the certificate. I was a little disappointed but not entirely cause the fact that I completed the course was so much more Important to me than being seen, cause it gave me a satisfactory knowing I went back and tried again. I would work extra hard to please everyone to make a good impression so I can get a better post but at the end I didn't get a permanent job. The trainers had developed a great love and amusement for who I am, and my work ethic. They applaud all my hard work and had told me they will never get someone like me again and I left there thanking them for giving me the opportunity to work there. I had compromise alot in proving my working ability, however my internship ended and I had to go. People respect my standard of working and all the extra time I put in to complete a task. I am open to new ways of completing a task , I believe in critism because it creates a platform for me to grow and become better.

I think it was helpful but i do check the details as i do like to ensure we can get things resloved on the one call.

Spot on! Everything is said is true. 😊

I did find this useful as I have now learnt attributes to use when dealing with future situations at work places.

Thanks for that Any feedback is positive feedback

Really valuable insights. The results helped me to articulate myself and more confidently describe the kind of mentality I have towards work!

I found this very useful because now I can focus on what I need to work on about myself

This helped me get a better insight of how my personality is portrayed to others

WOW that was amazing you have picked me to a tee, I enjoyed that feedback and advise and I will also take it onboard Thank You

Really good reflection on my answers, found out about about myself

I'm not sure I found it useful as such. I did find that most of the comments and summary were straight out of books dealing with people who are resistant to change... the comments contradicted the summary summary

It's good to hear what an opinion of me would be and also the advice about being more outspoken will give me a bit of courage to do so

I found this useful because it gave me an insight into what other people think about me

Yes thought it was very interesting

The way I was described is the way that I am everyday, reading the coaching tip and advice allows me to see where I can improve myself

Yes. It gave me positive insight on how to better myself. Thank you.

This was refreshing to read through after submitting my application, and scarily accurate! I’ve never seen a company who cares so much about their applicants, employees and customers!

Because it will help me understand myself more , and I find feedback great

I really value the feedback because it is something that has rarely been given to me. I really appreciate that my personality traits have been taken into consideration and they were pretty much spot on. I worked for Webhelp previously but due to personal issues I couldn't continue at that time but would definitely be focused 100% on working for the company again.

I found this very useful because now I full idea of what kind of a person I am and what I need to work on as an individual.

yes, i found it so useful as i can improve by ability to experience new things

I am enlighten about my own attributes it boost myself to continue things like that.

it was very helpful and self motivating

understanding the application is so very important and you've got me about right

I’m very grateful for hearing these insights and feedback. I’m going to take this all into consideration when ever it’s needed.

How do I improve myself, that will help me secure employment. Thankyou Sandy

The insights are really appreciating. The coaching tip is also very helpful to understand my areas of improvement. Thank you for sharing the results so promptly.

It's good to see how im viewed for this test thank you.

What inspires us

Academic Research

Our data science team is always learning and experimenting which means they are closely connected to other research in this area around the world.

Personality testing in employment settings: Problems and issues in the application of typical selection practices

Personality testing in employment settings: Problems and issues in the application of typical selection practices

Why we love it

This paper just nails everything that is wrong with traditional psychometric testing for hiring.It is great to read a visionary paper published in 2001 that foresaw all these issues almost 20 years earlier.

What I learnt from it

Some of the issues highlighted are,

  1. the (in)appropriateness of linear selection models;
  2. the problem of personality-related self-selection effects;
  3. the multi-dimensionality of personality;
  4. bias associated with social desirability, impression management, and faking in top-down selection models; and
  5. the legal implications of personality assessment in employment contexts.

Why it’s a must

Before fixing the issues of traditional psychometric testing for hiring, is it important to understand and acknowledge them. It also feels really good to see how PH’s approach solves some of these issues. We use machine learning models that are able to model complex non-linear relationships. Moreover, complex multi-dimensional relationships between the hiring outcome and not just personality, but rather a broad range of signal variables are LEARNT by our models. We use a chat-like text-based conversation to assess candidates and have noticed how hard it is to game such a system compared to MCQs used in psychometric testing. With regard to bias in our models, we constantly evaluate our models for gender and ethnicity bias and remediate such biases.

Who should pay attention

CHROs, Hiring managers, Talent Acquisition teams

Notes from the frontier: Tackling bias in AI (and in humans)

Notes from the frontier: Tackling bias in AI (and in humans)

Why we love it

Authors provide a succinct yet comprehensive overview of how biases can be baked into AI, ways to mitigate it and when developed well, how AI can help reduce human biases in decision making. It is written for a non-technical audience but includes a great list of original research work in the Endnotes section that anyone interested in further reading can access.

What I learnt from it

It is great to see our own experience building AI solutions in a domain (talent acquisition) where unconscious bias is commonplace, resonating well with what the authors describe. Especially in how AI can help bring human bias to light and steps to follow in building AI solutions with no measurable biases. Some of the key points include:

  • Having a clear and applicable definition of bias and fairness. This includes thinking about group vs. individual fairness, protected characteristics, predictive parity vs. error rate parity etc
  • Being aware of biases in data, data collection methods, and societally unacceptable correlations (algorithmic biases) learnt by algorithms, all of which are discoverable within a sound machine learning process
  • Using methods such as Local Interpretable Model-agnostic Explanations (LIME) to explain the outcomes of seemingly complex algorithms that act as black boxes.

Why it’s a must

As AI becomes pervasive, the topic of algorithmic bias and fairness has attracted lot of attention. This is a great short paper for decision makers, especially at the C-level to demystify the topic. I would say a must read for all CHRO’s exploring the use of AI in their workflows. Especially the five suggestions listed in the conclusion of the paper forms a framework to maximise fairness and minimise bias when using AI.

Who should pay attention

CHROs, Hiring managers, Business leaders

Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach

Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach

 

Why we love it

This two papers highlight how incredibly one’s personality can be inferred from their language use and how these inferred personality profiles can be used to find the best congruence between one’s personality and personality demand of jobs. These papers in fact provided us inspiration and a benchmark for our own ‘language use to personality’ model.

Links

https://doi.org/10.1371/journal.pone.0073791 and

https://doi.org/10.1073/pnas.1917942116

 

What I learnt from it

It was in fact scary at first to see how much we give away in our conversations about who we are. After coming into terms with that, it was encouraging for the DS side of me to see how open-vocabulary approaches can outperform the dictionary-based closed-vocabulary approaches used in previous studies in modelling language use. On the other hand, when these models are applied to Tweets from different professionals, it is amazing to see how different professions have different personality signatures. I can personally vouch for high openness and low agreeableness among top open-source developers.

Why it’s a must

These two pieces of research reaffirm the importance of congruence between one’s personality and job/career. As Denissen et al. (2018) put it, “economic success depends not only on having a ‘successful personality’ but also, in part, on finding the best niche for one’s personality”. In evaluating this congruence, one’s language use can reveal a lot about him/her and machine learning models can model complex non-linear relationships between personality and personality demand of jobs.

Who should pay attention

ML/DS practitioners, HR professionals, Personality junkies

Data Science through the looking glass and what we found there

Data Science through the looking glass and what we found there

 

Why we love it

The authors are from Microsoft and they perform one of the largest analysis of Data Science projects to date, focusing on key information that helps both Data Science solution builders and practitioners alike. They analyse publicly accessible Python notebooks in GitHub and Machine Learning pipelines from a corporate Machine Learning platform, AnonSys. While some of the findings are not so surprising such as the 4-fold growth in number from 2017 to 2019, Python emerging as a de-facto standard for Data Science etc. some of the findings are quite interesting.

What I learnt from it

Some of the interesting findings include,
1) “Big” (i.e., most used) libraries are becoming “bigger”, consolidating well in the DS field.
2) Deep Learning is becoming more popular, yet accounts for less than 20% of DS today.
3) Analysis of the top libraries and top transformers used in Data Science pipelines points to how text, a source of unstructured data, is being tapped in to.
Above all, it is fascinating to see how Data Science/Machine Learning is becoming a ubiquitous technology.

Why it’s a must

The paper uncovers the current state and a number of trends in the Data Science/Machine Learning field. These trends provide practitioners with a good indication on which technology or libraries they should invest their time in.

Who should pay attention

DS/ML practitioners

Bidirectional LSTM-CRF models for sequence tagging

Bidirectional LSTM-CRF models for sequence tagging

Why we love it

I enjoy this paper since it introduces a new method for entity extraction, called BI-LSTM-CRF. The method proposed by this paper is more accurate than previous methods in various wide-applicable tasks in NLP. This paper inspires us to explore the possibility of applying deep learning methods in our work.

What I learnt from it

I learnt that (1) BI-LSTM-CRF out-performs previous models such as CRF or pure LSTM in POS tagging, chunking, and NER tasks. (2) The stacked structure in BI-LSTM-CRF allows it to factor in both word-level features via an LSTM layer and sentence-level feature via the CRF layer, so that it can make use of syntactic and contextual information in the language efficiently.

Why it’s a must

Published in 2015, this paper is one of the first work showing that LSTM based method can be applied to NLP tasks. According to Google Scholar, this paper has more than 1000 citations.

Who should pay attention?

NLP researchers, Machine Learning Engineers

RECENT POSTS

Get our insights newsletter to stay in the loop on how we are evolving PredictiveHire