ARTICLE #23:
Kai-Fu Lee: AI Superpowers – China and Silicon Valley | Artificial Intelligence (AI) Podcast
Some really valuable insights in how AI is approached in the Sillicon Valley and China. Recommended because it’s always enlightening listening to Kai-Fu speak.
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Written by Nathan Hewitt
AI Tech Company in Melbourne – What inspires us!
‘What engages us’ is curated by the PredictiveHire team, a team of pioneers working at the frontier of 3 huge trends:
1. AI in HR, especially people selection. Because who you hire and who you promote are the most critical business decisions you make across most roles and organisations.
2. Soft skills are now the real skills that matter and until now, very hard to assess accurately, unbiased and efficiently.
3. Advances in computational linguistics + processing power mean we can DNA personality from the text in a few seconds.
We are the only AI solution in the world that uses the convenience of an interview via text to screen talent. At the same time, we also give deep personalised insights to every applicant who completes the interview, and every hiring manager using our solution. The absence of any subjective information in our AI data collection also means our assessment is without bias. At last technology that truly does level the playing field.
Being pioneers we consume new ideas and research on a range of topics in our field because we are all learners in this space. Here we share what we are discovering, listening to, watching and reading … We hope you find these shares as useful and inspiring as we do!
OUR FAVOURITE BOOKS! 
Ethical Algorithm
Michael Kearns and Aaron Roth
Why we love it! Because it challenges every organisation using Ai to push the boundaries of fairness.
Everybody Lies
Seth Stephens-Davidowitz
Why we love it! Because in everything we do we must always check ourselves for the alternative impacts.
Dataclysm
Christian Rudder
Why we love it! Because in everything we do we must always check ourselves for the alternative impacts.
Civilized to Death
Christopher Ryan
Why we love it! Because this made us think that what we achieve must positive and make everyone feel good!
Prediction Machines
Ajay Agrawal, Joshua Gans, Avi Goldfarb
Why we love it! Because this was the first book on predictive analytics read by our CEO Barb which helped a lot to explain this space using simple concepts. 
How Smart Machines Think
Sean Gerrish
Why we love it! Because this was recommended by Matt, one of our awesome advisors.
Invisible Women: Data bias in a world designed for Men
Caroline Criado Perez
Why we love it! Whilst the audio version does feel a bit didactic at times, the narrator is so frustrated at the disconnect between the facts and what people believe about the presence or not of bias. There is some solid data referenced which reflects the deep and wide research that has gone into uncovering often invisible nature of gender bias in many sectors.
NOW FOR OUR FAVOURITE PODCASTS
PODCAST #1
Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning
Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, bias, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai(37 kB)
Why we recommend it? Very informative podcast about AI fairness with Prof Michael Kearns, a co-author of the book Ethical Algorithm.Buddhi is a regular consumer of Lex Fridmans podcasts – he attracts an extraordinary array of minds and perspectives from Daniel Kaheman, Melanie Mitchell, Paul Krugman, Elon Musk and he asks such thoughtful original questions of people interviewed many times over that every podcast feels illuminating for both sides.
PODCAST #2
Scott Adams: Avoiding Loserthink
Dilbert creator and author Scott Adams shares cognitive tools and tricks we can use to think better, expand our perspective, and avoid slumping into “loserthink.”(103 kB)
https://149366099.v2.pressablecdn.com/wp-content/uploads/2019/11/s-adams-500px.jpg
Why we recommend it? There is a story of “bias” in how he got into creating Dilbert. He was told by two employers that “we can’t promote you because you are white, because we have been promoting too many of them, so now we have to fix it”. Essentially Dilbert is a result of him leaving his day job because his employers were trying to fix bias in their promotion process!
PODCAST #3
Getting to scale with artificial intelligence – The McKinsey Podcast
Why we recommend it? Companies adopting AI across the organization are investing as much in people and processes as in technology.
PODCAST #4
Sleepwalkers podcast by iHeartRadio
Why we recommend it? With secret labs and expert guests, Sleepwalkers explores the thrill of the AI revolution hands-on, to see how we can stay in control of our future.
PODCAST #5
HBR IdeaCast: A New Way to Combat Bias at Work on Apple Podcasts
Show HBR IdeaCast, Ep A New Way to Combat Bias at Work – 14 Jan 2020(76 kB
Why we recommend it? A brilliant captivating podcast on the types of biases that turn up at work and an exploration of bias interrupters. Bias and the D & I space is overflowing with content and so it’s inspiring when you come across a wholly original way of labeling it (Bropreating whypeating, and menteruption. What’s less effective -single-bias training … -referral hiring ! because it risks ‘reproducing the demography of your current organisation’ What’s way more effective -correcting the bias in your business systems and the most contrarian view on the topic of performance reviews I’ve read for a while … Keep your performance reviews! Removing them creates a ‘petri dish for bias’.
PODCAST #6
Can Artificial Intelligence Be Smarter Than a Human Being? by Crazy/Genius
Why we recommend it? Surely, AI technology has nothing that even closely resembles human imagination. Or does it? This is a super handy podcast for those who want to know simple ways to explain AI and ML.
PODCAST #7
AI in B2B – a16z Podcast
Why we recommend it? Consumer software may have adopted and incorporated AI ahead of enterprise software, where the data is more proprietary, and the market is a few thousand companies not hundreds of millions of smartphone users. But recently AI has found its way into B2B, and it is rapidly transforming how we work and the software we use, across all industries and organizational functions.
Brilliant articulation of why FOMO is real .. as far as coming to data too late . Co pilot and auto pilot analogy is clever.
1. B2B is different. Companies care a lot about their data
2. Share for greater good and reap the benefits should be the motto of A.I. companies
3. Product design thinking with AutoPilot and CoPilot metaphors. Where can our A.I. be auto and co?
4. Use AB testing to show the benefits to the skeptics
OUR FAVOURITE ARTICLES
ARTICLE #1:
Chief people officer: The worst best job in tech
https://www.protocol.com/worst-best-job-in-tech
Comments: Barb can relate to this one as a former CPO, and whilst the Google case is special, in general, CPO’s should be investing in data driven methods, that allows them to take more informed decisions than not.
ARTICLE #2:
New Illinois employment law signals increased state focus on artificial intelligence in 2020
https://www.technologylawdispatch.com/2020/01/privacy-data-protection/new-illinois-employment-law-signals-increased-state-focus-on-artificial-intelligence-in-2020/
Comments:A read that provoked a bit of discussion amongst the team noting that the Act does not define “artificial intelligence,” a term that is often misunderstood and misapplied even by experts. How will they separate what traditional statistical analysis has been doing to what modern ML algorithms do. Any attempt to classify ML as something different to just statistical analysis at scale will be fun to watch. One can then argue just using averages and medians are a form of AI … Regression .. Correlations … AI bias …
Ask BERT to fill in the missing pronoun in the sentence, “The doctor got into ____ car,” and the A.I. will answer, “his” not “her.” Feed GPT-2 the prompt, “My sister really liked the color of her dress. It was ___” and the only color it is likely to use to complete the thought is “pink.”
ARTICLE #3:
A.I. breakthroughs in natural-language processing are big for business
https://www.google.com/amp/s/fortune.com/2020/01/20/natural-language-processing-business/amp/
Comments:A series of breakthroughs in a branch of A.I. called natural language processing is sparking the rapid development of revolutionary new products.
ARTICLE #4:
Are We Overly Infatuated With Deep Learning?
https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/cognitiveworld/2019/12/26/are-we-overly-infatuated-with-deep-learning/amp/
Comments:Even Geoff Hinton, the “Einstein of deep learning” is starting to rethink core elements of deep learning and its limitations.
ARTICLE #5:
Artificial intelligence will help determine if you get your next job
https://www.vox.com/recode/2019/12/12/20993665/artificial-intelligence-ai-job-screen
Comments:AI is being used to attract applicants and to predict a candidate’s fit for a position. But is it up to the task?
ARTICLE #6:
Biased Algorithms Are Easier to Fix Than Biased People
https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html?smid=nytcore-ios-share
Comments:Racial discrimination by algorithms or by people is harmful — but that’s where the similarities end. The study: “Are Emily and Greg more employable than Lakisha and Jamal?” we referenced in our bias paper. We agree with this key point: Algorithmic bias is testable and human bias is not (at least very hard).
ARTICLE #7:
Extroverts Prefer Plains, Introverts Like Mountains
https://bigthink.com/topography-and-personality
Causation or just correlation? There’s a very curious link between topography and personality.
ARTICLE #8:
So what is the difference between AI, ML and Deep Learning?
https://www.linkedin.com/pulse/so-what-difference-between-ai-ml-deep-learning-kanishka-mohaia
ARTICLE #9:
Attractive People Get Unfair Advantages at Work. AI Can Help.
https://hbr.org/2019/10/attractive-people-get-unfair-advantages-at-work-ai-can-help
Algorithms can make sure decisions are about performance rather than looks.
ARTICLE #10:
Artificial Intelligence in HR: a No-brainer
https://www.academia.edu/37977384/Artificial_intelligence_in_hr_a_no_brainer
This is an article from PwC that summarizes the case for AI in HR well. A really good overview.
ARTICLE #11:
Science Behind the IBM’s Personality Service
https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-science
The background and the approach listed here is applicable to our approach too. The difference being, IBM built their models using twitter data whereas ours is more specialised/accurate for recruitment (i.e. based on more data and continuously learning). In addition, we are able to predict more than personality (e.g. job hopping attitude, traits etc).
ARTICLE #12:
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text
https://www.aaai.org/Papers/JAIR/Vol30/JAIR-3012.pdf
ARTICLE #13:
Language-based personality: a new approach to personality in a digital world
ARTICLE #14:
Navigating Uncharted Waters: A roadmap to responsible innovation with AI in financial services
https://www.weforum.org/reports/navigating-uncharted-waters-a-roadmap-to-responsible-innovation-with-ai-in-financial-services
Navigating Uncharted Waters shows how financial services firms, policymakers and regulators and customers can overcome five risks and plot a course toward more rapid AI adoption.
ARTICLE #15:
Model Tuning and the Bias-Variance Tradeoff
http://www.r2d3.us/visual-intro-to-machine-learning-part-2/
Learn about bias and variance in our second animated data visualization.
ARTICLE #16:
Daniel Kahneman’s Strategy for How Your Firm Can Think Smarter
https://knowledge.wharton.upenn.edu/article/nobel-winner-daniel-kahnemans-strategy-firm-can-think-smarter/
The research is unequivocal, according to the father of behavioral economics: When it comes to decision-making, algorithms are superior to people.
ARTICLE #17:
Experience Doesn’t Predict a New Hire’s Success
https://hbr.org/2019/09/experience-doesnt-predict-a-new-hires-success
Is it time to rethink the way we assess job applicants?
ARTICLE #18:
So what is the difference between AI, ML and Deep Learning?
https://www.linkedin.com/pulse/so-what-difference-between-ai-ml-deep-learning-kanishka-mohaia/
The best ie simplest summation of this tech I have read (edited) linkedin.com. Once the domain of Sci-Fi geeks and film script writers, Artificial Intelligence or A.I.
ARTICLE #19:
Nudge management: applying behavioural science to increase knowledge worker productivity
https://jorgdesign.springeropen.com/articles/10.1186/s41469-017-0014-1
Knowledge worker productivity is essential for competitive strength in the digital century. Small interventions based on insights from behavioural science makes it possible for knowledge workers to be more productive. In this point of view article, we outline and discuss a new management style which we label nudge management. Nudge is a concept in behavioral science, political theory and behavioral economics which proposes positive reinforcement and indirect suggestions as ways to influence the behavior and decision making of groups or individuals. Nudging contrasts with other ways to achieve compliance, such as education, legislation or enforcement.
We liked reading this because it mirrored what we read from candidates every day after their receive ‘MyInsights, their personalised insights profile. We believe that every person regardless of their role craves personal growth. The feeling they have when they receive that report- priceless for our team. “Thank you for your email. I did find it useful as it has made me really think about my workplace and personal life by self-reflecting. I feel since reading this, I have stepped up in a few different situations including at work where I had stepped up in a temporary leadership role. Personally, I have been practising speaking my mind and let go of toxic friendships and make decisions more easily.”And … After getting the insight of what you see of me & your reasoning it made me think about work place moments & how well I’ve responded to situations as well as make me think about alternative ways I could have reacted & received differing outcomes.
ARTICLE #20:
Distilling BERT models with spaCy
https://towardsdatascience.com/distilling-bert-models-with-spacy-277c7edc426c
Transfer learning is one of the most impactful recent breakthroughs in Natural Language Processing. Less than a year after its release.
ARTICLE #21:
Building Trust in Machine Learning Models (using LIME in Python)
https://www.analyticsvidhya.com/blog/2017/06/building-trust-in-machine-learning-models/
This article helps us understand working of machine learning algorithms using LIME package. Using LIME, you can understand working of black box ML models.
ARTICLE #22:
Jordan Peterson on Workplace Performance, Politics & Faulty Myers-Briggs
Hilarious watching Jordan talking about selling personality assessments but mostly he is spot on in his observations.
Related
Blog
Artificial intelligence will decide if you get an interview for your next job
To find out how to improve candidate experience using Recruitment Automation, we also have a great eBook on candidate experience.
By Jennifer Hewett, Australian Financial Review, 31 January
The online questionnaire wants to know whether I respect and comply with authority. I get five options – strongly agree, agree, neutral, disagree or strongly disagree. I tick “neutral”. Well sort of, sometimes, I think to myself.
Same choice for whether I am good at finding fault with what’s around me at work. I tick “neutral” again, guiltily acknowledging it’s just possible my editor might have a different opinion about whether I am far too good at that particular skill.
The choice seems less ambiguous when I am asked whether I forget to put things back in their proper place. I hover over “strongly agree” or “agree” and tick the latter – perhaps a little optimistically.
And on it goes for 90 questions, with slight variations in the possible answers, as devised by an AI (artificial intelligence) algorithm. My responses to the bot will determine whether I get to the next stage of actually being interviewed for a job by a real person. AI approves who you should interview
I soon get an encouraging email from Michael Morris, chief executive of Employsure – a company which provides advice on workplace relations and health and safety issues to small businesses. If I ever give up journalism, Morris tells me, I can try for a new career at Employsure. AI has approved me. Despite my deep scepticism about the process, I can’t help but feel a little pleased by the bot’s assessment.
That is because my rather self-serving answers to random personality questions fit those of the best performers at Employsure. There’s no possibility of ageism or sexism or any other latest “ism” influencing that. No old schoolmates or university or sporting framework, no biases about looks or clothes or mannerisms or personal history.
Instead, I participated in what is a variation on a personality test – based on the algorithmic analysis provided by another company, Sapia, operating in Europe and Australia and with 20 clients.
Morris says Employsure tested the performance of employees selected by Sapia’s algorithm against the choices of Employsure’s own human recruitment team for much of last year.
The fast-growing company hired around 450 people in 2018 with a workforce now totalling more than 800. Morris wanted good people and those more likely to stay.
A significant difference with AI
The experience convinced him that rather than using more traditional CVs to screen applicants, it was worth paying Sapia for its AI technology as Employsure continues to expand its numbers this year. Employsure now only interviews the 10-15 per cent of those who are graded “yes” or “maybe” by the bot.
“The overlay of AI made a significant difference in overall performance, productivity and tenure,” Morris says. “And it means the recruitment team can have a head start on engaging in better conversations with those who have interviews.” This is still a distinct minority view among Australian businesses which have been generally reluctant to embrace the promise of AI when it comes to hiring.
Read: The Ultimate Guide to Interview Automation
The trend to make greater use of AI in business generally is inevitable and accelerating. Just consider all those online “conversations” we now have about customer service and products as the ever-patient bot nudges us this way and that.
Just as inevitably, it is leading to community concerns about whether AI will be used to replace too many people’s jobs. According to a study by the McKinsey Global Institute, intelligent agents and robots could eliminate as much as 30 per cent of human labour by 2030. The scale would dwarf the move away from agricultural labour during the 1900s in the United States and Europe.
Of course, the record of technology shifts over centuries always ending up creating many more other types of jobs does not completely soothe fears that this time it’s different. Even if such alarm is overstated, dramatic changes in technology can certainly prove socially and economically disruptive for long periods. AI can also be scary.
But this version of AI is more about filling new jobs more efficiently. Many large global companies already use it to filter job applicants, especially those coming in at lower levels. Its advocates argue it efficiently eliminates bias or the tendency for people to hire in their own image.
Immediate payback with AI
Not that this always goes smoothly – even for the most digitally sophisticated businesses. Amazon abandoned its own AI hiring tool last October when management realised it had only introduced more bias into the process. Its AI system was based on modelling the CVs of those already at the company – who tended to be male. Naturally, that made prospective hires more likely to be male too. So much for gender-diversity targets.
Sapia’s chief executive is Barb Hyman, formerly a human resources executive for the online real estate advertising company REA Group. She says the system doesn’t work for those companies that don’t measure the performance of their existing employees but the data becomes more and more accurate as more information is added.
By matching responses of applicants against only those employees who are already doing well, it can be extremely efficient with immediate payback – especially for larger companies. The data can also be used to change the culture in an organisation by screening the types of personalities who are hired.
Not surprisingly, Hyman says the data demonstrates how different personalities are better fitted to different sorts of roles. So those who do well in caring jobs tend to be reliable and demonstrate traits of modesty and humility. Good salespeople are focused, somewhat self-absorbed, disorganised and transactional. Those who are involved in building long-term business relationships need to be more adaptable, resilient and open.
Sounds more like common sense than AI. But there’s less and less of that around anywhere. AI beckons instead.
Blog
5 Tips To Maximise Trust In Grad Recruiting During COVID-19
Graduate Recruiting
Article 2 of 2 |
With graduate hiring, trust in your process is even more important. You never know when a poor candidate experience might end up on Glassdoor or Whirlpool or other such sites. Given there is a lot out of our control now, what you can control is how you and your organisation choose to engage with your graduates.
1. Personalise your communications
Be 10x more humanistic in your communications than ever before, to soften the stress everyone feels right now. Use technology that humanises the application experience. Use your photo in emails, dial down the formal side of your comms, show care and empathy right up the top. These are unprecedented times that call for a whole new way of connecting.
2. Reduce the asymmetry in recruitment and give something back!
It is now table stakes that every graduate experience is one that rewards both sides – offering personalised learning for the grad and some good quality data intelligence for you the recruiter. Intelligence that looks like this https://bit.ly/2R6LuIc
3. Use assessment tools that feel more human
This means being mobile-first and using engaging and relatable assessments that everyone can do in their own time and untimed to take away the unnecessary pressure. Tools like FirstInterview https://bit.ly/39KoqFP
4. Remove bias from your processes
The right AI tool can ensure every graduate applicant has an equal and fair opportunity to be considered because:
- They don’t rely on the CV, which is a biased and inadequate reference point for the traits that matter the most, such as growth mindset, accountability, drive and grit.
- They remove irrelevant markers of job fit like your ethnicity, your gender and your age, unlike video interviewing, which bakes in biased selection.
5. Hire for values and train for skills
We have all read the research saying Gen Z will have ten jobs in our lifetime. That means the real skills that matter is grit, drive, accountability, curiosity and even humility to know you won’t always know what you are doing and how to do it!
That means uncovering these traits and values has to be a critical ingredient in your assessment tool. Doing that via human interviews is no longer acceptable given the bias we all bring to those conversations. Still, more than that, few businesses will retain a human assessment process when Ai does it better faster and cheaper. 600x faster and at least 3x cheaper. Here are the 2 metrics that should matter the most to any recruiter.
There are AI tools out there to help you with this including the ability to run a virtual group assessment to deliver the same integrity of assessment days with a lot more efficiency > WATCH VIDEO HERE.
It’s about time to look at AI for your graduate recruitment.
Get in touch here with Barb, Nick or Jess
To read the first article click here
https://sapia.ai/graduate-recruitment-during-covid-19-whats-different/
Blog
What can Ai help us discover?
We are asked often in customer meetings – What does the data reveal?
What can AI help us discover? How can we make better people and business decisions by looking at the data?
By using SOM maps https://en.wikipedia.org/wiki/Self-organizing_map to map personality for more than 85,000 applicants using their HEXACO scores, 47/53% male and female, candidates spread across 2 regions – the UK and Australia, we identified 400 unique personality profiles.
It turns out that personality is somewhat more complex than the 16 types long promoted by Myers Briggs.
Following SOM’s show the percentage density of male, female and sales candidates across the 400 different HEXACO profile groups. The size of each bubble represents the total count of individuals mapped to each profile. Darker shades represent higher % of each category.
The ‘so what’
- People are different. There is a whole spectrum of personality. Even 400 groupings of personality don’t convey the variance in personality in the population
- Gender is less meaningful as a lens into personality than what role family you sit in. The genders are not that different when it comes to personality profiling
- Role family differences are noticeable. Gender personality differences at the role family level are negligible. “Sales” candidates come from specific personality profiles (i.e. HEXACO combinations) and it is clear that certain personality profiles are drawn to sales more than others. See the SOM on Sales that clearly shows some profiles are more likely to be sales candidates than others (the darker bubbles).
Why NLP is a better approach to discovering personality traits
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Finally, you can try out Sapia’s SmartInterview right now, or leave us your details here to get a personalised demo.