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.
‘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
Joe & the Juice Partners with Sapia.ai, Scaling an Exceptional Candidate Experience and Cutting Time to Hire
Read the full press release about the partnership here.
Joe & the Juice, the trailblazing global juice bar and coffee concept, is renowned for its vibrant culture and commitment to cultivating talent. With humble roots from one store in Copenhagen, now with a presence in 17 markets, Joe & The Juice has built a culture that fosters growth and celebrates individuality.
But, as their footprint expands, so does the challenge of finding and hiring the right talent to embody their unique culture. With over 300,000 applications annually, the traditional hiring process using CVs was falling short – leaving candidates waiting and creating inefficiencies for the recruitment team. To address this, Joe & The Juice turned to Sapia.ai, a pioneer in ethical AI hiring solutions.
A Fresh Approach to Hiring
Through this partnership, Joe & The Juice has transformed its hiring process into an inclusive, efficient, and brand-aligned experience. Instead of faceless CVs, candidates now engage in an innovative chat-based interview that reflects the brand’s energy and ethos. Available in multiple languages, the AI-driven interview screens for alignment with the “Juicer DNA” and the brand’s core values, ensuring that every candidate feels seen and valued.
Candidates receive an engaging and fair interview experience as well as personality insights and coaching tips as part of their journey. In fact, 93% of candidates have found these insights useful, helping to deliver a world-class experience to candidates who are also potential guests of the brand.
“Every candidate interaction reflects our brand,” Sebastian Jeppesen, Global Head of Recruitment, shared. “Sapia.ai makes our recruitment process fair, enriching, and culture-driven.”
Results That Matter
For Joe & The Juice, the collaboration has yielded impressive results:
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33% Reduction in Screening Time: Pre-vetted shortlists from Sapia.ai’s platform ensure that recruiters can focus on top candidates, getting them behind the bar faster.
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Improved Candidate Satisfaction: With a 9/10 satisfaction score from over 55,000 interviews, candidates appreciate the fairness and transparency of the process.
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Bias-Free Hiring: By eliminating CVs and integrating blind AI that prioritizes fairness, Joe & The Juice ensures their hiring reflects the diverse communities they serve.
Frederik Rosenstand, Group Director of People & Development at Joe & The Juice, highlighted the transformative impact: “Our juicers are our future leaders, so using ethical AI to find the people who belong at Joe is critical to our long-term success. And now we do that with a fair, unbiased experience that aligns directly with our brand.”
Trailblazing for the hospitality industry
In an industry so wholly centred on people, Joe & the Juice is paving the way for similar brands to adopt technology that enables inclusive, human-first experiences that can reflect a brand’s core values.
If you’re curious about how Sapia.ai can transform your hiring process, check out our full case study on Joe & The Juice here.
Blog
Sapia.ai Wrapped 2024
It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.
And we’re just getting started 🚀
Take a look at the highlights of 2024
All-in-one hiring platform
This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.
Supercharging hiring efficiency
Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks.
Giving candidates the best experience
Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.
Leading the way in AI for hiring
We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders.
Blog
Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment
Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.
AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection.
This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?
1. The Static Nature of SJTs
Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements.
Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.
AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.
2. Richer Data Through Open-Ended Responses
One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.
AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.
3. The Candidate Experience: Stressful or Supportive?
SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.
In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.
4. Addressing Bias and Fairness
Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups.
AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.
5. An Assessment That Improves Over Time
Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.
AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.
Rethinking Candidate Assessment
As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.
These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.
For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you choose tools that help you make decisions that are both informed and equitable.