‘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.
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.
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!
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’.
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.
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
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.
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.”
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.
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).
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).
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text https://www.aaai.org/Papers/JAIR/Vol30/JAIR-3012.pdf
Language-based personality: a new approach to personality in a digital world https://www.sciencedirect.com/science/article/pii/S2352154617300487
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.
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.
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.
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?
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.
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.
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.
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.
Jordan Peterson on Workplace Performance, Politics & Faulty Myers-Briggs https://www.youtube.com/watch?v=GXHj7eZ23gk&=&feature=youtu.be Hilarious watching Jordan talking about selling personality assessments but mostly he is spot on in his observations.
Kai-Fu Lee: AI Superpowers – China and Silicon Valley | Artificial Intelligence (AI) Podcast https://www.youtube.com/watch?v=cQ48rP_Rs4g&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 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.