Govert Doedijns

You've probably seen the fascinating data on personality changes reported in the Financial Times. But how might this impact the workplace? You can find correlations between personality and work engagement in the second table (average weighted in the first column; corrected for unreliability in the second). The short story is that rises in neuroticism and declines in conscientiousness are not good. Plenty of interesting takes on this research to explore, including in the brilliant FT article from John Burn-Murdoch. I'll just say this... As a leader or an HR team, there's little you can do to influence personality changes in the general population. What you can do, however, is make every effort to design YOUR culture in ways that support people and engage them meaningfully with their work. If you treat people like an expendable resource and place them in environments of high anxiety and overwork, these trends are likely to come back to bite you. --------- Data in the second chart from: Association between the five-factor model of personality and work engagement, by Toshiki Fukuzaki and Noburu Iwata.

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I have been bewildered by the so many conflicting fairness metrics out there. So, when you are free from corporate life and management entertainment, this is what I end up with. Some people solve puzzles; as a machine learning nerd, I come up with this paper: https://lnkd.in/e_TX5BSr. Ever wondered why achieving "fair" AI feels like trying to satisfy a room full of lawyers, ethicists, AND statisticians all at once? Turns out there's actual math behind why fairness is so... unfair to achieve! I just finished a theoretical deep-dive that connects ALL major fairness measures (demographic parity, equalized opportunity, calibration, individual fairness) through a single mathematical framework using Pinsker's inequality from information theory. This is a draft, so if you find any mistake, I would greatly appreciate the feedbacks. If you find many of them and correct them then I would like to ask you as a co-author for the final paper. What we discovered: - There's a mathematical hierarchy to fairness - some criteria are fundamentally harder to achieve than others - Information theory explains WHY multiple fairness criteria can't be satisfied simultaneously (looking at you, Kleinberg impossibility results!) - We can now predict exactly how much bias remains in any "fair" system using mutual information bounds - Practical win: Direct formulas for regulatory compliance (hello, 80% rule and GDPR!) The bottom line: Instead of playing fairness whack-a-mole, we now have a unified theoretical compass that shows us the fundamental trade-offs and helps navigate them systematically. For my fellow ML practitioners: This isn't just theoretical navel-gazing. We derive concrete formulas for constraint setting, bias detection, and algorithm design that you can actually use in production systems. The best part? All those "impossible" fairness trade-offs everyone complains about? They're not bugs - they're features of information theory itself. Sometimes it helps to know you're fighting math, not just organizational politics! Who else has been frustrated by the fairness metrics maze? What's your experience navigating these trade-offs in practice?

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For most of human history, learning was vertical - passed down through families and traditions. Then came horizontal learning, spreading knowledge across peers, schools, and institutions. Now, a third phase may be emerging: ambient learning. Knowledge surrounds us - embedded in daily life, supported by AI, sensors, and connected environments. This shift doesn’t just transform education - it revives polymathic thinking, freeing us to connect ideas across fields. Learning is no longer an event - it’s an atmosphere. https://lnkd.in/euEuBGJE #Learning #Education #Universities #Degrees #Credentials #Skills

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Great leadership development creates transformation and lasting impact. The real power of facilitation lies in holding space rather than filling it, asking questions that spark reflection, modelling authenticity and courage, linking skills to purpose, and turning events into journeys. When done well, facilitation becomes less about slides and more about creating the soil where leaders grow. #facilitation #leadership #development

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From Tension to Trust: Navigating Conflict with W–NLTTLM In most conflicts, we rush to prove our point—rarely pausing to understand the other side. That’s where the W–NLTTLM framework makes all the difference. It invites us to shift focus: ✔️ Willingness — to converge, to listen, to find common ground ✔️ Now–Later — what needs resolving immediately vs. what can wait ✔️ This–That — clarity on what we’re offering vs. what we expect ✔️ Less–More — deciding where we’re flexible and where we stand firm Seven letters. One powerful mindset shift. When paired with awareness of the Balance of Power, it transforms tough conversations—helping us move from tension to trust. 🎥 In this short podcast with Prashant Singh, we unpack how leaders can apply W–NLTTLM to navigate conflict and build stronger relationships. #Leadership #Training #Negotiations #ConflictManagement #LeadershipLadderAcademy #SelfManagement #ProfessionalDevelopment #WorkplaceLearning #PersonalEffectiveness #trainingdevelopment #leadershiplessons #GrowthMindset Follow our company page Leadership Ladder Academy Pvt Ltd for regular updates!

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