Psych Tech @ Work kapak görseli

Psych Tech @ Work

Psych Tech @ Work

Yazan: Charles Handler
Şimdi ücretsiz dinle, üyelik gerekmez

Season's Savings | £0.99/mo for 3 months

£5.99/mo after 3 months - terms apply. Cancel monthly
Science 4-Hire is now Psych Tech @ Work! - a podcast about safe innovation at the intersection of psychological science, technology, and the future of work. Psych Tech @ Work promotes safe technological innovation and human/machine partnerships as an essential force in creating equilibrium and between psychology and commerce. Maintaining this balance in a time of unprecedented change is essential for ensuring that the future of work is ethical, positive, and prosperous. Creating such a future requires an unprecedented level of interdisciplinary collaboration. With the goal of educating, engaging, and inspiring others through thoughtful and practical discussions with guests from a wide variety of backgrounds and specialties, Psych Tech @ Work provides a smorgasbord of food for thought and practical takeaways about the issues that will make or break the future of work!

charleshandler.substack.comCharles Handler Economics
Bölümler
  • Calling All TA Leaders: Success Requires Embedded Science

    Calling All TA Leaders: Success Requires Embedded Science
    15 Oct 2026

    “We need to have rigor adjusted for reality.” — Allie Wehling

    Episode Overview

    I am lucky to be joined by my friend Allie Wehling. I consider Allie a superstar. She has achieved what I call the gold standard for internal I/Os.

    Allie is a rare example of having an I/O helping guide the business of hiring. At Cisco, she owns both the IO content and the end-to-end hiring process. She sees the whole system and how every piece affects the others.

    Most hiring functions treat science as a bolt-on: a test added to a process nobody designed with measurement in mind. That approach has value, but it leaves results to chance. Consistent, defensible hiring requires a holistic approach, where science, process, and business needs are designed together.

    Join us as we dig into what an embedded science lens brings to hiring, and why TA can’t afford to go without it.

    Topics Discussed & Key Insights

    1. TA Leaders: Embed Science, Don’t Bolt It On

    Assessment usually gets added to hiring without anyone thinking about the whole process — piecemeal jamming assessments into legacy tools and processes. Allie’s role is built differently. Her teams cover both what gets measured and how it’s implemented, from requisition through pre-boarding. Having one leader across both is rare, and it gets results.

    “We have the WHAT, the content, the assessments from our COE, and then the HOW. How are we actually implementing it?”

    2. Science Sees the Whole Funnel, Not Just the Test

    The assessment is one input. Every handoff, from assessment to interview to decisioning, introduces the potential for bias AND the potential for ROI. The trick is pulling both levers in the right direction. And we can’t forget that candidate experience is critical. When top candidates drop out because of slow scheduling or untrained interviewers, the process fails, not the test.

    “because you can have the most beautifully designed and validated assessment and still walk away with a bad hire because the assessment is just one input to the much longer process.”

    3. Science Has to Be in Lockstep With the Business

    What’s scientifically best and what’s operationally viable are different questions, and both have to be solved. Process and technology have to be designed together. Allie’s team works “two in a box” with recruiting tech, and Cisco has hired an AI orchestration lead so the tools feel like one system.

    “they’re two different optimization problems. And if we pretend like they’re one, our systems are going to die quiet deaths.”

    4. Science Prepares the Org. for Transformation

    Allie listens for the problems leaders haven’t voiced yet. When executives kept asking how Cisco assesses for AI, her team built a library of work samples to measure it. Bespoke assessments built from validated items give hiring managers a hand in the content. An internal AI tool sets cut scores against predicted pass rates instead of gut thresholds.

    “I try to listen to what problem are they not even voicing yet.”

    5. Science Keeps You Defensible

    Job analysis documents job relatedness. Vendor claims get tested, not trusted. If a vendor can’t explain what its score predicts, or who’s on its science team, it’s not the right partner.

    “They’re predicting something, but is it the thing that we want to predict?”

    Final Takeaway

    Speed, volume, and AI are making hiring harder to get right. Science is what turns a pile of tools into a system that works effectively, consistently and holds up to scrutiny. It’s not a test you add. It’s a lens you build in.



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com
    Daha fazlasını göster Daha Az Göster
    45 dk.
  • You're Not Losing Your Job to AI. You're Handing It Over.

    You're Not Losing Your Job to AI. You're Handing It Over.
    24 Sep 2026
    “If you automate chaos you get chaos at scale.”— Tomas Chamorro-PremuzicEpisode OverviewIn this episode I’m joined for a third time by Tomas Chamorro-Premuzic — organizational psychologist, author, and now the first Chief Science Officer at Russell Reynolds Associates, one of the world’s top executive leadership advisory firms.We set out to role play this conversation as if it were happening in 2027. We didn’t stick to it. We kept sliding between the future, the present, and whatever is right around the corner — which says more about this moment than the role play ever could.We’ll come back in a year to see how we did. Underneath the predictions is one argument: machines can only take what has already been standardized.Topics Discussed & Key Insights1. The Future Is Already Here. It’s Just Not Evenly Distributed.Tomas recently returned from Shenzhen — stores staffed by humanoid robots, selling humanoid robots, with shoppers carrying them out in boxes. No human employees. His point, borrowing William Gibson: “the future is already here, just not evenly distributed.” What looks weird and niche today becomes mainstream fast. That’s the honest baseline for any one-year prediction: 95% of work will look the same next year. The question is which 5% won’t.2. You Can’t Automate What Hasn’t Been StandardizedThe core flip of the episode. Automation doesn’t take jobs — it takes standardized work. As Tomas puts it: “you cannot automate what hasn’t been standardized already.” Which raises his uncomfortable question: are knowledge workers standardizing themselves? Leaning on AI for everything produces what he calls “work slop and cognitive surrender” — the intellectual version of elevator music. Designed to blend in. Designed to be ignored. The more predictable your output, the easier you are to model, and the easier you are to replace.3. Hiring for Potential, Not Track RecordHis first concrete prediction: organizations will hire leaders less for what they have done and more for what they could do. AI enriches the signals needed to model curiosity, humility, coachability, and EQ — the things good assessment science has been chasing for decades. Technical skills, including the AI literacy everyone is worried about, “will be far less important than we think.” Human and humane skills will matter more. Track record measures what’s already been commoditized. Potential is what’s left.4. Autonomous Hiring Splits the MarketI ran a live query on my TA Tech Navigator during the episode. TA Tech Navigator is my online market research portal that contains 540 profiles of candidate evaluation and assessment tech. It has several frameworks that classify AI use and Science and can run trends reports using the aggregate data. The trend it flagged: fully autonomous hiring stacks are moving from fringe to viable. There are now vendors where no human touches the process until the final decision. Employers are splitting into two camps — buying efficiency or buying defensibility. Is the tool validated? Is there something here you can trust? That gap will widen, with regulation as the wildcard. The Navigator’s one-year read: faster, cheaper, more automated — but not fairer, more valid, or more defensible unless somebody demands it.5. The Zombie ProblemsTomas closes with a warning. Obsessing about the future is often an excuse for not dealing with present problems. Two have been with us for decades, and AI doesn’t touch either one: measuring leadership performance objectively, so tools predict actual performance instead of a popularity contest — and our stubborn preference for intuition over validated assessment. The same world that worships data, science, and validity is the world in which the MBTI is the number one personality assessment. New technology doesn’t kill these problems. Only deciding to solve them does.Final TakeawayMachines don’t take work. They take standardized work. The defensible position — for a knowledge worker, a leader, or a hiring process — is to stay hard to predict: keep thinking, keep surprising, and measure the things that actually differentiate people. That’s been the job all along. AI just raised the price of skipping it. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com
    Daha fazlasını göster Daha Az Göster
    47 dk.
  • AI Hiring Law: What Changed While You Weren't Looking?

    AI Hiring Law: What Changed While You Weren't Looking?
    11 Aug 2026
    “The way the law is working — the Workday case is exhibit A here — is that vendors are going to be on the hook if their systems are producing biased outcomes. Period.”— Marc WeinsteinEpisode OverviewIn this episode I’m joined by Marc Weinstein, an attorney with 27+ years of practice focused on organizations that use tests to make high-stakes decisions about people — licensure, certification, admissions, and employment selection. His work centers on the legal rights of people impacted by AI systems and the organizations that use them.The legal ground under AI hiring is moving fast. In the past few months a state AI law got sued into a rewrite, a court handed down a ruling that shields bias audit data from view, and a new lawsuit opened a line of attack nobody was watching. If you buy, sell, or use AI hiring tools, this episode helps you stay on top of the shifting sands.Topics Discussed & Key Insights1. First, how to roll: governance before headlines.Marc’s advice starts above the regulations. Your own governance is the key to successful risk mitigation. Get this part right and the rest gets much easier.* Before you track any law, ask why you want to use AI in the first place. If you have good answers, then ask how* Set principles for your organization — not for the whole world, for your organization. Let them drive your use cases, your vendor selection, and your evaluation criteria* Operate by them for real. A policy that lives on paper is worth nothing* Do this and you’re already in compliance with most of the laws that could touch you. Everything below is what’s shifting underneath that foundation2. Vendor liability is shifting fast — get ready now.For a long time, vendors selling hiring tools got a pass. How the customer used the tool was the customer’s problem. That arrangement is breaking down.* In Mobley v. Workday, the court is letting claims proceed on the theory that the platform acts as an agent of its employer customers — which puts the same federal anti-discrimination laws that apply to employers on the platform* Plaintiffs go where the deep pockets are. Mega vendors like Workday and 8fold are the deep pockets* Employers are still independently on the hook. Vendor liability adds to yours, it doesn’t replace it* Newer legislation points the same direction, and California’s new regulations make bias testing itself evidence in court — more on that below* Since we recorded: the court refused to dismiss most of the remaining claims against Workday. The case keeps moving, and the core theory keeps surviving3. Bias audit data can be withheld — and that says a lot.A discovery ruling in the Workday case protected the company’s bias testing data from the plaintiffs. Worth sitting with what that means.* The court held the data was privileged because lawyers curated it and the testing was done to provide legal advice — not for business use* Publicly advertising that you conduct bias testing did not waive the privilege* So the people alleging harm from the system may never see the data that would show whether the system harmed them* The bigger question: if the most important evidence about how these tools perform can be shielded, what does accountability actually look like? Proof may have to come from other directions4. There are new ways to hold these tools accountable.The Eightfold case shows creative lawyering finding routes into AI hiring that didn’t exist a year ago.* The claim is built on the Fair Credit Reporting Act and privacy law — the argument is that AI-generated candidate scores are consumer reports, compiled without the required disclosures and consent* These theories don’t require proving discrimination as a legal element, which makes them easier cases to bring* The target hasn’t changed. It’s still about how these tools treat people. There are just more ways to get there now5. Colorado is the cautionary tale: get aggressive, get crushed.Colorado passed the most rigorous state AI law in the country — mandatory risk management, impact assessments, a real duty of care. Then it got taken apart.* xAI sued to block the law. The DOJ intervened on xAI’s side — the first time the federal government moved to invalidate a state AI law* The legislature rewrote the law under pressure. The replacement is a notice-and-transparency framework, delayed to January 2027* The lesson for everyone else: states that regulate AI aggressively are going to get sued by the federal government and the private sector. Plan around that reality6. Meanwhile, California quietly became the strictest.While everyone watched Colorado, California put real requirements on the books — and nobody has sued to stop them.* Under the FEHA regulations, conducting a documented bias test supports an employer’s defense in discrimination litigation. Not conducting one supports the plaintiff. Records must be kept four years* The privacy agency’s automated decision-making rules ...
    Daha fazlasını göster Daha Az Göster
    54 dk.
adbl_web_anon_alc_button_suppression_t1
Henüz yorum yok