1. Unconscious bias → Biased algorithms → Less hiring diversity On Science Friday (@SciFri), experts pointed out unintended consequences in algorithms for hiring. But even better was the discussion with the caller from Google, who wrote an algorithm predicting tech employee performance and seemed to be relying on unvalidated, self-reported variables. Talk about reinforcing unconscious bias. He seemed sadly unaware of the irony of the situation.

2. Business theory → Narrow definitions → Subtle distinctions If Uber isn't disruptive, then what is? Clayton Christensen (@claychristensen) has chronicled important concepts about business innovation. But now his definition of ‘disruptive innovation’ tells us Uber isn't disruptive – something about entrants and incumbents, and there are charts. Do these distinctions matter? Plus, ever try to get a cab in SF circa 1999? Yet this new HBR article claims Uber didn't "primarily target nonconsumers — people who found the existing alternatives so expensive or inconvenient that they took public transit or drove themselves instead: Uber was launched in San Francisco (a well-served taxi market)".

3. Meta evidence → Research quality → Lower health cost The fantastic Evidence Live conference posted a call for abstracts. Be sure to follow the @EvidenceLive happenings at Oxford University, June 2016. Speakers include luminaries in the movement for better meta research.

4. Mythbusting → Evidence-based HR → People performance The UK group Science for Work is helping organizations gather evidence for HR mythbusting (@ScienceForWork).

5. Misunderstanding behavior → Misguided mandates → Food label fail Aaron E. Carroll (@aaronecarroll), the Incidental Economist, explains on NYTimes Upshot why U.S. requirements for menu labeling don't change consumer behavior.

*** Tracy Altman will be speaking on writing about data at the HEOR and Market Access workshop March 17-18 in Philadelphia. ***

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