[LI] AI, HR, recruitment & the individual

Several items posted on LinkedIn in late summer and early fall 2017 regarding how automation is being used and perceived as developing in hiring and the workplace. Includes one from 5 years ago when the dialogue was more from the perspective of “big data.” Also re-upped current observations about LinkedIn’s attempts to automatically serve suggested positions.

Artificial intelligence to become most important workplace tech trend over next decade,” Mark Eltringham, Insight, 18 August 2017 (posted August 2017)

Interesting article about a Gartner, Inc. report that is too expensive for most of us to buy. It disaggregates what we lump under the heading of “AI,” and considers adoption and impact of specific technologies with reference to the “hype cycle.” It may be useful to click through to the report’s page which includes the table of contents – effectively a list of the technologies discussed.

AI technologies “will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks.”

As AI in its various forms becomes more important in all economic sectors, I would again insist that we: (1) continuously reflect on “whose AI?” meaning who benefits from its increased use; and (2) relentlessly juxtapose deployment of advances in AI in organizations with the largely manual tasks that job applicants to positions in those same organizations are still being asked to do and improve on.

How AI can aid, not replace, humans in recruitment,” Zach Emmanuel, Computer Weekly, August 2017 (posted August 2017)

Interesting overview of artificial intelligence (AI) in service of recruitment in ComputerWeekly.com. However it is also an illustration of how AI technology is being developed for recruitment/HR and for intermediaries (job sites in particular), but not for people involved in searching employment. In other words, in this imbalanced development, job seekers still use late 20th century technology to manually search and apply for employment.

To the extent that is changing, it is still out of the hands of the job seeker – an example from the article is “chatbots to personalise and improve the job-seeking experience for candidates.” How to move beyond such apps that work on behalf of job seekers (but according to someone else’s agenda, however beneficent they may be) to AI working at the behest of job seekers (i.e., tasked by them and under their control)?

Evolv and The Wharton School use ‘big data’ to predict when you will quit your job,” Christina Farr, Venture Beat, 19 November 2012 (posted September 2017)

Another milestone(?) in ongoing development of algorithms for hiring was profiled by Christina Farr in this 2012 Venture Beat piece. A company called Evolv (now part of Cornerstone OnDemand), working with UPenn’s Wharton School, had developed algorithms to predict how long potential employees might stay on the job, based on factors related to attrition rates. This use of algorithms was conceived ostensibly to make for better employer/employee fits, but were clearly intended for use by those hiring. Would be interested to know where this is now in terms of development and use.

The article also mentions then future interest in evaluating the “characteristics of a good supervisor.” A logical follow-on question would be whether job seekers would have access to algorithms analyzing such data on organizations they apply to.

Evolv began working in 2007, the same year Google began filling jobs with algorithms. [see “Early use of algorithms for hiring“]

Other people’s job algorithms – is this a path we want to follow? Don Osborn, LinkedIn, 3 July 2017 (posted October 2017)

LinkedIn’s job page is still up to the “Because you viewed” silliness, so I’ll take the opportunity to re-up this piece from the summer. Other people’s job algorithms still churn on overtime, without any observable qualitative improvement. They get “silly” when they process based on any input as on LinkedIn, or outrageously funny when, for example someone with my background gets an email about a staff psychiatrist opening, as happened today (may be the result of not feeding tireless algorithms?).

Other blogs > LinkedIn > LinkedIn articles & posts, 2017 (Jul–Dec)

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