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    Entries in HR Tech (237)


    HRE Column: LinkedIn One Year Later

    Once again, I offer my semi-frequent reminder and pointer for blog readers that I also write a monthly column at Human Resource Executive Online called Inside HR Tech that can be found here.

    This month, I take a look back at the Microsoft acquisition of LinkedIn which (although it seems like a lot longer), only closed officially about this time last year. It has been a pretty interesting, innovative, and fascinating year for the largest professional social network. Since LinkedIn is such an important and influential technology for organizations and individual professionals alike, it seemed like a good time to reflect back on the year and to speculate a bit on what might lie ahead.

    In the HRE Column, I dig a little bit into some of LinkedIn's recent product announcements, look at how the Microsoft angle is beginning to play out and how LinkedIn could evolve moving forward. I hope to have some execs from LinkedIn on an upcoming HR Happy Hour Show totalk about some of these ideas in more depth.

    Having said that, here's a taste of the HRE piece titled 'Betting on LinkedIn'

    I recently was invited to attend a quarterly product update from the folks at LinkedIn Talent Solutions, an online event where the product and marketing teams provide demonstrations and details about new product initiatives and capabilities that are (or are about to be) released. I get these kinds of invites from solution providers quite often, and admittedly do not usually attend -- either I am busy planning the annual HR Tech Conference or I simply don't get all that excited by incremental updates to existing platforms or solutions.

    But I made an exception in this particular case and watched this most recent LinkedIn update. The reasons why were twofold: I had some extra time; and I was interested in one particular update that LinkedIn planned to share information regarding the integration of LinkedIn information with Microsoft Word in the context of a user creating a resume.

    And, since Microsoft finished its $26.2-billion acquisition of LinkedIn about a year ago now, I figured it was an appropriate time to reflect on that industry development, as well as some new capabilities being added to the platform, the challenges the company faces, and what might be coming next.

    On its latest product update webcast, LinkedIn showcased two new initiatives that reflect its continued need to provide value to two distinct constituencies: HR and talent-acquisition professionals; and its rank-and-file members. Each obviously have very different needs and goals.

    The first enhancement for organizational users of its Talent Solutions products was a new performance summary report, which provides them with a simple but comprehensive overview of organizational activity and results on the platform. On one dashboard, HR and talent management professionals can see data such as the number of hires who were "influenced" by candidates viewing company profiles and content on LinkedIn prior to being hired; the effectiveness and response rates of candidate outreach; and most interestingly to me, the top five companies that organizations are losing and winning talent I can recall working at an organization where we were suddenly losing lots of talented sales reps over a short period of time, and had to scramble (and pull up lots of individual LinkedIn profiles) to figure out which competitors were poaching them. We would have loved to have had this information in one place.

    The other new capability -- and probably the more innovative development -- was the announcement of a deeper integration of LinkedIn data with Microsoft Word. For users drafting a resume in Word, information from other LinkedIn profiles is used to help craft a resume. This Resume Assistant asks them to provide a job role of interest and then surfaces examples from LinkedIn of typical work-experience summaries and skills descriptors

    Read the rest at HR Executive Online...

    If you liked the piece you can sign up over at HRE to get the Inside HR Tech Column emailed to you each month. There is no cost to subscribe, in fact, I may even come over and re-surface your driveway, take your dog for a walk, rake up your leaves, and eat your leftover pumpkin pie.

    Have a great day and Happy Day Before Thanksgiving!


    Job Titles of the Future: Man-Machine Teaming Manager

    It's been ages since I have had a new entry in the extremely popular 'Job Titles of the Future' series, but over the weekend I came across an interesting report from tech consultancy Cognizant titled '21 Jobs of the Future: A Guide to Getting - and Staying - Employed Over the Next 10 Years'that more or less has the next 21 posts in this series all in one report. With so much interesting source material (thanks Cognizant!), I had to bust out a new post for the series.

    Then entire report is really interesting, and I imagine I am going to re-visit it again for future installments, but I thought today I would call out one really interesting future job from the list of 21 - a job that I can see playing a large role in the future of work and too, the future of HR.

    The job title of interest is 'Man-Machine Teaming Manager' and I will share some details from the 'job description' for this theoretical role as laid out by our pals at Cognizant.

    The key task for this role is developing an interaction system through which humans and machines mutually communicate their capabilities, goals and intentions, and devising a task planning system for human-machine collaboration. The end goal is to create augmented hybrid teams that generate better business outcomes through human-machine collaboration.

    As a man-machine teaming manager, you will identify tasks, processes, systems and experiences that can be upgraded by newly available technologies and imagine new approaches, skills, interactions and constructs. You will define roles and responsibilities and set the rules for how machines and workers should coordinate to accomplish a task. This involves designing flexible experiences that meet workers’ expectations, while providing a simple and intuitive interaction with machines (translating consumer behavior to business users, as well as to machines, for instance). Ideal candidates will be passionate about advancing human-robot cooperation strategies in a dynamic business environment.

    Lots of the more enlightened 'robots are taking away the jobs' commentary and predictions have arrived at a similar conclusion, that the future of work will be much more about people and robots/machines/algorithms working together, with each contributing their unique and hard to copy strengths. If you did in to the job responsibilities for the Man-Machine Teaming Manager role, (and kudos to Cognizant for writing this report in the form of a bunch of new-age job adverts), the first one talks about the manager needing to identify and describe the business functions and capabilities that are uniquely possessed by people and the ones that would be better performed by machines.

    It seems to me, if you took this conceptual job, and instead of 'people' and 'machines' being the groups that the manager had to better combine as teams and collaborators, and just described it in today's terms of cross-functional teams of people, then in many ways you would be describing the role of an HR leader or Chief Talent Officer.

    Figuring out strengths, capabilities, gaps, and the best ways for diverse groups of talent to combine and connect and collaborate in order to achieve desired business outcomes seems to be one of the most important roles in any organization, and one that should be owned and championed by HR and Talent leaders. So if the Cognizant report is right, and I have no reason to nay say it, then in the near future more of the talent and the collaborators will be some form of technology or robots or algorithms.

    That doesn't change the essential need, purpose, and importance of the role - organizations need leaders that can assess, understand, support, and put in place systems and processes that enable all the talent in the organization to work together to produce the best possible outcomes.

    Hopefully, that role will be filled by people for some time to come.

    Hopefully, they will be HR people.

    Have a great week!


    PODCAST: #HRHappyHour 302 - Tim Sackett and Talent Acquisition Technology

    HR Happy Hour 302 - Tim Sackett and Talent Acquisition Technology

    Host: Steve Boese

    Guest: Tim Sackett

    Listen to the show HERE

    This week on the HR Happy Hour Show, Steve is joined by Tim Sackett, President of HRU Technical Resources, popular writer and speaker on all things Talent Acquisition and keynote speaker at the upcoming Recruiting Trends and Talent Tech Conference.

    On this show, Tim shared his perspectives on how technology continues to change the Talent Acquisition function, how roles for sourcers and recruiters are impacted, and some of the keys for corporate talent acquisition leaders to make the most of their technology investments.

    Additionally, Tim talked previewed his upcoming keynote at the Recruiting Trends and Talent Tech Conference, (November 28 - 30, 2017 in Palm Beach, Florida), how to balance the 'people' side of recruiting with the technology, as well as the one single area of Talent Acquisition Technology that Tim thinks more corporate leaders should be investing in today.

    Finally, we talked about innovation across the board in HR and Talent Tech, the role of technology in candidate experience, and Steve teased his NBA podcast, tentatively titled 'Bounding and Astounding'.

    You can listen to the show on the show page HERE or by using the widget player below:

    This was a really fun show with a long time friend of the HR Happy Hour Show - thanks Tim for taking the time and we hope to see lots of HR Happy Hour listeners at the Recruiting Trends and Talent Tech Conference later this month.

    Reminder: HR Happy Hour listener survey here.

    Thanks to show sponsor Virgin Pulse - www.virginpulse.com.

    And finally, subscribe to the HR Happy Hour on Apple Podcasts, Stitcher Radio, or wherever you get your podcasts.


    Looking for bias in black-box AI models

    What do you do when you can't sleep?

    Sometimes I watch replays of NBA games, (how about my Knicks?), and sometimes I read papers and articles that I had been meaning to get to, but for one reason or another hadn't made the time.

    That is how I spent an hour or so with 'Detecting Bias in Black-Box Models Using Transparent Model Distillation', a recently published paper by researchers at Cornell, Microsoft, and Airbnb. I know, not exactly 'light' reading.

    Full disclosure, I don't profess to have understood all the details and complexity of the study and research methods, but the basic premise of the research, and the problem that the researchers are looking to find a way to solve is one I do understand, and one that you should too as you think about incorporating AI technologies into workplace processes and decision support/making.

    Namely, that AI technology can only be as good and as accurate as the data it’s trained on, and in many cases we end up incorporating our human biases into algorithms that have the potential to make a huge impact on people’s lives - like decisions about whom to hire and promote and reward.

    In the paper, the researchers created models that mimic the ones used by some companies that created 'risk scores', the kinds of data that are used by a bank to decide whether or not to give someone a loan, or for a judicial administration to decide whether or not to give someone early parole. This first set of models is similar to the ones that these companies use themselves.

    Then the researchers create a second, transparent, model that is trained on the actual outcomes that the first set of models are designed to predict - whether or not the loans were paid back and whether or not the parolee committed another crime. Importantly, these models did include data points that most of us, especially in HR, are trained to ignore - things like gender, race, and age. The researchers do this intentionally, and rather than me try to explain why that is important, read through this section of the paper where they discuss the need to assess these kinds of 'off-limits' data elements, (emphasis mine):

    Sometimes we are interested in detecting bias on variables that have intentionally been excluded from the black-box model. For example, a model trained for recidivism prediction or credit scoring is probably not allowed to use race as an input to prevent the model from learning to be racially biased. Unfortunately, excluding a variable like race from the inputs does not prevent the model from learning to be biased. Racial bias in a data set is likely to be in the outcomes — the targets used for learning; removing the race input race variable does not remove the bias from the targets. If race was uncorrelated with all other variables (and combinations of variables) provided to the model as inputs, then removing the race variable would prevent the model from learning to be biased because it would not have any input variables on which to model the bias. Unfortunately, in any large, real-world data set, there is massive correlation among the high-dimensional input variables, and a model trained to predict recidivism or credit risk will learn be biased from the correlation between other input variables that must remain in the model (e.g., income, education, employment) and the excluded race variable because these other correlated variables enable the model to more accurately predict the (biased) outcome, recidivism or credit risk. Unfortunately, removing a variable like race or gender does not prevent a model from learning to be biased. Instead, removing protected variables like race or gender make it harder to detect how the model is biased because the bias is now spread in a complex way among all of the correlated variables, and also makes correcting the bias more difficult because the bias is now spread in a complex way through the model instead of being localized to the protected race or gender variables. ŒThe main benefi€t of removing a protected variable like race or gender from the input of a machine learning model is that it allows the group deploying the model to claim (incorrectly) that they model is not biased because it did not use the protected variable.

    This is really interesting, if counter-intuitive to how most of us, (me for sure), would think about how to ensure that AI and algorithms that we want to deploy to evaluate data sets for a process meant to provide decision support for the 'Who should we interview for our software engineer opening? question.

    I'm sure we've seen or heard about AI for HR solutions that profess to eliminate biases like the ones that have existed around gender, race, and even age from important HR processes by 'hiding' or removing the indicators of such protected and/or under-represented groups.

    This study suggests that removing those indicators from the process and the design of the AI is exactly the wrong approach - and that large data sets and the AI itself can and will 'learn' to be biases anyway.

    Really powerful and interesting stuff for sure.

    As I said, I don't profess to get all the details of this research but I do know this. If I were evaluating an AI for HR tool for something like hiring decision support, I probably would ask these questions of a potential provider:

    1. Do you include indicators of a candidate's race, gender, age, etc. in the AI/algorithms that you apply in order to produce your recommendations?

    If their answer is 'No we don't include those indicators.'

    2. Then, are you sure that your AI/algorithms aren't learning how to figure them out anyway, i.e., are still potentially biased against under-represented or protected groups?

    Important questions to ask, I think.

    Back to the study, (in case you don't slog all the way through it). The researchers did conclude that for both large AI tools they examined, (loan approvals and parole approvals), the existing models did still exhibit biases that they professed to have 'engineered' away. And chances are had the researchers trained their sights on one of the HR processes that AI is being deployed in, they would have found the same thing.

    Have a great day!


    CHART OF THE DAY: Reminding you that China is really, really big

    Regular readers of the blog will remember that I've been fortunate enough to be a part of the first two HR Technology - China Conferences in 2016 and earlier this year. Both times visiting China, learning more about the HR and the HR technology ecosystems there, and meeting some truly engaged HR leaders, I have left more and more impressed and in a way, awed by the size, scale, growth, and innovation of HR and HR tech in that country.

    I look forward to going back in 2018 for sure and in the meantime, I am a member, (the only non-Chinese member I think), of a 30-person strong group chat on WeChat titled 'AI in HR', where HR folks I met in China share information and discuss innovation in HR and HR tech. It is really cook, even if I can only successfully translate about half of it. Get on that, WeChat.

    So I'm a mark for interesting information and additional insight about China and when I saw the below chart/infographic, wanted to share on the blog as a reminder for those of us that sometimes forget, or just never think about, the scale and size (and opportunity), the growth opportunities for businesses of all kinds that China presents.

    So here's the chart, courtesy of Visual Capitalist, then a comment or two from me after the data. Email and RSS subscribers may need to click through to see the chart.

    Pretty amazing, right? That many 'mega-cities' that rival many medium to large countries in terms of the size of their economies.

    A couple of things struck me. One was kind of personal in that the first HR Tech China Conference was held in city called Zhuhai, which, (it seemed to us), was a really large, growing, busy, and important city in Southern China, strategically positioned between Kong Kong and Macau. That city, Zhuhai, does not even crack the Top 30 in terms of economy size in China. Amazing.

    And last, taking a closer look at the map in China, and thinking about these different cities and regions and how they are different, i.e. some still focusing on manufacturing while others are financial centers or hubs for innovative new tech (like AI), reminds me that it is really, really hard to get to 'know' China from just taking a few business trips or attending an event or two. Spending four days in Beijing and thinking you 'get' China would be like taking a long weekend in New York City and concluding that you 'get' America.

    Anyway, file today's post under my philosophy for the blog since 2008 - 'It's interesting to me, so I'm blogging about it'. Your mileage may vary.

    Happy Tuesday.