| The Truth about Open Offices | Werner Herzog | Bias, Bias, Bias in The Workplace, including ‘Lookism’| It Pays To Be Smart |
|Nov 2|| 1|
Beacon NY | 2019–11–01 | A wave of stories about bias in the workplace: how attractive people benefit from ‘lookism’, how we should dissect the constituent elements building to systemic bias at the atomic level, why supposed efforts to counter gender inequality in tech just isn’t working, and how we might employ AI to sidestep human blinders.
It seems we need to move to a war footing against bias in the workplace. I think Herzog’s quote below is apt.
In The Truth About Open Offices, Ethan Bernstein and Ben Waber jab a finger in the eye of today’s prejudices about the workplace. Writing in a strongly admonitory tone — with a strong undertone of disdain for the unexamined premise that there is a single, best form of workplace — the authors advocate extensive research and experimentation around what they call the ‘anatomy of collaboration’:
Workers are surrounded by a physical architecture: individual offices, cubicles, or open seating; a single floor, multiple floors, or multiple buildings; a dedicated space for the organization, a space shared with other companies, or a home office. That physical architecture is paired with a digital architecture: email, enterprise social media, mobile messaging, and so forth.
But although knowledge workers are influenced by this architecture, they decide, individually and collectively, when to interact. Even in open spaces with colleagues in close proximity, people who want to eschew interactions have an amazing capacity to do so. They avoid eye contact, discover an immediate need to use the bathroom or take a walk, or become so engrossed in their tasks that they are selectively deaf (perhaps with the help of headphones). Ironically, the proliferation of ways to interact makes it easier not to respond: For example, workers can simply ignore a digital message.
When employees do want to interact, they choose the channel: face-to-face, video conference, phone, social media, email, messaging, and so on. Someone initiating an exchange decides how long it should last and whether it should be synchronous (a meeting or a huddle) or asynchronous (a message or a post). The recipient of, say, an email, a Slack message, or a text decides whether to respond immediately, down the road, or never. These individual behaviors together make up an anatomy of collaboration similar to an anthill or a beehive. It is generated organically as people work and is shaped by the beliefs, assumptions, values, and ways of thinking that define the organization’s culture.
The authors explore case studies where companies found that open office plans led to decreases in productivity, examples where decreasing interaction between different functional teams — in one case by moving people to other buildings — led to beneficial results.
The key takeaway from this — which I recommend you read in its entirety — is that companies have to decide how to measure what behaviors and outcomes they want, and experiment with office architecture and interaction patterns to gain them. You can’t simply adopt what WeWork office designers give you and expect to operate at some nebulous peak of efficiency.
Quote of the Day
Thwart institutional cowardice.
Werner Herzog, A Guide for the Perplexed: Conversations with Paul Cronin
Using AI to Eliminate Bias from Hiring | Frida Polli lays out the bind confronting business: for every open job around 250 people apply. HR starts by reducing the 250 to a manageable number of candidates, based on heuristics like college background, employee referrals, and if the candidates work for competitors. This leads to lack of diversity since these factors are in effect filtering out candidates with diverse backgrounds. Consider this thought:
AI can assess the entire pipeline of candidates rather than forcing time-constrained humans to implement biased processes to shrink the pipeline from the start. Only by using a truly automated top-of-funnel process can we eliminate the bias due to shrinking the initial pipeline so the capacity of the manual recruiter can handle it. It is shocking that companies today unabashedly admit how only a small portion of the millions of applicants who apply are ever reviewed. Technologists and lawmakers should work together to create tools and policies that make it both possible and mandatory for the entire pipeline to be reviewed.
The longer article is good, too.
10 Ways to Mitigate Bias in Your Company’s Decision Making | Elizabeth C. Tippett applies what has been learned in how to counter school discipline bias to the context of employee experience. Here’s just one example:
Work backwards from pay, promotion, and performance criteria. If you already have well-defined criteria, consult with managers to break them into subparts. What are the steps an employee needs to complete to achieve those objectives? What skills, knowledge, and experience do they need? Then figure out which components are most important, and whether all employees have equal access.
In Why Tech’s Approach to Fixing Its Gender Inequality Isn’t Working, Alison Wynn seems to reflect Polli and Tippett’s points:
Past research shows that organizations contribute to inequality in varied ways: through referral hiring that leads to narrow pipelines of candidates from similar backgrounds; through subjective evaluation criteria that open the door to bias during performance evaluations; and through a lack of transparency and accountability in pay decisions that leads to unfairness in who gets rewarded.
My work suggests that if tech companies want to attract and retain women, they can’t place the blame on individuals — they need to recognize the role their policies and culture play in causing inequality, and they need to pursue organizational change. Implementing broader recruiting strategies, specific and measurable performance evaluation criteria, and transparent procedures for assigning compensation will go a long way toward reducing gender inequality in tech.
And use AI in hiring.
Attractive People Get Unfair Advantages at Work. AI Can Help. | Tomas Chamorro-Premuzic on a specific sort of bias benefiting attractive people, or ‘lookism’.
It Pays to Be Smart | David Rotman points out that ‘superstar companies are dominating the economy by exploiting a growing gap in digital competencies’.
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