David Dapaah-Afriyie's Profile

Hi Kendall,

Thank you for your great post and astute identification of trust as the bedrock of people analytics within DEI efforts in particular, and of people analytics in general. In reviewing your post and previous comments, it seems as though essential components of an organization cultivating trust within the workplace—and thereby creating an environment conducive to the implementation of people analytics— are (1) operating with transparency, so that employees understand the goals of the DEI efforts and how the organization intends to achieve said goals, and (2) demonstrating respect for employee input and wellbeing by incorporating the individuals whom the DEI efforts are ostensibly aimed to serve into the effort development process, so that the DEI efforts ultimately realized are indeed catered to the needs and desires of their targets. All the while, in implementing people analytics into DEI efforts, it remains important to avoid overburdening those very same employees and to be sensitive to their data, which leads me to believe that, though potentially counterintuitive or counterproductive, providing an opportunity for employees to opt out of data being used in DEI efforts—in addition to transparency and inclusion—can serve to engender greater trust. An opt-out mechanism recognizes the data autonomy of employees and aids an organization’s ability to assess genuine buy-in for people analytics within DEI.

Hi Elena,

Great title and post! I appreciate you highlighting how, in the absence of a cautious, substance-sensitive approach, relying upon data of past behavior that indicates probabilities—but not certainties—about future behavior, and may be encoded with human bias, might serve to promote confirmation bias and stymie an organization’s ability to envision and pursue novel opportunities for success.

To answer your final question, it seems as though we, as future users of Big Data and AI, can make a better use of these technologies by doing so in a critical, reflective manner that is characterized by an understanding that data interpretation is an inherently subjective process to an extent, so a thoughtful interrogation of the goal(s) and inputs of any given model is necessary. Merely receiving data-produced outcomes and regarding them as wholly objective outputs free of noise or bias runs the risk of perpetuating the perils that you note.

Hi Ellen,

Thank you for writing this eye-opening piece on how the alteration in the USMLE Step 1 feedback that students and residency programs receive might have potential, unintended adverse effects on students and residency programs alike. In consideration of the purported goals of Step 1 being (1) to assess the competency level of medical students early within their education (be it post-completion of the pre-clinical coursework or the clinical core) and (2) to inform the development and refinement of medical education curricula to promote the competency of medical students, and the purported goal of FSMB and NBME’s 2022 feedback change being to improve student wellness by reducing student stress via the elimination of their Step 1 scores relative to their peers, I find your selective score disclosure proposal quite appealing and promising. This entire scenario calls to mind the feedback administration dilemma in the AMD case that we reviewed earlier in the semester. As with the change to a ratings-free program in the AMD case, I wonder how the FSMB and NBME went about contemplating and weighing the tradeoffs of the potential increase in bias and loss of the equitable function that the standardized Step 1 scores fulfilled with the potential gains in student wellness that may be brought about by eliminating Step 1 scores, and to what extent student feedback (like employee feedback in AMD) was involved in this making this change.