Michael Lissack
what gets valued, who receives visibility, and how contribution is under- stood. This normative power brings significant ethical responsibilities. Several ethical dimensions require particular attention:
Algorithmic Bias
Digital recognition systems often incorporate algorithms that in- fluence who receives acknowledgment and for what. These algorithms inevitably reflect the values, assumptions, and perspectives of their cre- ators—potentially perpetuating existing biases or creating new forms of systematic disadvantage. For instance, performance recognition systems that heavily weight uninterrupted work patterns might systematically disadvantage caregiv- ers with family responsibilities. Collaboration metrics based on meeting participation might undervalue team members from cultures where verbal participation patterns differ. And customer satisfaction algorithms might reflect societal biases about which voices sound “authoritative” or “helpful.” Organizations at the forefront of ethical recognition practices con- duct regular algorithmic audits to identify potential biases, involve diverse stakeholders in system design, and maintain human oversight of algorith- mic recommendations. They recognize that seemingly “objective” metrics inevitably incorporate subjective judgments that require ongoing ethical examination.
Privacy and Surveillance
Digital recognition systems inevitably involve monitoring activity to determine what deserves acknowledgment. This monitoring can easily cross from appropriate visibility into problematic surveillance that dam- ages psychological safety, undermines autonomy, and creates counterpro- ductive behavioral adaptations. Organizations navigating this boundary successfully create what pri- vacy scholars call “contextual integrity”—information practices aligned
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