The Next Next Common Sense
with the reasonable expectations and appropriate norms of specific con- texts. They distinguish between making work visible for legitimate rec- ognition purposes and monitoring behavior in ways that create control rather than acknowledgment. And they involve those being recognized in determining appropriate visibility boundaries rather than imposing surveillance unilaterally. Microsoft’s productivity analytics offers an instructive example of this balanced approach. Their Viva Insights tool provides individuals with personal productivity data while only sharing aggregated, anonymized information with managers. This design gives individuals useful feedback while preventing the tool from becoming a surveillance mechanism that would inevitably undermine its effectiveness.
Recognition Justice
Recognition systems inevitably create what philosopher Nancy Fraser calls “distribution of the visible”—determining whose contributions receive acknowledgment and whose remain unseen. This distribution has profound implications for organizational justice, power dynamics, and inclusion. Traditional recognition often systematically undervalued certain types of contributions, particularly those performed predominantly by marginalized groups. Emotional labor, behind-the-scenes coordination, conflict prevention, and community maintenance work frequently received less acknowledgment than more visible technical contributions or dra- matic interventions—despite their essential role in organizational success. Digital recognition systems can either perpetuate these patterns or help correct them by making previously undervalued contributions more visible. Organizations committed to recognition justice deliberately ex- amine which types of contribution their systems acknowledge, whose work traditionally receives less visibility, and how recognition practices might create more equitable acknowledgment. Atlassian provides an example of this approach through their “team play- book” framework, which explicitly acknowledges historically undervalued
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