In this chapter, I illustrate the impact of Johnson’s scholarship on the study of corporate responsibility. I extend Johnson’s accountability-as-practice to begin to scope (a) the normative grounding for why tech firms are accountable to their stakeholders and (relatedly) (b) what tech firms are accountable for, and (c) to whom firms are accountable. Firms are accountable for their design and deployment decisions about AI because firms have the power to make different decisions to elicit different moral implications in use. Firms are accountable for the decisions they make that impact others—whether those impacts are positive, as when creating value for stakeholders, or negative, as when firms destroy value for other stakeholders. Currently, industry has accountability dissonance where scholars and firms take credit for their ability to design algorithms that create value for key stakeholders while simultaneously shying away from the negative consequences, rules being broken, value being destroyed, or rights being diminished for those same decisions. I use online platforms to illustrate the importance of Johnson’s approach to algorithmic accountability. In each case, firms have been slow to embrace accountability in the moral implications of their decisions; and attribution is made more complicated with the use of AI on a platform.

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Johnson’s Algorithmic Accountability and Corporate Accountability Dissonance

  • Kirsten Martin

摘要

In this chapter, I illustrate the impact of Johnson’s scholarship on the study of corporate responsibility. I extend Johnson’s accountability-as-practice to begin to scope (a) the normative grounding for why tech firms are accountable to their stakeholders and (relatedly) (b) what tech firms are accountable for, and (c) to whom firms are accountable. Firms are accountable for their design and deployment decisions about AI because firms have the power to make different decisions to elicit different moral implications in use. Firms are accountable for the decisions they make that impact others—whether those impacts are positive, as when creating value for stakeholders, or negative, as when firms destroy value for other stakeholders. Currently, industry has accountability dissonance where scholars and firms take credit for their ability to design algorithms that create value for key stakeholders while simultaneously shying away from the negative consequences, rules being broken, value being destroyed, or rights being diminished for those same decisions. I use online platforms to illustrate the importance of Johnson’s approach to algorithmic accountability. In each case, firms have been slow to embrace accountability in the moral implications of their decisions; and attribution is made more complicated with the use of AI on a platform.