FHWien der WKW – Die Klimakrise und nicht zuletzt die Covid-19 Pandemie unterstreichen die Dringlichkeit für eine Transformation der sozioökonomischen Rahmenbedingungen unseres gesellschaftlichen Lebens. Mit der Vorlesungsreihe „Responsible Management Lectures“ möchten wir einen interdisziplinären Diskurs über die Zukunft unseres Wirtschaftssystems anstoßen mit einem besonderen Fokus auf wirtschafts- und unternehmensethische Aspekte. Seit dem Wintersemester 2012 organisieren wir regelmäßig eine Vorlesungsreihe zu allgemeinen wirtschaftsethischen und nachhaltigkeitsbezogenen Themen. Für das Wintersemester 2021 wählten wir Corporate Responsibility in the Digital Age als übergreifendes Thema für die Vorlesungsreihe.
Es freut uns sehr, dass Frau Prof. Dr. Kirsten Martin (https://mendoza.nd.edu/mendoza-directory/profile/kristen-martin/), William P. and Hazel B. White Center Professor of Technology Ethics | Director, ND Technology Ethics Center (ND TEC) | Professor of IT, Analytics, and Operations, University of Notre Dame, einen Vortrag, in englischer Sprache, hält.
Titel
Bias and Corporate Responsibility: How Companies Hide Behind The False Veil of The Technological Imperative
Abstract
In this chapter, I explore the importance in acknowledging that algorithms have biases, value-laden design features with more implications in use – and that companies encode those biases in the design and development of AI. Acknowledging the value-laden biases of algorithms as inscribed in design allows us to identify the associated responsibility of corporations that design, develop, and deploy algorithms. Put another way, claiming algorithms are neutral or that the design decisions of computer scientists are neutral obscures the morally important decisions of computer and data scientists. I also examine the implications of making technological imperative arguments – framing algorithms as evolving under their own inertia, providing more efficient, accurate decisions, and outside the realm of any critical examination or moral evaluation.
I argue specifically that judging AI on efficiency and pretending algorithms are inscrutable produces a veil of the technological imperative which shields corporations from being held accountable for the value-laden decisions made in the design, development and deployment of algorithms. While there is always more to be researched and understood, we know quite a lot about testing algorithms. I then outline how the development of algorithms should be critically examined to elucidate the value-laden biases encoded in design and development.
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