TY - JOUR
T1 - Stabilizing Translucencies
T2 - Governing AI transparency by standardization
AU - Högberg, Charlotte
PY - 2024/2/25
Y1 - 2024/2/25
N2 - Standards are put forward as important means to turn the ideals of ethical and responsible artificial intelligence into practice. One principle targeted for standardization is transparency. This article attends to the tension between standardization and transparency, by combining a theoretical exploration of these concepts with an empirical analysis of standardizations of artificial intelligence transparency. Conceptually, standards are underpinned by goals of stability and solidification, while transparency is considered a flexible see-through quality. In addition, artificial intelligence-technologies are depicted as ‘black boxed’, complex and in flux. Transparency as a solution for ethical artificial intelligence has, however, been problematized. In the empirical sample of standardizations, transparency is largely presented as a static, measurable, and straightforward information transfer, or as a window to artificial intelligence use. The standards are furthermore described as pioneering and able to shape technological futures, while their similarities suggest that artificial intelligence translucencies are already stabilizing into similar arrangements. To rely heavily upon standardization to govern artificial intelligence transparency still risks allocating rule-making to non-democratic processes, and while intended to bring clarity, the standardizations could also create new distributions of uncertainty and accountability. This article stresses the complexity of governing sociotechnical artificial intelligence principles by standardization. Overall, there is a risk that the governance of artificial intelligence is let to be too shaped by technological solutionism, allowing the standardization of social values (or even human rights) to be carried out in the same manner as that of any other technical product or procedure.
AB - Standards are put forward as important means to turn the ideals of ethical and responsible artificial intelligence into practice. One principle targeted for standardization is transparency. This article attends to the tension between standardization and transparency, by combining a theoretical exploration of these concepts with an empirical analysis of standardizations of artificial intelligence transparency. Conceptually, standards are underpinned by goals of stability and solidification, while transparency is considered a flexible see-through quality. In addition, artificial intelligence-technologies are depicted as ‘black boxed’, complex and in flux. Transparency as a solution for ethical artificial intelligence has, however, been problematized. In the empirical sample of standardizations, transparency is largely presented as a static, measurable, and straightforward information transfer, or as a window to artificial intelligence use. The standards are furthermore described as pioneering and able to shape technological futures, while their similarities suggest that artificial intelligence translucencies are already stabilizing into similar arrangements. To rely heavily upon standardization to govern artificial intelligence transparency still risks allocating rule-making to non-democratic processes, and while intended to bring clarity, the standardizations could also create new distributions of uncertainty and accountability. This article stresses the complexity of governing sociotechnical artificial intelligence principles by standardization. Overall, there is a risk that the governance of artificial intelligence is let to be too shaped by technological solutionism, allowing the standardization of social values (or even human rights) to be carried out in the same manner as that of any other technical product or procedure.
KW - Artificial Intelligence
KW - Algorithms
KW - Transparency
KW - Standards
KW - Governance
KW - Uncertainty
KW - Standardization
U2 - 10.1177/20539517241234298
DO - 10.1177/20539517241234298
M3 - Article
SN - 2053-9517
VL - 11
JO - Big Data and Society
JF - Big Data and Society
IS - 1
ER -