The supply chain capitalism of AI: a call to (re)think algorithmic harms and resistance por Valdivia, A

Datafication genealogies beyond algorithmic fairness: making up racialised subjects por Valdivia, A, Tazzioli, M

There is an elephant in the room: towards a critique on the use of fairness in biometrics por Valdivia, A, Serrajòrdia, JC, Swianiewicz, A

Neither opaque nor transparent: a transdisciplinary methodology to investigate datafication at the EU borders por Valdivia, A, Aradau, C, Blanke, T, Perret, S

How fair can we go in machine learning? Assessing the boundaries of accuracy and fairness por Valdivia, A, Sanchez-Monedero, J, Casillas, J

Judging the algorithm: algorithmic accountability on the risk assessment tool for intimate partner violence in the Basque Country por Valdivia, A, Hyde-Vaamonde, C, García-Marcos, J

What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules por Valdivia, A., Martínez-Cámara, E., Chaturvedi, Iti, Luzón, M. V., Cambria, Erik, Ong, Yew-Soon, Herrera, F.

Ligazón do recurso

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis por Schweinsberg, M, Feldman, M, Staub, N, van den Akker, OR, van Aert, RCM, van Assen, MALM, Liu, Y, Althoff, T, Heer, J, Kale, A, Mohamed, Z, Amireh, H, Prasad, VV, Bernstein, A, Robinson, E, Snellman, K, Sommer, SA, Otner, SMG, Robinson, D, Madan, N, Silberzahn, R, Goldstein, P, Tierney, W, Murase, T, Mandl, B, Viganola, D, Strobl, C, Schaumans, CBC, Kelchtermans, S, Naseeb, C, Garrison, SM, Yarkoni, T, Chan, CSR, Adie, P, Alaburda, P, Albers, C, Alspaugh, S, Alstott, J, Nelson, AA, de la Rubia, EA, Arzi, A, Bahnik, S, Baik, J, Balling, LW, Banker, S, Baranger, DAA, Barr, DJ, Barros-Rivera, B, Bauer, M, Blaise, E, Jong, J, Nolan, R, Valdivia, A