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

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

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

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

How fair can we go in machine learning? Assessing the boundaries of accuracy and fairness by 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 by 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 by Valdivia, A., Martínez-Cámara, E., Chaturvedi, Iti, Luzón, M. V., Cambria, Erik, Ong, Yew-Soon, Herrera, F.

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Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis by 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