Beyond ideals: why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it by Liefgreen, A, Weinstein, N, Wachter, S, Mittelstadt, B

“…Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. …”

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

“…Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. …”