TY - GEN
T1 - Impossible Explanations? Beyond explainable AI in the GDPR from a COVID-19 use case scenario
AU - Hamon, Ronan
AU - Junklewitz, Hendrik
AU - Malgieri, Gianclaudio
AU - De Hert, Paul
AU - Beslay, Laurent
AU - Sanchez, Ignacio
PY - 2021/3/3
Y1 - 2021/3/3
N2 - We present a case study of a real-life scenario designed to illustrate the application of an AI-based automated decision making process for the medical diagnosis of COVID-19 patients. The scenario exemplifies the trend in the usage of increasingly complex machine-learning algorithms with growing dimensionality of data and model parameters. Based on this setting, we analyse the challenges of providing human legible explanations in practice and we discuss their legal implications following the General Data Protection Regulation (GDPR).
AB - We present a case study of a real-life scenario designed to illustrate the application of an AI-based automated decision making process for the medical diagnosis of COVID-19 patients. The scenario exemplifies the trend in the usage of increasingly complex machine-learning algorithms with growing dimensionality of data and model parameters. Based on this setting, we analyse the challenges of providing human legible explanations in practice and we discuss their legal implications following the General Data Protection Regulation (GDPR).
UR - http://www.scopus.com/inward/record.url?scp=85102621603&partnerID=8YFLogxK
UR - https://dl.acm.org/doi/10.1145/3442188.3445917#sec-ref
U2 - 10.1145/3442188.3445917
DO - 10.1145/3442188.3445917
M3 - Conference paper
T3 - FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
SP - 549
EP - 559
BT - FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
PB - ACM
T2 - FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and TransparencyMarch 2021
Y2 - 25 March 2021
ER -