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Wireless traffic is exploding, due to the myriad of new connections and the exchange of capillary data at the edge of the networks to operate real-time processing and decision making. The latter especially affects the uplink traffic, which will grow in 6G and beyond networks, calling for new optimization metrics that include energy, service delay, and electromagnetic field (EMF) exposure (EMFE). To this end, reconfigurable intelligent surfaces (RISs) represent a promising solution to mitigate the EMFE, thanks to their ability of shaping and manipulating the impinging electromagnetic waves. In line with this vision, this paper proposes an online adaptive method to mitigate the EMFE under end-to-end delay constraints of a computation offloading service, in the context of RIS and multi-access edge computing (MEC)-aided wireless networks. The goal is to minimize the long-term average of the EMF human exposure under such constraints, investigating the advantages of RISs towards blue (i.e. low EMFE) communications. A multiple-input multiple-output (MIMO) system is investigated as part of the visions towards 6G. Focusing on a typical scenario of computation offloading, the method jointly and adaptively optimizes user precoding, transmit power, RIS reflectivity parameters, and receiver combiner, with theoretical guarantees on the desired long-term performance. Besides the theoretical results, numerical simulations assess the performance of the proposed algorithm, when exploiting accurate antenna patterns, thus showing the advantage of the RIS and that of our method, compared to benchmark solutions.