  Reconfigurable intelligent surfaces (RIS) can be crucial in next-generation
communication systems. However, designing the {RIS} phases according to the
instantaneous channel state information (CSI) can be challenging in practice
due to the short coherent time of the channel. In this regard, we propose a
novel algorithm based on the channel statistics of massive multiple input
multiple output systems rather than the instantaneous {CSI}. The beamforming at
the base station (BS), power allocation of the users, and phase shifts at the
RIS elements are optimized to maximize the minimum signal-to-interference and
noise ratio (SINR), guaranteeing fair operation among various users. In
particular, we design the RIS phases by leveraging the asymptotic deterministic
equivalent of the minimum {SINR} that depends only on the channel statistics.
This significantly reduces the computational complexity and the amount of
controlling data between the {BS} and {RIS} for updating the phases. This setup
is also useful for electromagnetic fields (EMF)-aware systems with constraints
on the maximum user's exposure to EMF. The numerical results show that the
proposed algorithms achieve more than $100 \%$ gain in terms of minimum SINR,
compared to a system with random RIS phase shifts, when $40$ RIS elements, $20$
antennas at the BS and $10$ users, are considered.
