  Today's large-scale algorithmic and automated deployment of decision-making
systems threatens to exclude marginalized communities. Thus, the emergent
danger comes from the effectiveness and the propensity of such systems to
replicate, reinforce, or amplify harmful existing discriminatory acts.
Algorithmic bias exposes a deeply entrenched encoding of a range of unwanted
biases that can have profound real-world effects that manifest in domains from
employment, to housing, to healthcare. The last decade of research and examples
on these effects further underscores the need to examine any claim of a
value-neutral technology. This work examines the intersection of algorithmic
bias in consumer mobile health technologies (mHealth). We include mHealth, a
term used to describe mobile technology and associated sensors to provide
healthcare solutions through patient journeys. We also include mental and
behavioral health (mental and physiological) as part of our study. Furthermore,
we explore to what extent current mechanisms - legal, technical, and or
normative - help mitigate potential risks associated with unwanted bias in
intelligent systems that make up the mHealth domain. We provide additional
guidance on the role and responsibilities technologists and policymakers have
to ensure that such systems empower patients equitably.
