  Advances in algorithmic fairness have largely omitted sexual orientation and
gender identity. We explore queer concerns in privacy, censorship, language,
online safety, health, and employment to study the positive and negative
effects of artificial intelligence on queer communities. These issues
underscore the need for new directions in fairness research that take into
account a multiplicity of considerations, from privacy preservation, context
sensitivity and process fairness, to an awareness of sociotechnical impact and
the increasingly important role of inclusive and participatory research
processes. Most current approaches for algorithmic fairness assume that the
target characteristics for fairness--frequently, race and legal gender--can be
observed or recorded. Sexual orientation and gender identity are prototypical
instances of unobserved characteristics, which are frequently missing, unknown
or fundamentally unmeasurable. This paper highlights the importance of
developing new approaches for algorithmic fairness that break away from the
prevailing assumption of observed characteristics.
