This article discusses the development of an ‘accountability bill’ in the US, which aims to punish 
company algorithms that appear to discriminate against people based on age, race, religion, gender, sexual orientation or citizenship status.
Whilst it would be a good law in theory, in reality, it would be very hard to implement. Algorithmic systems are so difficult to break down, designed to be increasingly complex, gathering millions of data points and “woven together with hundreds of other algorithms to create algorithmic systems” (Williamson 2017). Added to that,companies are very secretive about how their models work and what type of parameters make up the design of the algorithms, so that “the rules generated by (algorithms) are compressed and hidden” (Williamson 2017). Discrimination would be very hard to prove as being deliberate in these cases, but hopefully it will encourage scientifically sound data builds, validated in appropriate ways, and to eventually make them more transparent to the public.
Williamson, B. 2017. Introduction: Learning machines, digital data and the future of education (chapter 1). In Big Data and Education: the digital future of learning, policy, and practice