Monthly Archives: October 2017

Mapping the Axis of Disability onto the Axis of Race: Can We Reclaim the Possibilities?

Kimani Paul-Emile, Blackness as Disability, 106 Geo. L. J (forthcoming, 2017).

This is how a myth becomes reality: how contingent social choices and practices can create the disabled subject.1

What counts as healthy and what counts as disability are not necessarily biologically determined, but rather can be socially constructed. Kimani Paul-Emile’s forthcoming paper, Blackness as Disability, calls our attention to this truth, and does so in a way that shows how our chosen constructions could not have higher stakes for any given individual, or for the fate of our collective life.

Any definition of health, even the most biomedical, depends on a conception of “normal” functioning, as I have written of before. Wendy Parmet puts it thus: “[T]he questions of whether the capacity to stay focused in a classroom or to see well at night are [part of normal functioning depend] . . . on what is expected in a given society of people and their interactions with their environment.” Continue reading "Mapping the Axis of Disability onto the Axis of Race: Can We Reclaim the Possibilities?"

The Answer to the Machine is in the Rule of Law?

Mirielle Hildebrandt, Law as Computation in the Era of Artificial Legal Intelligence. Speaking Law to the Power of StatisticsU. Toronto L. J. (2017), available at SSRN.

Mireille Hildebrandt’s forthcoming article is a companion piece to her Chorley Lecture of 2015.1 In the earlier piece, she highlights the relationship between the ‘deep structure of modern law’ and the printing press and written text – building on this a case concerning constitutional democracy and transparency, both in the world of print and the world of electronic data. In this new paper, the emphasis is on law as computation – as compared with law as information in the earlier lecture.

Machine learning is often discussed as an opportunity for legal practice and adjudication, but what will that mean? Hildebrandt highlights how machine learning in the context of law is primarily a simulation of human reasoning found in written legal text; one needs to identify how law is associated with ‘meaningful information’ rather than information simpliciter. Key concerns with applying machine learning in law include the catch-22 of deskilled lawyers becoming unable to verify a machine’s output, and various ways in which such systems can be opaque. Continue reading "The Answer to the Machine is in the Rule of Law?"