The Transformation of the Human – Rethinking Explainability in Artificial Intelligence - Tui Shaub & Nicole Rigillo

September 19, 2019

2pm Edinburgh

AI systems are increasingly being used to both augment and replace human decision-makers of all kinds: an HR representative screening a job applicant, a judge assessing a prisoner’s bail request, or an immigration agent issuing a visa, for example. Concerns about fairness and transparency in such systems has led to legislation demanding that decision-making AI be explainable, both to users and those ultimately affected by algorithmic decisions. This positions explainability as a crucial – yet often ill-understood – epistemic interface between humans and intelligent machines. Drawing on interviews with members of Element AI’s explainability team, I show how explainability experts frame and encode notions of trust, accountability, and bias as they produce models intended to make an AI system’s decision-making process more legible and actionable to humans. I argue that for AI systems to be ethical and trustworthy, system designers and regulators must better account both for the differences in human and machine forms of intelligence, as well as for the subtle conceptual shifts that accompany the development of AI systems capable of autonomously making decisions about us.

This event is free and open to all. No registration necessary.

Moot Court Room
Old College
South Bridge

composed by Arswain
machine learning consultation by Anna Tskhovrebov
commissioned by the Berggruen Institute
premiered at the Bradbury Building
downtown Los Angeles
april 22, 2022

Human perception of what sounds “beautiful” is necessarily biased and exclusive. If we are to truly expand our hearing apparatus, and thus our notion of beauty, we must not only shed preconceived sonic associations but also invite creative participation from beings non-human and non-living. We must also begin to cede creative control away from ourselves and toward such beings by encouraging them to exercise their own standards of beauty and collaborate with each other.

Movement I: Alarm Call
‘Alarm Call’ is a long-form composition and sound collage that juxtaposes, combines, and manipulates alarm calls from various human, non-human, and non-living beings. Evolutionary biologists understand the alarm call to be an altruistic behavior between species, who, by warning others of danger, place themselves by instinct in a broader system of belonging. The piece poses the question: how might we hear better to broaden and enhance our sense of belonging in the universe? Might we behave more altruistically if we better heed the calls of – and call out to – non-human beings?

Using granular synthesis, biofeedback, and algorithmic modulation, I fold the human alarm call – the siren – into non-human alarm calls, generating novel “inter-being” sonic collaborations with increasing sophistication and complexity. 

Movement II: A.I.-Truism
A synthesizer piece co-written with an AI in the style of Vangelis’s Blade Runner score, to pay homage to the space of the Bradbury Building.

Movement III: Alarmism
A machine learning model “learns” A.I.Truism and recreates Alarm Call, generating an original fusion of the two.

Movement IV: A.I. Call
A machine learning model “learns” Alarm Call and recreates A.I.Truism, generating an original fusion of the two.