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Précis and Abstract for Research Subproject: Ethics of Epistemically Opaque Black Box AI Algorithms.

There are many philosophical views about, and treatments of, artificial intelligence and its ethical problems. Some approaches embody scientistic enthusiasm and anticipate transhuman outcomes. They involve acceptance of premises about such things as extended cognition according to which our cognition is already augmented by such technology as smartphones and The Internet. Normative ethics of information, computing, and AI done with this metaphilosophical background take seriously anticipated problems like the singularity – that point in history at which strong AI gains human level-intelligence and surpasses it. Other theorists are more conservative and cautious. For example, philosopher and cognitive scientist Daniel Dennett has warned that philosophical treatments of the singularity are currently little more than fanciful speculation.

Either way, existing low-grade unsupervised and Bayesian AI black box algorithms in machine learning and deep learning applications - that are far from any realisation of the singularity or human level intelligence - already have significant ethical implications. (The of consciousness of AI is another, albeit related, question altogether, and not necessarily the same as the question of self-awareness). One is that sophisticated deep learning and machine learning training algorithms like those used in marketing, web, scientific, and medical diagnostic systems are already so cumulatively internally complex as to be either effectively, or else actually, epistemically opaque.



By epistemic opacity I mean something subtly different to epistemic inaccessibility. Human computer scientists and programmers can access and analyse the code associated with such systems as they work (using memory snapshots and dumps, and sophisticated development environments which can step through and monitor code execution, and the inputs, outputs, and activity of functions and methods in the program). However, in many cases at certain levels of abstraction there is no way to determine what the logic of the trained system is actually doing, and why. The information is technically epistemically accessible on a causal and interactive basis, but opaque to epistemically useful or explanatory analysis nonetheless.


As recursively self-modifying unsupervised and Bayesian deep learning training algorithms become more sophisticated, and in some cases as existing systems build enormous banks of trained data, this epistemic opacity increases. As unsupervised training algorithms and reinforcement deep learning becomes more recursion-capable facilitating greater self-modification, epistemic opacity further increases. Even without reconfigurable hardware-firmware like field-programmable gate arrays and other more radical wetware-bioware platforms, software alone can prospectively implement the equivalent of new wetware neuron types with completely different functional parameters. Humans, by comparison, can only evolve new neuron types over long preiods, and have no conscious cognitive control of this process. These more speculative concerns aside, existing recursively self modifying black box training algorithms are epistemically opaque. This presents immediate and long term challenges for policy makers and law makers, and in this project I investigate some of the reasons why this is so, and what the specific implications are.

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