Next week I’m presenting a workshop for Code4Lib Edmonton, An Introduction to Machine Learning using Ruby, and I made sure to include a section on AI ethics, which is going to combine some of the lessons from Safiya Noble’s work, as well as some of the criticisms of analytics and tracking in academic libraries. However, because I’ve currently been reading around in the philosophy of science - particularly Roy Bhaskar’s critical realism - I wanted to include something around the ways in which empiricism (specifically positivism) informs some of the ethical mistakes common to AI (and technology more generally).

In his first two books, A Realist Theory of Science (1975) and The Possibility of Naturalism (1979), Bhaskar critiques dominant positivist conceptions of science, which he believes are inadequate to support the legitimacy of scientific knowledge (i.e, unable to give us confidence in scientific knowledge itself). Classical and contemporary empiricism is based - often implicitly - on a concept of causality derived from Hume, in which causality is expressed and proved when a cause and an effect always occur together and in the same sequence. Bhaskar takes issue with this idea, first because outside the “closed systems” of controlled experiments, the relationship between cause and effect is not as clear cut as Humean causality assumes. Other variables can modify the effect, or the effect could not take place at all. According to Humean causality, if the effect doesn’t always take place, then no causal law obtains. Bhaskar argues, however, that causal laws are really generative mechanisms that only tend to have certain effects in the real world (i.e they are tendencies rather than invariances), subject to countervailing forces.

The real problem with empiricism, especially in its positivist mode, is the following: because only visible (measurable) causes and effects are recognized as “facts”, empiricism is unable to account for generative mechanisms or tendencies which do not always produce regular effects. In other words, positivism seeks to extrapolate from the closed conditions of controlled experiment to the real workings of nature, which it is unable to do because natural causes do not always produce regular effects (they are tendencies rather than invariances, as above).

In the natural sciences, this leads to scientists thinking that they bring nothing with them to their scientific work: no culture, no metaphors, no prior explanations, no theory. In reality, of course, even scientists know this isn’t true (or else scientific teaching and communication would be impossible). Positivism makes it difficult for scientists to see their own practice as socially grounded, even socially constructed. (Note that for Bhaskar, the social foundations of science are not an obstacle to the legitimacy of scientific knowledge, but he argues that critical realism, not empiricism, is necessary to ground it).

In the social sciences, positivism means that “generative mechanisms” like ideology, class, race, gender can’t be understood as operative because, like natural scientists, positivist social scientists don’t see themselves as bringing theories or values with them to their scientific work (therefore, ideology, race, class, and gender are inoperative in “value free” social science). In addition, empiricism - because it arose within the context of bourgeois liberal philosophy - also includes an individualistic sociology, that is a presumption that individuals are ontologically primary which precludes collective mechanisms like ideology, race, class, gender, etc, from being recognized. As isolated invididuals, the objects of social science - people - cannot coherently be affected by any structures of gender, race, class, etc.

What does all this have to do with Artificial Intelligence? In my view, we are getting better at explaining that technologies (especially algorithmic technologies) encode values, biases, theories, cultures, etc. (i.e. all the things positivism excludes), though there are still many people who believe in the “neutrality” of science and technology. We still have a long way to go to convince people how this happens - though a lot of the great work done by Safiya Noble, Marie Hicks, Cathy O’Neil, and Sarah Roberts is painstakingly exposing that. One thing I want to focus on here is the way empirical assumptions play into the design of machine learning systems.

By a priori excluding explanatory causes “from outside” the closed system of the program or data set, programmers are able see their systems and its data as closed, isolated systems. Similarly to the way methodological individualism¹ sees people, every data point, and every algorithmic decision-point is seen as as self-contained, unrelated (except perhaps in a schematic sense) from all the others. So, to take Safiya Noble’s example of a Google image search, a search for “doctor” brings up images of almost exclusively white people, while “thug” brings up images of exclusively young Black men. Empiricism/positivism sees each image in isolation - no commonality is allowed to disturb the “individualism” of each data point. So it becomes either an unexplainable accident, or worse a reflection of social “truth” that all doctors are white and all thugs are Black. Empiricism/positivism is unable to understand how its own assumptions and methods contibute to building up and reinforcing these particular (erroneous and dangerous) views of the world.

Similarly, when programmers seek to build a system, we are taught to build the system based on the specs of the data we have. Do not prematurely optimize for data that may or may not become available in the future. This is pure positivism: only the data you currently have count as “facts”. If the current set, say a gym membership list, includes only doctors who are men, then ontologically the system will only recognize “male doctors”. This has huge implications not only for connectionist AI systems (which by definition build their ontologies based on what is “present” in their training data) but for semantic systems as well. In practice, the limit to “anyone can say anything about any subject” is a positivist preference for present facts.

What is missing in the positivist conception of data-as-presence is the idea of data (and individual data points) as semiotic, as holding meaning. Positivism disallows “meaning” (like values) from the outset because they do not fit its ontology or its epistemology. Meanings come “from outside” or “from before”, they are not present. In order to challenge the presumptions of positivism, we have to understand and recognize meaning in code, data, in social relationships, etc. In other words, we have to learn to interpret code and data, to understand their meaning beyond their immediate, value-free significance as code or data in isolation.

I’m not philosopher enough to get into the metaphysics of presence/absence here, but suffice it to say that the implicit assumptions of empiricist/positivist science and social science are one of the vectors by which biases and values get encoded in technological systems. In the library world, we would do well to become more familiar with alternative philosophies of science as an aid to understanding how these systems get designed and operate.