A Noob Is Critiquing My Algorithm!

Computer Literacy, Social Science and Artificial Intelligence

Face it, we do not have anyone that can do everything, and I doubt we ever will. Not even an algorithm is optimised for everything, selecting the best elements from alternatives or otherwise the most effective use of a resource. What is an optimised human-being? Depends on the resource in question, assets that can be drawn upon from nature or yourself.

Yet this is already a preconceived notion or lens we view the world through. Consider this a statement from a noob, a person who is inexperienced in a particular sphere of activity — particularly computing.

Algorithms are unambiguous specifications for performing calculation, data processing, automated reasoning, and other tasks. We assumed rationality for humans, especially within economics, so why do some assume rationality in algorithms?

Let us be conclusive and make some broad generalisations. Firstly, we are wrong to some extent about programmed rationality. Secondly, we need to rethink data science and optimisation. Broad statements! Indeed, the climate crisis is perhaps not optimal for humanity. War is not very optimal. Misunderstandings are not very optimal. With an uncomfortably numb feeling I worry about war amidst this climate crisis, yet I question whether these concerns are optimised for.

Let us indulge in misunderstandings, or missed understandings.

Missed understandings. The understanding or perspective that you are missing out on when you dedicate yourself so intensely to one field. Can FOMO (fear of missing out) be extended to the scientific community? As a social scientist should I even be focusing on artificial intelligence at this time when we lack so much understanding of our own communities? It attracts my attention and I am not sure if I should respond so easily.

I have declared the decision of learning a few programming languages such as Python and R. I have a rudimentary understanding of HTML, CSS, Javascript and SQL. I have taken a machine learning module based on the R programming language. This involves a dedication to learning more mathematics, particularly statistics. As a student majoring in anthropology this is either lauded or frowned upon (I get both frequently) and it is eclectic. At first I found eclectic to be a harsh criticism, as it was said almost exclusively with rude tonality and admonishment. Now I am not so sure.

Eclectic: deriving ideas, style, or taste from a broad and diverse range of sources.

When I dedicate a set amount of my time to the computer at home or programming with people at social events it changes my priorities. I have an incredibly deep respect for some of my fellow students who study homelessness, religion, hate, love, fish and a variety of different topics. That is what makes studying society so exciting. Anthropology is an incredible and ridiculously eclectic field, possibly because humans are such eclectic beings.

We take so much input or data from the world and it becomes who we are. This experience of life draws upon a vividly diverse range of sources. Can we be inspired to write a code structure based on a sunset? Yes we can! It can come from a breakup, jealousy, sorrow and you name it. The tip of your fingertips that play almost magically across a keyboard, they are you.

This philosophical split between the subject and the thing seems so critical to society. To such an extent that it is said in classes to be the foundation of modern philosophy. We are stuck in this Cartesian worldview of “res extensa” the thing or extension of our thinking, and “res cogitans” the thinking. It is this mind and body dualism that still seems to shape so much of scientific thinking. Emotion shapes society and things shape emotion; it is this strange interplay we forget or ignore.

Technological and not technological – techie or non-techie, programmer or someone else. Becoming a programmer or being programmed, it seems we are presented with such a choice. Particularly in this oft described paradigm or fourth industrial revolution. Your job will either be to teach algorithms or to make algorithms. To become the mind or become the body, to become the user of technology or the used — in this intimate relationship paradoxically ever more distant or present.

Boundaries in society and research between virtual or real seemingly another expression. Boundaries between cultures equally blurring, because they were defined by us in the first place. Us eclectic people, I am told not to use ‘us’ or ‘we’ without definitions — because who is ‘us’ or ‘we’?

Grey areas are so hard to operate in that we discard them for the 1 or 0, ignoring that the combinations of these numbers are grey areas, either it works or it does not work. Either it runs or it does not run. Once culture is defined it changes.

I am afraid of diving to deep into this culture of programming and loosing myself as a budding social scientist. At the same time I am afraid of loosing out on this wonderful and strange expression that has become part of our eclectic world. Code is human and human is code. Programming is human and not humane or mundane, who — it is when too. Is it time for social scientists to learn a new language?

Let us, whoever we are, dare to be noobs critiquing algorithms.

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Photo by Ian Schneider from Unsplash

This is day 15 of my project #500daysofAI.

What is #500daysofAI?

I am challenging myself to write and think about the topic of artificial intelligence for the next 500 days with the #500daysofAI.

This is inspired by the film 500 Days of Summer where the main character tries to figure out where a love affair went sour, and in doing so, rediscovers his true passions in life.

Hope you enjoyed the read!


Written by

AI Policy and Ethics at www.nora.ai. Student at University of Copenhagen MSc in Social Data Science. All views are my own. twitter.com/AlexMoltzau

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