Submitted by fr4nl4u t3_103bdub in MachineLearning
Hi all!
What are some interesting applications of machine learning in fields that are not typically associated with technology?
For example, have there been any successful projects using ML in fields like sociology, psychology, or political science? If so, could you share some links or references?
Looking forwards to discovering your use-cases!
I would love it if we could rebrand tech as this.
One of my areas of interest is the intersection of machine learning and social work:
That second one is really fascinating for someone just learning about NLP. Thanks for sharing
Sociology: A team of researchers used machine learning to analyze social media data and predict the likelihood of an individual becoming homeless (https://www.nature.com/articles/s42256-019-0106-5).
Psychology: A group of psychologists used machine learning to predict the likelihood of a person developing depression based on their social media posts (https://www.nature.com/articles/s41562-017-0214-1).
Political science: Researchers used machine learning to analyze political text data and predict the likelihood of conflict in different regions of the world (https://www.pnas.org/content/115/41/10302).
Agriculture. Sensors on planting and harvesting equipment grab thousands of data points each second they’re running and upload to the cloud in near real-time.
ML comes in to play when correlating yield response to soil types, plant varieties, and product applications to do things like nitrogen availability modeling, etc.
University of Lincoln in the UK has a big focus on this area. Here is one of their papers: https://arxiv.org/pdf/2109.11048.pdf
Marketing, ie, getting other humans to behave in ways that benefit your institution with no concern for themselves, is the established number one widespread application for machine learning.
Show me a marketing operation without any ML, and I will directly chat my marketing director pal to assess this opportunity and its scope for reals.
Btw Marketing is psychology with a particular purpose.
Forestry - image recognition on satellite data to identify patches of forest that may be overtaken by invasive plant species and thus more susceptible to wildfire. Also to recognize patches of disease.
Edit: adding link: https://academic.oup.com/forestry/article/95/4/451/6518266
Just one of many papers. I’ve also come across a few startups which specialize in this topic.
I’ve co-authored a social-science + machine learning research paper about name-ethnicity classification. The paper is going to be released soon but you might be interested in our GitHub organization:
https://github.com/name-ethnicity-classifier
I’ve also developed a webapp but it’s currently down because of technical problems and I don’t have the time to properly maintain it.
What good does your model do ? Programmable stereotypes. I genuinely don’t see how it helps. Help me understand how name —-> ethnicity mapping is useful ?
Social science research moved from a color-blind approach back to color-consciousness. It’s important to know how the ethnicities are distributed in a dataset when doing social science research because if you don’t you might get results that only benefit the group of people that were most represented in the dataset and doesn’t recognize minorities. Therefore knowing ethnicities while doing research can help against or identify biased results.
This is my guess. It is useful because many countries do not track statistics according to race or ethnicity. Knowing which names belong to which ethnicity could allow for more comprehensive public records dataset interpretation. A country not tracking race or ethnicity may sound nice at first, but it is a form of colorblind racism. That is because it hides systemic injustices that happen to specific groups of people. It is like when the last president guy said covid numbers shouldn’t be recorded too keep cases down.
What if i told you you can track any such injustices by tracking such injustices themselves. Dimensionality reduction to pre-scientific concepts of race do more harm than good i'm glad more and more people are recognising. The amount of causal factors such downsampling obscures (which may then be adressed) is enormous. You can call me an old-school leftist but i think a social-economical-cultural perspective makes a lot more sense than the pseudo-biological sexuality/race perspective some of the new kids are pushing. It has brought some new ideas but also a lot of regression, 'colorblind racism' being one of them. You might call it color-lens racism - reducing people to immutable medieval categories is the real harm assuming this is something we don't want to happen (which i don't). We are all mixed race. Peace.
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I’m not advocating for one position over any other, I’m open to ideas. I was merely guessing what the use for that dataset could be.
That out of the way my first major question is how exactly do you propose tracking “said injustices” when the crimes are related to a subjective and human perception of race itself? Like hate speech, or redlining? Objective measurements of race related crimes do not make sense when said injustices come from flawed human judgments themselves. I still feel like this is the illusion of if you don’t measure it, it doesn’t exist.
The folliow up to that is, so what are you suggesting as an alternative exactly? Tracking DNA? I personally am all for knowing my genome, but considering how many people think mrna vaccines are evil, how will that work? You would have to demonstrate great medical capabilities that are blindingly obvious to the layperson and affordable to get everyone on board. Alternatively you could track facial features, odor, whatever and get an approximation, but again, consent is an issue. Instead you can track nothing about race or ethnicity. How exactly do you pull meaningful information from nothing? In my mind this approach presupposes a naive idealism about the good nature of people, their intelligence, self-awareness, compassion, empathy, and selflessness. It also ignores tribalistic tendencies of humans. More importantly though it ignores the biological root of racism which is based in disgust.
If you think about disgust, it makes sense. The reason when you see and smell dog crap and you feel disgusted is because it prevents you from catching diseases. If it smelled like fresh cookies…. Disgust is a preventive mechanism for disease, which is why in evolutionary sociological terms, ingroups and outgroups form. If you see a person that looks different from you they are likely to be from far away, and people from far away can bring you new diseases and kill you. Outgroup violence happens as a secondary effect to this initial disgust which is sensed as a threat, this bubbles to the surface of consciousness as racism. Fascism is effective because of disgust for the outgroup and loyalty to the ingroup.
I just wanna mention here, I’m talking about the phenomenon of racism on the level of human perception, because that is where it happens. It does not happen in analysis, it is only recorded as a weak signal of the original phenomenon, maybe a downsampling type of reductionism.
Secondly, i think you have a bit of spotlight bias (i’m not claiming i don’t have it too). I’m not good at the bayesian thing, but i would guess the amount of people believing that “pre-scientific “ judgments about race are invalid assessments is really low. Partially because people like me assert that they literally social constructs. You cannot reinterpret race as a biological construct because it is partially judgment and subjective based. That is why a light skinned puerto rican might pass as white, act white, or even identify as white — because others identify her that way.
3rd question is
Problematic application is problematic. Sure hope your labeled training data is from people self identifying ethnicities at least and not a few turkers gut* judgements.
https://www.wadhwaniai.org/programs/pest-management/
Few other examples under “Our Work” section in this page.
bubudumbdumb t1_j2y58t2 wrote
I think you mean "not associated with the advertising industry"