Social Science – Page 17

24/03/202422/03/2024

AI Sorting

Algorithms do the work behind AI systems. Therefore a basic understanding of how algorithms work is helpful to gauge the potential, risks and performance of such systems. The speed of computers determines the for example the amount of data you can sort at a reasonable time. Efficiency of the algorithm is an other factor. Here we go, we are already a bit absorbed into the the sorting as purely intellectual exercise. The website of Darryl Nester shows a playful programming exercise to sort numbers from 1 to 15 in a fast way (Link to play sorting). If you watch the sorting as it runs you realize that programs are much faster than us in such simple numeric tasks. Now think of applying this sorting routine or algorithm to a process of social sorting. The machine will sort social desirability scores of people’s behavior in the same simple fashion even for thousands of people. Whether proposed AI systems in human interaction or of human resource departments make use of such sorting algorithms we do not know. Sorting applicants is a computational task, but the data input of personal characteristics is derived from another more or less reliable source. Hence, the use of existing and newly available databases will create or eliminate bias. Watching sorting algorithms perform is an important learning experience to be able to critically assess what is likely to happen behind the curtains of AI.

21/03/202420/03/2024

AI and dialect

The training of Large Language Models (LLM) uses large data sets to learn about conventions of which words are combined with each other and which ones are less frequently employed in conjunction. Therefore, it does not really come as a surprise that training which uses standardised languages of American English might not be as valid for applications that receive input from minority languages or dialects. The study forthcoming in the field of Computer science and Language by Hofmann et al. (Link) provides evidence of the systematic bias against African American dialects in these models. Dialect prejudice remains a major concern in AI, just like in the day-to-day experiences of many people speaking a dialect. The study highlights that dialect speakers are more likely to be assigned less prestigious jobs if AI is used to sort applicants. Similarly, criminal sentences will harsher for speakers of African American. Even the more frequent attribution of death sentences for dialect speakers was evidenced.
If we translate this evidence to wide-spread applications of AI in the workplace, we realise that there are severe issues to resolve. The European Trade Union Congress (ETUC) has flagged the issue for some time (Link) and made recommendations of how to address these shortcomings. Human control and co-determination by employees are crucial in these applications to the world of work and employment. The need to justify decision-making concerning hiring and firing limit discrimination in the work place. This needs to be preserved in the 21^st century collaborating with AI. The language barriers like dialects or multiple official languages in a country ask for a reconsideration of AI to avoid discrimination. Legal systems have to clarify the responsibilities of AI applications before too much harm has been caused.
There are huge potentials of AI as well in the preservation of dialects or interacting in a dialect. The cultural diversity may be preserved more easily, but discriminatory practices have to be eliminated from the basis of these models otherwise they become a severe legal risk for people, companies or public services who apply these large language models without careful scrutiny.
(Image AI BING Designer: 3 robots are in an office. 2 wear suits. 1 wears folklore dress. All speak to each other in a meeting. Cartoon-like style in futuristic setting)

20/03/202419/03/2024

Sleeping BPS-SPB

Sleeping is a good example of the co-determination of the biological, psychological and societal spheres of life. The environment with the daily cycles of light and dark as well as the social norms of work and rest determine the circadian cycles of hormones. Shift work or otherwise disrupted sleep patterns depend on social norms like regulation of noise or light in cities. Healthy sleep patterns, therefore, depend to a large amount on regulation and implementation of those social norms. Birthday parties are tolerated, but much less the irregular partying in shared housing with lots of neighbours. Reducing social contacts during Covid-19 led to the changes in sleep patterns as well.
The psychological determinants of sleep go well beyond the world of dreams as theorized by Freud. Nowadays, we investigate all sorts of behavioural patterns that have an impact on sleeping like “bedtime technology use” of smartphones or the ability to switch off thinking of problems. Sleeping is a particular functional state of our mind. A lot of sorting of daily impressions into memory occurs during the different phases while sleeping. Persistent disrupted or impeded sleep is recognized as torture in severe cases. Stress at work or working overtime is also a major cause of sleep disorders.
The biological indicators used to investigate sleep have revealed a lot of links of sleep and the hormones of melatonin as well as cortisol. Testing has become more accessible and provides good indicators of how the biological clocks tick within our bodies.
However, we are only at the beginning of the analysis of more complex interactions of the multiple forms of interaction of the bio->psycho->social (BPS) as well as the social->psycho->bio (SPB) co-determination of sleeping. Scientific research is faced with a steep challenge as the direction of causality is not uniform except in very controlled experimental settings. Maybe the arts have coined and popularized a useful term in this respect. “I am in a New York state of mind”.
(Image: extrait of Magritte. La clairvoyance, 1936 and The cultural context of aging, Jay Sokolovsky)

15/03/202413/03/2024

AI and S/he

There was hope that artificial intelligence (AI) would be a better version of us. Well, so far that seems to have failed. Let us take gender bias as a pervasive feature even in modern societies, let alone the societies in medieval or industrial age. AI tends to uphold gender biases and might even reinforce them. Why? A recent paper by Kotek, Dockum, Sun (2023) explains the sources for this bias in straightforward terms. AI is based on Large Language Models. These LLMs are trained using big detailed data sets. Through the training on true observed data like detailed data on occupation by gender as observed in the U.S. in 2023, the models tend to have a status quo bias.
This means they abstract from a dynamic evolution of occupations and the potential evolution of gender stereotypes over years. Even deriving growing or decreasing trends of gender dominance in a specific occupation the models have little ground for reasonable or adequate assessment of these trends. Just like thousands of social scientists before them. Projections into the future or assuming a legal obligation of equal representation of gender might still not be in line with human perception of such trends.
Representing women in equal shares among soldiers, 50% of men as secretaries in offices appears rather utopian in 2024, but any share in-between is probably arbitrary and differs widely between countries. Even bigger data sets may account for this in some future day. For the time being these models based on “true” data sets will have a bias towards the status quo, however unsatisfactory this might be.
Now let us just develop on this research finding. Gender bias is only one source of bias among many other forms of bias or discriminatory practices. Ethnicity, age or various abilities complicate the underlying “ground truth” (term used in paper) represented in occupation data sets. The authors identify 4 major shortcoming concerning gender bias in AI based on LLMs: (1) The pronouns s/he were picked even more often than in Bureau of Labor Statistics occupational gender representations; (2) female stereotypes were more amplified than male ones; (3) ambiguity of gender attribution was not flagged as an issue; (4) when found out to be inaccurate LLMs returned “authoritative” responses, which were “often inaccurate”.
These findings have the merit to provide a testing framework for gender bias of AI. Many other biases or potential biases have to be investigated in a similarly rigorous fashion before AI will give us an authoritarian answer, no I am free of any bias in responding to your request. Full stop.

01/03/202428/02/2024

Error 444

The error message 444 is a not so rare encounter while surfing on the web. The error code 444 stands for the message that from the side of the server the connection is closed without any additional message. The occurrence leaves you without further indication of what exactly went wrong in building a connection to a web service or website. You just simply get shut out and that’s it. It may be tough on you if concerns your online banking or other access to vital services delivered through the internet.
In organization science and social science there are many new studies dealing with the finding, dealing, coping or handling errors. It has become much more acceptable to deal openly with errors, blunders or failures. From an individual as well as organizational perspective the cultures that deal openly with these events seem to have a certain advantage to overcome the consequences of errors at all or faster than others.
In some biographies failures are part of the lessons learned throughout life. It is deemed important to acknowledge failures or paths not taken for better or worse. Organizations just like individuals seem to learn more intensively from their failures or omissions than from everything seemingly running smoothly. Learning curves are faster also for “machine learning” if you have access to a huge data set which contains ample data on failures rather than encountering failures after release. Hence, the compilation of errors is an important part or early stage of a learning process. Failed today? Fail again tomorrow. You’ll be really strong the days after although it might hurt.

29/08/202305/04/2024

Barbie explore

The film on Barbie after more than 60 years of the first puppets to arrive on the market is a huge money spinning exercise. Hitting more than 1 billion $ is really a huge box office success. More interesting even is the banning of the film in some countries like Algeria. This gives the film an interesting subversive touch to it, which we in the Western countries no longer see as something special. Emancipated women pose a threat to authoritarian regimes.
However, we see in the stereotypes of beauty-driven dolls not that much of an emancipatory chance. To view emancipation independent of the looks of a person is another interesting twist to the role in stereotypes of beauty. It is not only fun to play around with stereotypes, that is mostly, if you are not negatively affected by them (age, gender, ethnicity, extraordinary persons). A nice task for sociology and psychology to explain the working of stereotypes in society and possible remedies. Tolerance is a competence that needs to be learned and updated continuously, from early age onwards.
Therefore, the website created by the US Design Agency Rvnway offers an entertaining way to play around and learn about stereotypes. Perceived, generalized beauty or gender roles can be explored using the tool. Maybe some see themselves differently after such explorations. Everybody is a model. This is the message. www.bairbie.me will let you explore other formats of yourself. After 3-D rendering and printing your children or grandchildren will decide what role they would like you to play in their playfull, or virtual “real” life. I suppose many of us will be up for a big surprise. Go on and imagine in 4D. In the age of selfies all around us, all the time, we believe we are very modern, but the artists of the 19^th and 20^th century following all great painters before, frequently started their careers with an “autoportrait” or “Selbstbildnis” or series of those as they were aging.