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.

Symbol

What is it that makes an object a symbol? Probably, it is the widely shared perception of the meaning of a symbol that turns an object into a symbol. The etymology of the word symbol refers back to the Greek word σύμβολον. The earliest philosophical refer back to the Greek philosopher Aristoteles who deals with symbols when he writes about interpretations. Written words have become powerful symbols in the ancient world. We still have them all around us today. The interpretations of the words as symbols, however, may change considerably over time. Some symbols keep their designation and significance over centuries. Maps are well known to contain lots of symbols for roads, railways, tunnels or height. We learn about these symbols and interpret them in a specific societal context. Science is making ample use of symbols, e.g. chemistry. Different cultures define and apply their own symbols. Colonialism has been a form to impose symbols upon other societies. Throughout history symbols of power have changed as well. Each of those topics is an interesting field of application in itself. Young generations create their own symbols to establish a specific cultural identity or subculture. Urban spaces have been invaded by graffiti that tend to spread symbols as messages or symbols for their own sake.
Sociology has taken up the challenge to identify “status symbols” of groups of society. Possession of gold and silver have long ago been symbols of being rich. Maybe, even today such easily visible symbols play a role in how a person’s role is perceived in societies. Not only for priests etc. dresses have been applied as a symbol. Modern fashion is full of symbols as well. Interpretation of the meaning or even no meaning is an act of becoming conscious of the world around you. From the seriousness of symbols, we have come to the playing around with symbols as expressions of ourselves.
No matter whether we use the word, like water, we all know the chemical symbol H20. An image or art work using the symbol in whatever form will be decoded by us accordingly. However, the meaning we attach to water depends on the environment as well as specific context we (or the artists) are using it in. Cross-cultural competences consist in the awareness that symbols grow out of contexts and need to be interpreted accordingly. It needs a lot of openness, willingness to learn about differences and careful consideration in our everyday world to handle symbols. Doing culture is doing symbols.(Image of art work by Anderegg, Andi taken in 2016)

Biographies

The biographies of painters, composers or artists in general can be reconstructed by use of their major works. The biography of René Magritte by Eric Rinckhout (2017) has chosen this way of a retrospective in images and explanatory texts. The biography is built around 50 major images starting with the first one by Magritte at the age of 28. “Les réveries du promeneur” (see below) deal with the confrontation of Magritte with death, suicide and the difficulty to find rational answers to all those questions of why this happens and what becomes of people who experience such a tragedy.
His own childhood was affected by such an event concerning his own mother who suffered from depression. Coping with the evolution of psychic illness over years and the absence of a supportive father have posed a steep challenge for the young person. Creation of art became a coping strategy as well as a relief for those who manage to eventually cross the bridge into their own life leaving behind the bad experiences. J.J. Rousseau was an influence on Magritte as well.
Finding your own destiny and your own style is a process. This process evolves over years. The chronological sequence of 50 images allows to follow the path taken. Thereby, it becomes possible to open up the personal learning and working trajectory of the artist. In retrospective perspective it seems often logical that one style or period of painting follows on from another one. However, in the forward living of the creative life many choices are heavily contingent on other circumstances. Influences of friends, exhibitions, reading, cinema, private or financial situations may determine the creative choices simultaneously or one at a time.
The chronological path of images writes a biography of a special kind. It allows to think in sequences just like in a sequence analysis as sociological methodology. Description, reconstruction, analysis, causality remain a challenge in our attempts to learn and understand more about biographies or the construction and reconstruction of life courses.

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.