Guest lecture with Winne Soon

Winnie Soon gave a lecture about Machine Learning and how she uses it in her artistic practice. From what I gathered from talking to the other students, I can only assume she did a great job at imparting and breaking down complex concepts associated with machine learning. With little coding background and a very short history interacting with the digital beyond a short course in html, much of what soon said went over my head. My biggest takeaway from her lecture was regarding censorship in china. My group are interested in how people fashion their digital self in contrast to their real self, including how and if people translate their sociopolitical views from real life to online. For Chinese individual’s freedom of speech/type has been taken away.  In a journal titled ‘Assessing Censorship on Microblogs in China: Discriminatory Keyword Analysis and the Real-Name Registration Policy’ the authors (King-wa Fu, CH Chan, Michael Chau) conduct a study ‘design helped researchers determine a list of Chinese terms that discriminate censored and uncensored posts written by the same microbloggers.’ They used the popular Chinese social networking platform, Weibo which is often referred to as a “free speech platform” by westerners. They concluded that this was far from the case and ‘Chinese authorities’ ubiquitous mechanisms for controlling the public information flow’ scourer through posts and delete what is not in alignment. It is argued that the internet in China plays an ‘overarching’ part in activism and ‘empowering’ citizens to ‘build the public agendas’. They go on to say ‘that Chinese authorities can tolerate posts that write on a wide range of criticism of the Chinese government and its policies, but tend to be more sensitive to censoring the spread of posts that might lead to collective action.’ Another way the Chinese government are censoring voices is through the real-name registration (RnR) system. Bloggers have to divulge information linked to their true identities to get government verification so what is written and how often is policed with fear of ‘arrest and imprisonment’.   Anonymity is both positive and negative depending on context. In china the removal of anonymity stifles freedom of speech in regards to sociopolitical events and happenings. In the west, anonymity has birthed a new phenomenon called ‘Trolling’.  A social media troll is someone who saying controversial things in order to upset or get a rise out of other users. The issue of trolling is an epidemic in the west for many demographics as emotional harm knows no age, money or race boundaries. As a result, YouTube and Google have expressed interest in making their users use their real identifies in order to police trolling. It poses the question how does one decipher freedom of speech and a difference of opinion from purposeful hate? This is an arena I would like to explore further with group. Especially as now more than ever there is money and free advertisement in certain forms organised form of trolling. For example, H&M have been accused multiple times of being either insensitive/ out of touch or just racist. An incident involving a young black boy wearing a hoodie with the words ‘coolest monkey in the jungle’ surface in 2018. Pictures were released and this was hurtful to many but H&M’s advertisement increase regardless of the reason.

President Trump has even been accused of being a troll on numerous occasions, using is platform to perpetuate hate and fear but for political gain.  

DATA - Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification by Joy Buolamwini and Timnit Gebru

Bias in facial recognition is topic slowly coming to the forefront of mainstream media arguably as a result of the growing awaking to data and how data is collected, used and distributed. Data is a word that can be compared to ‘Algorithm’ in the context of Gillespie’s essay called Algorithm. Gillespie expresses her thoughts on the term algorithm meaning different things to the various users of the word. No one use or meaning is more dominate, but awareness of the difference and similarities of the word is needed. Similar to this, Data is a word people hear and are familiar with on the surface but know of no concrete meanings. When a word’s meaning is unknown and speculated, policing becomes difficult. Data is what my group will explore further. How we curate online presences verse our real life presence and the concept of deleting yourself from the digital sphere. Our starting point was to read into the data protection laws for our respective countries. I also read the terms and conditions of poplar social networking sites like facebook and instagram. Reading through the long web pages I was shocked at the extent to which data we do not physically give or input is collected. For example, people are aware that you sign up to Facebook using details like your name and email. This is commonly accepted; however, the data collection does not stop there. By signing up and agreeing to the terms and conditions you allow Facebook to access a long list of things that include: 

1. The location of a photo or the date a file was created 
2. What is seen through features they provide, like the camera. 
3. Facial recognition software to analyze photos and videos they think you're in. This is the default setting and needs to be manually turned off. 
4. They can access information about your device, websites you visit, purchases you make, the ads you see, and how you use their services whether or not you have a Facebook account or are logged into Facebook. 

I wonder if widespread knowledge of terms and condition will have an affect on the popularity.

4. 
   

Joy Buolamwini is a computer scientist and digital activist who brought the bias in facial recognition to the forefront of many large institutions. She has been instrumental in the progression and diversity of a new stanard of training datasets. For her university studies she wrote a practice-based thesis titled ‘Intersectional Accuracy Disparities in Commercial Gender Classification’ that focused on machine learning algorithms not being able to accurately categorise women and men of darker skins tones.  She begins by stating ‘who is hired, fired, granted a loan, or how long an individual spends in prison, decisions that have traditionally been performed by humans are rapidly made by algorithms (O’Neil, 2017; Citron and Pasquale, 2014)’.  This immediately positions the text as significant as there is a possible threat to one's quality of life. She emphasises the point further expounding that ‘A year- long research investigation across 100 police departments revealed that African-American individuals are more likely to be stopped by law enforcement and be subjected to face recognition searches than individuals of other ethnicities (Garvie et al., 2016). False positives and un- warranted searches pose a threat to civil liberties.’ Errors can be detrimental and for this reason improvements must be made. 

Buolamwini  created a new face data set of 1270 individuals ‘annotated … with the Fitzpatrick skin classification system’ and 4 subgroups: darker females, darker males, lighter females and lighter males. Skin colour is a more precise visual in comparison to rather than race or ethnicity. Even further her ‘work introduces the first intersectional demographic and phenotypic evaluation of face-based gender classification accuracy’. She chose members of parliament from Rwanda, Senegal, South Africa, Iceland, Finland and Sweden. Countries where you will typically find the lightest or darkest individuals as well as a high population of women in parliament. The results of her study results revealed; one, all classifiers performed best for lighter individuals and males. Two, the classifiers performed worst for darker females. She concludes that further work should focus on ‘increasing phenotypic and demographic representation in face datasets and algorithmic evaluation’. 

People have studied ways to create fairer algorithms in many digital fields however the effects and ‘implications’ of false recognitions call for more relevant action to be done. ‘While the NIST gender report explored the impact of ethnicity on gender classification through the use of an ethnic proxy (country of origin), none of the 10 locations used in the study were in Africa or the Caribbean where there are significant Black populations’. Buolamwini implies diversity and representation is key at all levels. It can be argued that the oppressed will do a better job at speaking to the struggle they live than the oppressor speaking to the struggles of the oppressed. She goes on to say ‘Previous studies have shown that face recognition systems developed in Western nations and those developed in Asian nations tend to perform better on their respective populations (Phillips et al., 2011)’.  It is not on everyone's agenda to do a thorough job. It can also be argued that Joy, as a Ghanaian woman living in the diaspora, has different motivations for finding thoughtful and creative solutions to issue she is personally affected by or an advocate of.