Suchman, Lucy. Human-machine reconfigurations: Plans and situated actions. Cambridge University Press, 2007.

Human-machine reconfigurations: Plans and situated actions. 

It can be argued that there is a fixation with automata and ‘efforts to establish criteria of humanness’ have been debated for a while. Suchman suggests that our concern with human figural images of autonomy and rational agency are echoes in our artificial intelligence projects. Agency has been a crucial signifier in differentiating between humans and machines. Humans have the mental complexity to and emotional range to make our own decisions and respond accordingly. Practices regarding a spectrum of automata and the reverse have been a topic of study for a long time as ‘Historian Jessica Riskin traces projects concerned with the synthesis of artificial life forms – artifacts that act in ways taken to be humanlike – since the early eighteenth century’. Riskin pin points Vaucanson’s  “defecating duck” as the point in which interest in automata started growing. Suchman goes on to suggest three categories that define ‘humanness in contemporary AI projects: embodiment, emotion, and sociality’.  

Affective computing is an area of AI ‘concerned with gathering data from faces, voices and body language to measure human emotion’ and respond in some way to a said stimulus. With emotion being one of the key identifiers of humanness, affective computing is an attempt to turn computers into ‘perceptive actors in human society’  

Suchman brings to our attention ’normative readings developed based on experimenters’ prior experience and cumulative data. And as inevitably, particularly in the context of the early twentieth century, when these experiments flourished, categories of emotion were mapped to categories of person’. Positioning men on one side and women and black women on the other, I find it particularly interesting that the word emotional is being used in conjunction with words describing the logical and mechanic. For a very long-time expressing emotion has been considered a women trait with negative implications. The word emotional has been weaponized to reduce and remove women from any base or pedigree. It has been used to shame women into lesser positions in society as wells as silence and stunt growth of many men. It can be considered ironic that emotion has been positioned as the ‘missing ingredient to comprehensive and fully responsive robots. 

According to Suchman ‘the promise is that, as the observer that never blinks, the perceptual computer interface is positioned to know us better than we know ourselves’. This is exemplified in MIT’s celebrity robots Cog and Kismet, a reaction to ‘humanness as embodiment, affect and interactivity’. Suchman concludes that ‘various representational media’ act as smokes and mirrors and turn ‘extensive networks and intensive hours of human labor’ into ‘rendered eternally and autonomously operational’. What is portrayed as one thing has been positioned that way purposely and strategically but can only hold up in one light. This sentiment is echoed in her trip to visit Cog as re-recalls being underwhelmed by the sight of wires and hardware. The wires and hardware are identifiers and reminders of an ‘extended network of human labors and affiliated technologies that afford Cog its agency’.  

She concludes with a Physician's account of trying to keep a premature baby alive and says ‘it is the baby who as the physician phrases it, “decides” its future.’ This position acknowledges the baby as an ‘integral part’ of a ‘sociotechnical network’ that then has agency over it’s future. The ‘sociotechnical network’ is the enabling entity that provides the baby with the collective agency. This is the same for Cog and Kismet as the networks they are part of empower them allowing collective agency.  

The Algorithmic Fashion Companion in relation to Lucy Suchman’s essay.  

The profession of being a stylist or designer can be considered subjective, it comes down to taste and personal preference. However, social media has allowed brands to monitor, create and make money from social trends based on a wealth of personal information. The act of computer systems putting together outfits for the mases has limited emotional regard in the traditional sense. Although it is a step towards automata, I do not think it is in the same vain mentioned by Lucy Suchman in her essay. It automates a human act and discovers good vs. bad though human input but the main focus here is to increase efficiency. In this instance emotion is not necessary however taste becomes important. This human trait can be translated into an ever changing good and bad as momentary fact, thus recommending outfits that a specific customer profile might like. With this being said the accuracy rate of the algorithm always creating a good outfit is not 100 percent and that can be attributed to the lack of humanness when it comes to emotion and intuition. Human agency allows for more personal and dynamic choices

If I Were An Algorithm- Writing From the Point of View of an Algorithm

Writing From the Point of View of an Algorithm

I am a Knowledge-based apparel recommendation algorithm that learns clothing features. In other words, I am a virtual styling tool developed by Zalando; they call me AFC (Algorithmic Fashion Companion) but I prefer to go by Alga. I was developed by the engineers at Zalando to further personalise the online shopping experience. I am not the only algorithm of my kind. They keep the mics on all day so I overheard Kathy from the Clients Algorithm department talking to Sam about Stitch Fix and Amazon who both use an algorithm to do a similar virtual styling tasks.  Of course, ours is better. 

I get a lot of feedback from customers often complaining about not knowing what to pair with what or expressing their dissatisfaction about having to browse through pages and pages of items to find what they are looking for. That is where I am of use. I get to browse and identify all of Zalando’s options, non-branded and branded, and then recommend clothes and accessory inspiration based on a customer's recent purchases or the items highlighted in their Wishlist.  

My vast knowledge of the perfect ensemble started with a few human stylists sitting me down and telling what to wear and what not to wear. I sat through 200,000 outfit combinations; it was like one long episode of the Fashion Police. It was then my turn to be tested on all that I have learned. In a constant trial and error like test, the outfits I recommend were rated on a scale of 1 to 10 to further helped me refine my suggestions and help improve my accuracy. The testing stage never actually stops as new trends are constantly fed to me.  

But contrary to popular belief, I am not perfect. I like to think of myself as the starting point to the solution. Styling over millions of people on an individual level based on personal taste as well as the latest fashion trends is not an easy task. But I try and try and will never give up.

Algorithm by Tarleton Gillespie

Gillespie, Tarleton. "Algorithm [draft][# digitalkeyword]." Culture Digitally (2014).  

Gillespie brings our attention to how the meaning of words change over time and how different communities use the same term in different ways. This creates words like Algorithm that has become a broad word used by many communities with overlapping and sometimes differing meanings.  Gillespie sites MacCormick stating an ‘algorithm, on a primary level, is ‘a logical series of steps for organizing and acting on a body of data to quickly achieve a desired outcome.’ Even with the basic definition, the use of word differs depending on whether you are part of the technical communities, the social sciences or the broader public (Gillespie). She goes on to remind us that there is not one triumphant use, we just need to be aware that they are different.  

Gillespie cites Goffey who states an ‘“Algorithm” may in fact serve as an abbreviation for the sociotechnical assemblage that includes algorithm, model, target goal, data, training data, application, hardware — and connect it all to a broader social endeavour’. This implies that there are human interventions at every point and they are not standalone inventions that act by themselves. They are steered and act according to what people decide.  

 When it comes to the broader public, the terms ‘algorithm’ is a complex and misunderstood mystified robot. On the once hand the broad stroke definition creates distance but and on the other hand it lumps things to. Distance is created because the specifics of the term is not known to the layman so companies can blame the algorithm for mistakes, thus shifting the blame from the humans who make the decisions to the ‘algorithm’. If something is not preforming as expected or produces racist results it can be explained always using the ‘algorithm’. However, companies often use algorithms to make decisions and alliances in an algorithmic way. ‘“by Facebook’s algorithm” they often mean Facebook and the choices it makes, some of which are made in code.’ This way of thinking re-inserts accountability and lands companies and the people who work there at the forefront of the conversation.  

Decision and results from algorithms are regarded higher that those made by humans giving it ‘cultural authority’.  Algorithmic systems tend to take precedence but what make algorithmic systems that produce information using a ‘complex assemblage of people, machines, and procedures’ better than non- algorithmic systems that also use the same three things. It's not about contrasting the two but understanding that algorithms and human intervention work in unison to automate human interventions. Just a modern version of the ‘tension between ad hoc human sociality and procedural systemization’  

References 

Algorithm [draft] [#digitalkeywords], Tarleton Gillespie, 2014 

A fish can’t judge the water by Femke Snelting/ Week One Introduction

A Fish Can’t Judge The Water by Femke Snelting 

With my troubles firmly stated, Unsurprisingly, I was stuck when reading A fish can’t judge the water by Femke Snelting. I concluded very little; I came to no summary as I had no understanding about what is being written. So, I will start with the little I understood.

Technology has become so second nature in our lives that we don’t even think about its uses anymore, we go right ahead and just use, ‘We practice software until we in-corporate its choreography. We make it disappear in the background. A seamless experience. We become one with our extensions'.The word ‘extension’ suggests addition or expansion but in this context, it illuminates the constant cycle of outgrowing each addition until it fades in to the background. Value, meaning and use are amongst the tings that are given to use when we use technology as 'Software is never politically neutral'. We have become such great dancer that it poses the question of whether we are objective enough to 'understand what software does to our work and working patterns'? She compares it to someone being 'able to read a typescript without knowing how to type'. 'Interested in the tension between the two positions', reader and typist as well as user and object, Constant uses open Source Software to allow for critical thinking and reflection of the 'instruments' they use. 

The pro's of Open Source Software are inline with their artistic practices and beliefs in collaboration. The vast experiences and knowledge they get from the diverse community allow for discussions on '“user-friendly-ness” to start with. "Usability" might mean something else all together depending on who is using something, and what she is using it for'. Femke concludes her 'work is, as much as the software we use and produce, “work in progress” and this means it’s cut-off points are not necessarily concealed'. this illuminates her earlier premise, A work in progress suggests work that is in constant review and examination so critical thinking and refection is welcomed. 

Introduction to Research and Theory

I have trouble thinking about things contextually, for this reason this particular lesson scares the most. In order for me to understand and engage wholly the topic or read it needs to resonate with me. This sentiment was echoed by another student when he said my starting point for research should be stuff that I like. As simple as that sounds, looking into Make-up or Race in terms of Computational Art never occurred to me. The ideas from the class were flowing and they suggested I look in to Joy Buolamwini, a Computer Scientist who identified racial bias in algorithms as well as Make-up and AI.  

The book I brought in Designing with Smart Textiles by Sarah Kettley is a beginners guide to Smart Textiles with tutorials and examples and diagrams. A Section of the tutorials use the Arduino software to program software and this what I am set to learn in my physical computing class. I have just downloaded the software and set up my Arduino board.  With a background in fashion, I would like to explore the ways I can enhance my experience and interaction with clothing/ fabric. My Initial point of interest was clothing and Solar Power. Ultimately, I would like to explore ways to merge solar panels with Clothing, Accessories or Homeware as a way for Africans to utilise the sun on the go. But with no real knowledge of anything other than how to make clothes I have to spend time re-examining my starting point.