"The algorithm does not rule" - Lilo Meier on her ethnographic research on courier drivers

Lilo Meier, what interests you about the work of courier drivers; what is your research about?

Riders from companies such as Lieferando or Uber are very visible in major cities; from Berlin to Barcelona to Hong Kong, they characterise the cityscape worldwide. At the same time, the working conditions and experiences they have on the road often remain invisible. Initially, there was a lot of euphoria about digital platforms. It was assumed that they would democratise our work. We now take a more critical view and know that many of these promises are not being realised. There are precarious working conditions, little security, high pressure to perform, physical risks and a high level of dependence on the platforms. Based on this, I was particularly interested: How could things be different? That's why I looked at a platform cooperative that has set itself the goal of redesigning ownership and decision-making relationships, involving riders more closely and enabling co-determination.

So you are trying to visualise a positive counterpoint to the prevailing criticism?

Yes, I believe that technology does not necessarily have to lead to something bad or good. I am therefore more interested in how economic activity can be organised differently in digital capitalism. In other words: how can technology be used to enable solidarity and democratic interactions?

Why did you decide in favour of an ethnographic study in terms of methodology?

The supervisor of my doctoral thesis, Jana Costas, has already done a lot of ethnographic research and inspired me. The great thing is that you don't just look at a phenomenon from a distance, conduct surveys and interviews from time to time, but that you are very close to everyday life over a long period of time - I did it for 15 months. You immerse yourself in the field; we say "to immerse oneself in the field". It's about understanding how things actually work, how work is negotiated in everyday life. I spent a lot of time looking at very mundane things in order to then understand and scrutinise bigger ideas.

What exactly did you look at with the courier drivers?

My focus was to follow the algorithm to the various touchpoints where people interact with it - that included the order of the customer, the work in the warehouse and the route given to the rider for the delivery.

To do this, I travelled through Berlin with the riders and made deliveries, packed orders in warehouses and took a look at the technological development process. I wanted to find out to what extent the developers' idealised ideas about how technology works actually correspond to practice. How much of it is an idea - and what happens when people use the algorithm in everyday life?

So the riders are not completely at the mercy of the algorithm?

There is this idea that everything runs in a straight line and works smoothly - like on a Google Maps route from A to B. In practice, I was totally surprised because things never actually worked out the way they were supposed to. A box is badly packed, the yoghurt bottle falls down and we have to clean it up and repack the box - which of course takes longer than planned. Then there's a blockage on the road, or cobblestones, and the riders say: Nah, we'd rather take a different route. There are a thousand imponderables that are often not even recognised when you think of technologically moderated work.

Don't these uncertainties just cause stress because the riders can't fulfil the requirements?

On the one hand, of course, it's precarious; it's exhausting and there are many dangers. On the other hand, it's also a very important moment in their professional identity. If you only follow instructions, every micro-movement is controlled and you have the feeling that my person and my knowledge don't really matter, then it can be very alienating. Having autonomy then also makes the riders proud. How fast can I ride? How well do I know the neighbourhood? What trick did I developed to deliver something as quickly as possible?

I imagine it's not easy to do research on a bike. How did you record your observations?

Yes, this ethnographic research is incredibly exciting, a lot of fun and at the same time really exhausting. As I drove through the city, I recorded short voice notes. However, the real work didn't start until I got home: exhausted from the day, I often sat there for hours writing my field notes.

What are the key findings of your work?

The most interesting thing for me was that the algorithm does not rule in the traditional sense. It never makes decisions alone, but creates contexts in which responsibility is shifted - often to the drivers.
The second key point is that it is not so easy to transfer a technological model that has emerged in such a profit-orientated and often precarious context into a fair or solidarity-based framework.

You write about courier drivers who are creating better working conditions for themselves via a platform cooperative - a rather small group so far. What can we deduce from this for the wider context?

The drivers I accompanied are aware that they have built a small enclave within capitalism in which they have more autonomy and freedom than most others. Despite this visible boundary, I want to show that it is important to try out how things can work differently. This is the only way to create alternatives and change.

We can't just complain that the conditions aren't good for workers. I am in favour of having utopian ideas about how things could work better. I also notice this need in teaching. Students ask: How can we change things? How can we deal with crises? Even if an idea fails - the organisation I studied no longer exists in this form, for example - it's always about trying new things.

To what extent does your research also play a role in teaching?

Among other things, I co-designed a seminar on the "Algorithmic Society". It's about how algorithms change work and organisations, i.e. the relationship between people, technology and social order. Above all, my teaching is about taking away students' fear of technology. Public debates always paint the picture that autonomous machines will overrun us, take our jobs or rule over us. In the seminar and in my dissertation, I work against this technological determinism. I want to scrutinise such theories with the students and show that technological developments are always embedded in social conditions. It is people, organisations and power relations that shape what technologies look like.