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Collective brainstorming to imagine a feminist labour collective

[By Siddharth de Souza and Siddhi Gupta]

In early July 2021, as a group at FemLab.Co, we explored how to think about the connections between feminism, feminist design, and labour collectives that emerged in our work as researchers, lawyers, activists and designers. As part of this exercise, we were interested in discussing the ways in which we could come together to identify the workings of a collective, ascertain and reflect on issues of power and inequality that we were seeing in our field work, as well as imagine and speculate on what we could do and see as we continued our work, and digital storytelling. For the purpose of the workshop, the group members contributed their views in their individual capacities but also gave insights from their sectors of work namely, construction, sanitation, garment, home-based salon services, ride-hailing, and petty artisanal work.

Co-creating, imagining and speculating

This piece is in part a reflection on the workshop and an outcome of contributions from the team members which we have summarised.  It also serves as a reflection on the format of adopting a speculative design workshop as a medium for discussion and sharing. The workshop was inspired by a framework from Association for Women’s Rights in Development (AWID) that appeared in their exploratory toolkit called Feminist Realities Our Power in Action. AWID, a global, feminist, membership-based, movement-support organization believes “that feminist co-creation is driven by the desire to establish mutuality, equality and equity […]. It is collaborative, consensual and mutually-advantageous” where co-creation refers to a collaborative process in ”which stories are told and shared, dreams are dreamt and strategies for change are conceived”, and this was the guidance which we adopted for our discussions.

A key motivation of the workshop was to explore how to brainstorm collectively, without being weighed down by academic or technical knowledge of building a collective. We aspired to see if it was possible to co-create and collaborate on what people understood as structures for a feminist labour collective, and in doing so also unpack and imagine the elements of some of the constituent concepts including that of feminism and feminist design.

In order to be able to brainstorm, we felt that the best way to do so was to use the method of a workshop. The workshop was conducted online because the participants work across geographies and time zones. This established some constraints within which the workshop would operate. We were conscious as workshop facilitators that we were to drive a discussion on online platforms which can hinder communication and participation. For instance, only one person can speak at a time in an online meeting, there are network issues, and often not everyone is heard. Hence, the workshop was designed such that the participants could respond to the prompts in more ways than one. While responses could be verbalised in the Zoom room, participants could respond in writing on virtual sticky notes on the Miro board, which is an online collaborative tool.

This allowed multiple people to speak or record their responses, and as responses appeared in real time, it led to a collaboration where ideas emerged in response to what other participants had said. For instance, as a warmup to the workshop and Miro, which was a new platform for all our participants, we started with the game of ‘Yes, And’. The prompt was simple, “What would you like a feminist labour collective to be like?”. ‘Yes, And’ is a popular ice breaker in workshops where participants have to build on each other’s ideas. In our case, it was useful in enabling everyone to respond and to have all the voices in the room participate. It also set the stage for our next activity, where we would get into the details of imagining a feminist labour collective.

With everyone together in one Zoom room and one Miro board discussing parts of an issue, we were able to validate each other’s responses, draw linkages across ideas, and in a few cases articulate some very fundamental concepts that the larger group seemed to be resonating with. Participants could adjust the time they wanted to spend on the different parts of the workshop. They could revisit bits as well as go ahead if they felt they were done contributing to a certain section. Though we were moderating the workshop, it allowed flexibility to the group of around 7-8 persons to participate in a deliberative manner. None knew more than the other or had access to more or different information. It is the framework of a workshop that allowed these meta reflections to happen as it emphasized a do-it-yourself-work-in-progress type of atmosphere. From our discussions, it was apparent that if we were ultimately going to be talking about breaking hierarchies and power structures, this couldn’t have been done in an exchange where there are a few voices, or where some voices have more power than the other. Building a work-in-progress workshop in that sense meant less structure, but also more fluidity. However, we recognised that even in this format where things are open, there was a need to stop, and check that everyone in the room had the opportunity to contribute.

Understanding understandings

In setting out to imagine a collective we had a series of prompts. Drawing from the AWID workshop, we explored four aspects of a potential labour collective (see image below). The first was to understand the nature of the people that would make up the collective: examining their backgrounds, their motivations for joining, their opportunities to leave, as well as the kinds of relationships that they fostered. The second was to think of a place where a collective would come together and meet, what would define it as a space, what would ensure that it was a safe and secure space, and what kind of relationships would it afford. The third was to think in terms of the resources of the collective in terms of what resources are necessary to begin a collective, as well as to sustain and grow it. We also looked at how such resources could be shared and the ways in which resources could lead to community ownership. The final aspect was to think in terms of governance which was to think in terms of the values that govern the collective, the forms of accountability, and the ways to make it representative and visible.

Workshop output
Image credit: Justice Adda / FemLab.Co

In determining how to address some of these questions, the group identified two aspects that required further thinking. The first aspect was that of presence which included thinking about questions of identity, autonomy and representation in building a collective. The second aspect was that of process which involved the intent, the communicative aspects, the types of relationships and drivers of care that would inform the development of a collective. Through these two facets it became clear that there was also a need to be able to understand many worlds and recognise an epistemic diversity of the different members and the communities that the group were working with, thereby imagining a collective not in terms of key criteria, but rather, as multitudes with competing, complementary and even conflicting views. It became apparent that as we imagined what a feminist labour collective could look like, we also needed to think as a collective in terms of addressing the challenges of people, place, resources and governance that influence us personally and professionally.

Some preliminary connections

People

Comment from a participant to the question “What are the relationships between the different kinds of people?”

In the discussion on people, the group identified challenges of hierarchy, agency, motivations, stakeholders, external influences (like societal and familial). They raised questions of who made decisions and what methods were used. The group was interested in how agency was distributed and how transparency could be maintained for all stakeholders.

Places

Response from a participant to the question “What kind of relationships do people form in this place?”

There were many questions about access in physical and digital spaces while discussing the component of Places. There were discussions on the plurality of digital spaces, about borders and boundaries in spaces and how time affects those who experience these places. The group also addressed how space should be one that could nurture growth, and create feelings of care, trust and confidentiality.

Resources

A response to the prompt “What does it take to build a collective?”

In the discussion on the resources needed for a collective, there was a mention of tangible and intangible resources which ranged from considerations of safe spaces and questions of access and engagement, of associations of value with resources that are non-monetary and built on non-capitalistic systems, and the ability to create resources for the collective. There was acknowledgement of economic, social and cultural capital as well as the commonality of experiences that are all essentials for building a collective.

Governance

Response to the prompt “What values govern the collective?”

The discussion on Governance touched upon how maximum representation from different social groups could be achieved. Questions of transparency, recognising personal and professional priorities, different aspects of accountability were discussed in addition to decentralisation, and developing codes of conduct.

Conclusion

In brainstorming around the tenets of building a  feminist labour collective, we aspired to create a discussion where, as Dunne and Raby explain, “design thrives on imagination and aims to open up new perspectives on what are sometimes called wicked problems, to create spaces for discussion and debate about alternative ways of being, and to inspire and encourage people’s imaginations to flow freely.” The workshop as a method allowed us an opportunity to do so.

It provided a space where the team would discuss with a disciplinary openness, such that interventions could be both substantive and process based. There was a capacity to work with experiential ideas, such that the interventions were grounded in the ways in which people lived and understood their everyday acts and connections to feminism and how these could be understood in the design of ordinary things.  In doing so, the workshop afforded flexibility, fluidity and more importantly capacity to collaborate, one that gave freedom for new ideas, but also capacity to think through the limitations of existing ones.

Workshop output
Image credit: Justice Adda / FemLab.Co

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Is feminist design a solution to platform workers’ problems?

[By Pallavi Bansal]

Imagine a scenario in which you do not get shortlisted for a job interview – not because you are underqualified – but because the algorithms were trained on data sets that excluded or underrepresented your gender for that particular position. Similarly, you found out that you are consistently paid less than your colleagues in a sales job – not because of your inability to fetch clients or customers for the company – but because the rewarding algorithms favoured clients belonging to a certain religion, race or ethnicity. Further, you are asked to leave the company immediately without any notice or opportunity to interact with your manager – not because you committed a mistake – but because the clients rated you low based on prejudice.

While these biases, favouritism and discrimination could soon become a reality in mainstream workplaces due the exponential growth of decision-making algorithms, it is already causing disruption in the online gig economy. Recently, researchers at George Washington University found social bias in the algorithms related to dynamic pricing used by ride-hailing platforms Uber, Lyft and Via in Chicago, US. The study found “fares increased for neighbourhoods with a lower percentage of people above 40, a lower percentage of below median house price homes, a lower percentage of individuals with a high-school diploma or less, or a higher percentage of non-white individuals.” The authors of this paper Akshat Pandey and Aylin Caliskan told the American technology website, VentureBeat, “when machine learning is applied to social data, the algorithms learn the statistical regularities of the historical injustices and social biases embedded in these data sets.”

These irregularities are also spotted in relation to gender. A study conducted by Stanford researchers documented a 7% pay gap in favour of men, using a database of a million drivers on Uber in the United States. However, this study highlighted an even bigger problem that the researchers attributed to the following factors – differences in experience on the platform, constraints over where to work (drive), and preference for driving speed. A Cambridge University researcher Jennifer Cobbe told Forbes, “rather than showing that the pay gap is a natural consequence of our gendered differences, they have actually shown that systems designed to insistently ignore differences tend to become normed to the preferences of those who create them.” She said the researchers shifted the blame to women drivers for not driving fast enough and ignored why the performance is evaluated on the basis of speed and not other parameters such as safety. Further, in context to women workers in the Indian gig economy, it is imperative to understand whether these biases are socially inherent. For instance, if certain platform companies segregate occupations based on gender, then the resulting pool will inherently lack gender variation. This also compels us to ponder whether the concentration of female labour in beauty and wellness services, cleaning or formalised care work is a result of an inherent social bias or technical bias.

To make sense of all of this and understand how we can improve the design of these digital labour platforms, I spoke to Uday Keith, a Senior AI Developer with Wipro Digital in Bengaluru. His responses drew my attention towards Informatics scholar Bardzell’s feminist human-computer interaction design paradigm, which I use to contextualize them.

Illustration by Pallavi Bansal

PB: How can we overcome biases in algorithms?

UK: First of all, algorithms are not biased, it is the datasets which are biased. The imbalances in the datasets can be corrected via a method known as SMOTE (Synthetic Minority Over-sampling Technique) where the researchers recommend over-sampling the minority and under-sampling the majority class. In order to achieve this, we need to bring diversity to our training datasets and identify all the missing demographic categories. If any category is underrepresented, then the models developed with this data will fail to scale properly. At the same time, it is essential for the AI developers to continuously monitor and flag these issues as the population demographics are dynamic in nature.

This points us toward the two core qualities proposed by Bardzell – Pluralism and Ecology.  According to her, it is important to investigate and nurture the marginal while resisting a universal or totalizing viewpoint. She stresses to consider the cultural, social, regional, and national differences in order to develop technology. The quality of ecology further urges designers to consider the broadest contexts of design artifacts while having an awareness of the widest range of stakeholders. This means AI developers cannot afford to leave out any stakeholder in the design process and should also consider if their algorithms would reproduce any social bias. 

PB: Can there be a substitute for the gamification model?

UK: To simplify the process and ensure equity in the gig economy, platform companies can advise AI developers to introduce a “rule”. This would mean fixing the number of minimum rides or tasks a platform worker gets in a day, which can also help in ensuring a minimum wage to them and provide a certain level of income security. The introduction of a fixed rule can even eliminate social biases as this would not result in a particular gender or social group getting less work. Further, the reward system can undergo a major overhaul. For instance, rather than incentivizing them to drive more and indulge in compulsive game-playing, platform companies can build algorithms that provide financial rewards when the drivers follow traffic rules and regulations, drive within permissible speed limits, and ensure a safe riding experience. In fact, we can even provide options to the customers where they could be given discount coupons if they allow drivers to take short breaks.

Elaborating on participation, Bardzell suggests ongoing dialogue between designers and users to explore understanding of work practices that could inform design. This also means if the platform companies and AI developers are oblivious to the needs and concerns of labour, they may end up designing technology that could unintentionally sabotage users. Secondly, an advocacy position should be taken up carefully. In the earlier example, “driving fast” was considered as a performance evaluator and not “safety”, which usually happens because the designers run the risk of imposing their own “male-oriented” values on users.

PB: How work allocation can be more transparent?

UK: Well, deep learning algorithms used by various companies have a “black box” property attached to them to a certain extent. These algorithms are dynamic in nature as they keep learning from new data during use. One can only make sense of this by continuously recording the weightage assigned to the pre-decided variables.

The quality of self-disclosure recommended by Bardzell calls for users’ awareness of how they are being computed by the system. The design should make visible the ways in which it affects people as subjects. For instance, platform companies can display the variables and the corresponding algorithmic weightage per task assigned on the smartphone screen of the workers. So, if a platform driver has not been allocated a certain ride due to his past behaviour, then the technology should be transparent to reveal that information to him. Uncovering the weightage given to various decision-making algorithms will enable the platform worker to reform their behaviour and gives them a chance to communicate back to the companies in case of discrepancies or issues.

PB: How can we improve the rating systems?

UK: The platform companies have started using qualitative labels that could help users to rate the workers better. However, we do need to see whether sufficient options are listed and suggest changes accordingly. Moreover, if we want to completely avoid the numerical rating system, we can ask the users to always describe their feedback by writing a sentence or two. This can be analysed using Natural Language Processing (NLP), a subfield of Artificial Intelligence that helps in understanding human language and derive meaning.

Bardzell writes about the quality of embodiment in respect to meaningful interactions with the technology and acknowledging the whole humanness of individuals to create products that do not discriminate based on gender, religion, race, age, physical ability, or other human features. This concept should also be applied in relation to how users rate workers and whether they discriminate on the basis of appearances or other factors. Hence, there is a strong need to include the qualitative rating systems along with the quantitative ones.

Additionally, Uday Keith recommends defining “ethics” and frequently conducting ethics-based training sessions since a diverse set of people form the team of data scientists, which comprises of roughly 10% of women in an urban Indian city Bengaluru. He concluded by remarking that the issues in the platform economy are more of a system design fault than that of an algorithmic design – the companies consciously want to operate in a certain way and hence do not adopt the above recommendations.

These pointers make the case for the adoption of a feminist design framework that could bring about inclusive labour reforms in the platform economy. As Bardzell says, “feminism has far more to offer than pointing out instances of sexism,” because it is committed to issues such as agency, fulfilment, identity, equity, empowerment, and social justice.