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More promising is the idea of participation as justice. Here, all members of the design process work together in tightly coupled relationships with frequent communication. Participation as justice is a long-term commitment that focuses on designing products guided by people from diverse backgrounds and communities, including the disability community, which has long played a leading role here. This concept has social and political importance, but capitalist market structures make it almost impossible to implement well.
Machine learning extends the tech industry’s broader priorities, which center on scale and extraction. That means participatory machine learning is, for now, an oxymoron. By default, most machine-learning systems have the ability to surveil, oppress, and coerce (including in the workplace). These systems also have ways to manufacture consent—for example, by requiring users to opt in to surveillance systems in order to use certain technologies, or by implementing default settings that discourage them from exercising their right to privacy.
Given that, it’s no surprise that machine learning fails to account for existing power dynamics and takes an extractive approach to collaboration. If we’re not careful, participatory machine learning could follow the path of AI ethics and become just another fad that’s used to legitimize injustice.
A better way
How can we avoid these dangers? There is no simple answer. But here are four suggestions:
Recognize participation as work. Many people already use machine-learning systems as they go about their day. Much of this labor maintains and improves these systems and is therefore valuable to the systems’ owners. To acknowledge that, all users should be asked for consent and provided with ways to opt out of any system. If they chose to participate, they should be offered compensation. Doing this could mean clarifying when and how data generated by a user’s behavior will be used for training purposes (for example, via a banner in Google Maps or an opt-in notification). It would also mean providing appropriate support for content moderators, fairly compensating ghost workers, and developing monetary or nonmonetary reward systems to compensate users for their data and labor.
Make participation context specific. Rather than trying to use a one-size-fits-all approach, technologists must be aware of the specific contexts in which they operate. For example, when designing a system to predict youth and gang violence, technologists should continuously reevaluate the ways in which they build on lived experience and domain expertise, and collaborate with the people they design for. This is particularly important as the context of a project changes over time. Documenting even small shifts in process and context can form a knowledge base for long-term, effective participation. For example, should only doctors be consulted in the design of a machine-learning system for clinical care, or should nurses and patients be included too? Making it clear why and how certain communities were involved makes such decisions and relationships transparent, accountable, and actionable.
Plan for long-term participation from the start. People are more likely to stay engaged in processes over time if they’re able to share and gain knowledge, as opposed to having it extracted from them. This can be difficult to achieve in machine learning, particularly for proprietary design cases. Here, it’s worth acknowledging the tensions that complicate long-term participation in machine learning, and recognizing that cooperation and justice do not scale in frictionless ways. These values require constant maintenance and must be articulated over and over again in new contexts.
Learn from past mistakes. More harm can be done by replicating the ways of thinking that originally produced harmful technology. We as researchers need to enhance our capacity for lateral thinking across applications and professions. To facilitate that, the machine-learning and design community could develop a searchable database to highlight failures of design participation (such as Sidewalk Labs’ waterfront project in Toronto). These failures could be cross-referenced with socio-structural concepts (such as issues pertaining to racial inequality). This database should cover design projects in all sectors and domains, not just those in machine learning, and explicitly acknowledge absences and outliers. These edge cases are often the ones we can learn the most from.
It’s exciting to see the machine-learning community embrace questions of justice and equity. But the answers shouldn’t bank on participation alone. The desire for a silver bullet has plagued the tech community for too long. It’s time to embrace the complexity that comes with challenging the extractive capitalist logic of machine learning.
Mona Sloane is a sociologist based at New York University. She works on design inequality in the context of AI design and policy.