Power to the People? Opportunities and Challenges for Participatory AI

tags

AI Participatory Design

Notes

participation opens the gateway to an inclusive, equitable, robust, responsible and trustworthy AI.

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lack of clarity on what meaningful partici- pation entails and what it is expected to do.

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optimistic vision of AI intended to deeply support human agency and enhance prosperity.

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center the values of inclusion, plurality, collective safety, and ownership,

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self-determination and community empowerment.

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‘participation-washing’

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transparent, accountable, and responsive to the needs of those who participate.

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learn from and include knowledge created by the people directly affected

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traced back to the work of Scandinavian researchers in the 1970s and 80s

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‘workplace democracy’ understood as a system of structured consultation and dialogue between workers and employees with the aim of giving workers greater control over wages and the allocation of tasks.

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asymmetrical power dynamics in society left unchanged.

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range of participatory methods, whether they include Del- phi interviews, citizen’s juries, role-playing exercises and scenarios, workshops, force field analyses, or visual imagery

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mediated participation

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notion of par- ticipation played a central role in the administration of the British empire.

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agency and intentionality

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colonial projects claimed their legitimacy under the veneer of participation.

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shift away from logic-based AI systems towards more data-driven paradigms such Deep Learning [48] as well as new infrastructure for capturing and leveraging human-generated data (e.g. Amazon Mturk) prompted greater demand for “non-expert” participation

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participation without redistribution of power is an empty and frustrating process

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ImageNet [22], where the research team utilized over 49,000 workers from Amazon’s mechanical turk (Mturk)

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abuses and concentration of these forms of “Ghost Work" in low income countries and peoples

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industry stakeholders calling for greater par- ticipation without resolving these tensions has led to concerns of ‘participation-washing’

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Reciprocity, Reflexivity, and Empowerment,

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By not sharing their data, the Māori argue they are able to maintaining their autonomy and right to self-determination.

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foregrounding reciprocity and refusal

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the research, datasets and tools may be co-opted and monopolized by commer- cial actors to improve products or models without supporting the broader grassroots effort or the community’s interests or needs. As a result the primary beneficiaries of participatory data sourcing may not be speakers of ‘low-resourced’ languages but actors with access to such sufficient data and compute resources, thus gaining financial benefits, control and legitimacy off of such participatory efforts.

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Māori com- munity had to explicitly prevent corporate entities from getting hold of the dataset. The community thus established the Māori Data Sovereignty Protocols

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participation as a form of algorithmic perfor- mance and/or dataset quality improvement.

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participants are assigned pre-defined roles and very clear tasks.

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this form of participation focuses on fine-grained activities that come with pre-defined goals and objec- tives means that there is little room (if at all any) for co-exploring, co-creating, and/or negotiating the larger objectives, reflections, and implication of AI systems.

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Concerns remain of participation becoming a mechanism of cooptation.

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net harm to the targeted regardless of their improved perfor- mance.

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difficult to measure and provide attribution to the positive benefits of participation.

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participation is not the best mechanism for decisions/values/norms that are better decided and codified by democratic institutions, governance and laws.

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require stronger forms of validation and legitimacy [32]. Participatory activities convened by private actors or parallel institutions, cannot stand in for democratic politics,

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