The Paper identifies how participatory approach in AI can improve the outcomes of the AI algorithm and enhance its fairness.
About Participative AI (PAI)
- It refers to the involvement of a wider range of stakeholders than just technology developers in the creation of AI systems.
- Core tenets of PAI are derived from participatory governance. (see box)
- Need: Progress in AI and its deployment by public and private actors, like Facial Recognition Technology in Law enforcement, etc.
Benefits of PAI
- Counter unilateral, top-down decision making in AI deployment: Addressing potential contentious breakdowns in implementation.
- Inclusion and Fairness: Mitigate risks like bias, discriminatory output, etc., to communities where an AI system is deployed.
- Feedback loops: In flagging technical glitches and post deployment impact assessment.
- Enhance trustworthiness of AI Systems: Ensuring minimal false positives and false negatives causing a more enthusiastic adoption of such systems.
Challenges with PAI
- Co-optation: Domination by select dominant actors to serve their vested interests.
- Limited participation of non-experts: Existing AI governance models primarily see the participation of experts like industry representatives, bureaucrats, select civil society, etc.
- Participatory washing and tokenism: Stakeholder participation merely done for formal compliance.
- Transparency Paradox: Information shared about algorithms can be misused by malicious actors.
Idea of Participatory Governance In India and Abroad
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