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.