The Algorithmic-Human Manager: AI, Apps, and Workers in the Indian Gig Economy

2026-06-18 · Source: Artificial Intelligence · Field: Business & Management — Operations & Process Management, Human Resources & Workforce Development, Entrepreneurship & Start-ups · Depth: Advanced, quick

Summary

A study examines the impact of artificial intelligence and digital technologies on India's blue-collar gig economy, specifically focusing on algorithmic management in location-based services such as ride-sharing and delivery. Using a social justice framework and mixed-methods, including interviews with 16 gig workers and 21 key stakeholders, the research uncovers a dual reality. While AI-powered systems expand access to work and generate operational efficiencies, they simultaneously introduce significant challenges related to fairness, transparency, and worker dignity. Key findings reveal that algorithmic systems are opaque by design, produce inequitable outcomes, and are not structured to reward additional labor with proportionate pay. The study advocates for an Algorithmic-Human Manager framework, a pragmatic hybrid governance model combining technological efficiency and human accountability. These findings carry implications for policymakers, platform companies, and civil society organizations designing equitable AI governance in India and the Global South.

Key takeaway

For policymakers and platform companies designing AI governance for the gig economy, recognize that current algorithmic management, despite efficiencies, often creates significant fairness and transparency issues. You should prioritize implementing a pragmatic Algorithmic-Human Manager framework. This model integrates human accountability with technological efficiency to ensure equitable outcomes and worker dignity across India and the Global South.

Key insights

Algorithmic management in India's gig economy boosts efficiency but undermines worker fairness and dignity, requiring a hybrid human-AI governance model.

Principles

• Algorithmic systems are inherently opaque.
• AI can yield inequitable worker outcomes.
• Extra labor needs proportionate pay.

Method

A mixed-methods approach, guided by a social justice framework, involved interviews with 16 gig workers and 21 key stakeholders to analyze algorithmic management impacts.

In practice

• Design equitable AI governance.
• Implement hybrid human-AI management.
• Prioritize algorithmic transparency.

Topics

• Algorithmic Management
• Gig Economy
• AI Governance
• Worker Rights
• India
• Digital Platforms

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Policy Maker, AI Ethicist, Consultant

Related on AIssential

• Algorithms don’t care: how AI worsens the double burden for Indonesia’s female gig workers — AI platforms can perpetuate colonial exploitation by embedding extractive logics that disadvantage women in the Global South's gig economy.
• Gig workers are endlessly exploited. AI could make more of us share their fate — AI adoption is driving a broad "gigification" of the workforce, eroding traditional employment benefits for cost savings.
• Accountability — Real accountability arises from ordinary people demanding, publicizing, and using information to challenge entrenched power structures.
• Posthuman: We All Built Agents. Nobody Built HR. — Effective enterprise agentic AI requires robust, out-of-band governance infrastructure, not just better models.
• The human cost of the AI governance gap: What the data tells us — The rapid adoption of AI outpaces effective governance, leaving workers and human rights unprotected.
• Is It Exploitation… Or Just Smart Technology — Gig economy platforms often yield sub-minimum wage earnings and lack benefits, driven by algorithmic work allocation.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.