AI in Employment

13 academic & grey-literature sources bearing on this topic — catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.

• Fair Work for Platform Workers: Lessons from the EU Directive and Beyond ↗Peer-reviewed✦ AIAnalyzes the 2024 EU Platform Work Directive through Fairwork evidence, assessing its employment-status and algorithmic-management provisions and charting a path toward a proposed ILO platform-work Convention.
• Algorithm-facilitated discrimination: a socio-legal study of the use by employers of artificial intelligence hiring systems ↗Peer-reviewed✦ AIEmpirical socio-legal study of employer AI hiring systems showing how design and deployment choices generate discrimination that current anti-discrimination law struggles to reach.
• Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability ↗Peer-reviewed✦ AIField study of 391 NYC employers under LL 144: only 18 posted bias-audit reports; employer discretion over scope yields "null compliance", blunting the first AEDT bias-audit mandate.
• Auditing Work: Exploring the New York City Algorithmic Bias Audit Regime ↗Peer-reviewed✦ AIFrom qualitative interviews with 16 experts and practitioners, finds "LL 144 has not effectively established an auditing regime": undefined key terms, auditor data-access barriers, contested auditor roles.
• Regulating algorithmic management: A blueprint ↗Peer-reviewed✦ AIIdentifies regulatory gaps from algorithmic management (privacy harms, information asymmetries, loss of human agency) and sets out a concrete policy blueprint to address them.
• Algorithmic discrimination at work ↗Peer-reviewed✦ AIArgues existing European equality law is 'remarkably robust' against algorithmic management discrimination but that opacity and enforcement gaps blunt its effect, mapping where reform is needed.
• Algorithmic management and collective bargaining ↗Peer-reviewed✦ AIArgues collective bargaining and worker co-determination, not just individual data rights, are essential governance tools for regulating AI-driven algorithmic management at work.
• Regulating Algorithms at Work: Lessons for a 'European Approach to Artificial Intelligence' ↗Peer-reviewed✦ AISurveys EU data-protection, non-discrimination and social-acquis rules for governing "automated systems in high-risk settings such as the workplace", drawing lessons for the proposed EU AI Act.
• Big Data in the workplace: Privacy Due Diligence as a human rights-based approach to employee privacy protection ↗Peer-reviewed✦ AIProposes 'privacy due diligence' as a human-rights-based regulatory approach to algorithmic management and worker monitoring, arguing data-protection law alone inadequately constrains employer surveillance.
• Challenging Biased Hiring Algorithms ↗Peer-reviewed✦ AIEvaluates UK equality and data-protection law against algorithmic hiring tools and proposes a 'transparent recruitment scheme' incentivizing publication of equality metrics from data-protection impact assessments.
• Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices ↗Peer-reviewed✦ AISurvey of algorithmic employment-assessment vendors' bias-mitigation claims, examining how "algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law".
• "Negotiating the algorithm": Automation, artificial intelligence and labour protection ↗Working paper✦ AIArgues labour law must protect worker dignity under algorithmic management, urging a "human-in-command approach" with social partners governing automation.
• The future of employment: How susceptible are jobs to computerisation? ↗Peer-reviewed✦ AIEstimates computerisation probabilities for 702 occupations, finding about 47% of total US employment "at risk" — the headline figure framing displacement and retraining policy.