Equal Provision, Unequal Benefit: Social Class and the Conversion of Employer-Provided Digital Training into Worker Wellbeing Across 27 European Countries
Does equal access to AI training give every employee an equal chance to benefit?
Parenting Journal · Work & family · Volume 1 · Issue 3
Respect
Dignity, fairness, privacy and recognition of lives beyond the workplace.
20 articles · Page 1 of 1
Edition list · CSV ↓Read & explore ↗
The connected workplace: Characteristics and social consequences of work surveillance in the age of datification, sensorization, and artificial intelligence
Where should an employer’s data collection stop when work enters the home?
When resilience backfires: the counterintuitive effect of employee resilience in high-tech surveillance environments
Should employees have to become more resilient to intrusive AI monitoring?
The Impact of AI Negative Feedback vs. Leader Negative Feedback on Employee Withdrawal Behavior: A Dual-Path Study of Emotion and Cognition
How should performance feedback protect dignity when an algorithm delivers it?
The Positive Effects of Employee AI Dependence on Voice Behavior—Based on Power Dependence Theory
Can AI give an employee more room to question a manager’s decisions?
When technology meets aging: the paradoxical association between cognitive appraisal of AI and older employees’ successful aging at work
How can AI change respect the experience and independence of older colleagues?
AI technology adoption and intergenerational knowledge transfer among older employees
How can workplaces value older workers’ knowledge during an AI transition?
Care and control under dual transformation pressures: a three-wave multi-source study of paternalistic leadership, ambivalent psychological safety, surface acting, and employee voice in Malaysia’s service industry
Can a workplace sound caring while making it harder for employees to speak?
A Capability Approach to worker dignity under Algorithmic Management
Can a workplace be efficient while still expanding people’s real choices?
Ethical Design of a Robot Platform for Disabled Employees: Some Practical Methodological Considerations
Whose voice shapes a workplace technology designed to help disabled colleagues?
Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review
Who benefits from safer AI-supported work, and who might be left behind?
Increasing the ethnic diversity of senior leadership within the English National Health Service: using an artificial intelligence approach to evaluate inclusive recruitment strategies in hospital settings
Can AI help an employer inspect recruitment fairness without replacing accountability?
Objective and bias-free measures of candidate motivation during job applications
What should an employer be allowed to infer from a candidate’s facial behavior?
Artificial intelligence at work: The problem of managerial control from call centers to transport platforms
What changes when an old form of managerial control is automated?
Inclusive algorithmic work design: the moderating role of DEI interventions and diversity-supportive culture in AI-driven workplace transformation
How can inclusion be built into AI work design before people are asked to adapt?
Developmental or evaluative? Understanding the impact of algorithmic evaluation on gig workers’ thriving at work
Does an algorithmic evaluation help me grow, or only tell me how I rank?
Voice in the algorithmic era: how perceived algorithmic control influences gig workers’ voice behavior
Can a platform worker speak up about an algorithm and expect a response?
Exploring the impact of legislative absences on the working environment of gig workers in South Africa
What protections can fall through the gaps when work is managed through platforms?
Delivery app workers: working conditions and resistance to the control of digital platforms
Where can delivery workers find a human voice in an algorithmically managed day?
Algorithmic management and psychosocial risks at work: An emerging occupational safety and health challenge
Who should bear responsibility for psychosocial risks created by algorithmic management?
Study designs and age labels describe the source. Read each study’s findings and limitations before applying them to your family.