Unleashing Excellence through Inclusion: Navigating the Engagement-Performance Paradox

• URL: http://arxiv.org/abs/2407.09987v1
• Date: Sat, 13 Jul 2024 19:30:01 GMT
• Title: Unleashing Excellence through Inclusion: Navigating the Engagement-Performance Paradox
• Authors: Nicole Radziwill, Morgan C. Benton,
• Abstract summary: People who feel that they do not belong (or their voice is not heard at work) commonly become disengaged, unproductive, and pessimistic. This paper contributes to the literature on quality and performance management by developing a conceptual model of inclusion that directly impacts performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People who feel that they do not belong (or their voice is not heard at work) commonly become disengaged, unproductive, and pessimistic. Inclusive work environments aspire to close these gaps to increase employee satisfaction while reducing absenteeism and turnover. But there is always a job to be done, and under time and resource constraints, democratic approaches can result in reduced quality and unacceptable delays. Teams need actionable guidance to incorporate inclusive practices that will directly impact effectiveness. This paper contributes to the literature on quality and performance management by developing a conceptual model of inclusion that directly (and positively) impacts performance, and identifies eight factors that workgroups must address to create and maintain inclusive, high performing environments.

Related papers

• Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies [24.570596184292384]
  We propose an optimization-based fairness-aware desk-rejection mechanism. We prove that optimizing individual fairness is NP-hard, whereas group fairness can be efficiently optimized via linear programming.
  arXiv  Detail & Related papers  (2025-02-02T06:29:23Z)
• Employee Well-being in the Age of AI: Perceptions, Concerns, Behaviors, and Outcomes [0.0]
  The study examines how AI shapes employee perceptions, job satisfaction, mental health, and retention. Transparency in AI systems emerges as a critical factor in fostering trust and positive employee attitudes. The research introduces an AI-employee well-being Interaction Framework.
  arXiv  Detail & Related papers  (2024-12-06T06:07:44Z)
• Incentivized Communication for Federated Bandits [67.4682056391551]
  We introduce an incentivized communication problem for federated bandits, where the server shall motivate clients to share data by providing incentives. We propose the first incentivized communication protocol, namely, Inc-FedUCB, that achieves near-optimal regret with provable communication and incentive cost guarantees.
  arXiv  Detail & Related papers  (2023-09-21T00:59:20Z)
• Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks [71.19560970717495]
  Recent language models show impressive performance across a wide range of tasks. Are these skills general and transferable, or specialized to specific tasks seen during pretraining? We propose an evaluation framework based on "counterfactual" task variants that deviate from the default assumptions underlying standard tasks.
  arXiv  Detail & Related papers  (2023-07-05T17:50:42Z)
• Incentive Mechanism for Uncertain Tasks under Differential Privacy [17.058734221792964]
  Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This paper presents HERALD*, an incentive mechanism that addresses issues through the use of uncertainty and hidden bids.
  arXiv  Detail & Related papers  (2023-05-26T10:15:02Z)
• "How to make them stay?" -- Diverse Counterfactual Explanations of Employee Attrition [3.0839245814393728]
  Employee attrition is an important and complex problem that can directly affect an organisation's competitiveness and performance. Machine learning (ML) has been applied in various aspects of human resource management. This paper proposes the use of counterfactual explanations focusing on multiple attrition cases from historical data.
  arXiv  Detail & Related papers  (2023-03-08T13:54:57Z)
• Long-term dynamics of fairness: understanding the impact of data-driven targeted help on job seekers [1.357291726431012]
  We use an approach that combines statistics and machine learning to assess long-term fairness effects of labor market interventions. We develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.
  arXiv  Detail & Related papers  (2022-08-17T12:03:23Z)
• Optimising Equal Opportunity Fairness in Model Training [60.0947291284978]
  Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias. We propose two novel training objectives which directly optimise for the widely-used criterion of it equal opportunity, and show that they are effective in reducing bias while maintaining high performance over two classification tasks.
  arXiv  Detail & Related papers  (2022-05-05T01:57:58Z)
• Aligning to Social Norms and Values in Interactive Narratives [89.82264844526333]
  We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games. We introduce the GALAD agent that uses the social commonsense knowledge present in specially trained language models to contextually restrict its action space to only those actions that are aligned with socially beneficial values.
  arXiv  Detail & Related papers  (2022-05-04T09:54:33Z)
• Generative multitask learning mitigates target-causing confounding [61.21582323566118]
  We propose a simple and scalable approach to causal representation learning for multitask learning. The improvement comes from mitigating unobserved confounders that cause the targets, but not the input. Our results on the Attributes of People and Taskonomy datasets reflect the conceptual improvement in robustness to prior probability shift.
  arXiv  Detail & Related papers  (2022-02-08T20:42:14Z)
• Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach [131.20444904674494]
  We tackle the problem of measuring group fairness under unawareness of sensitive attributes. We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
  arXiv  Detail & Related papers  (2021-09-17T13:45:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.