The Double-Edged Sword of Diversity: How Diversity, Conflict, and
Psychological Safety Impact Software Teams
- URL: http://arxiv.org/abs/2301.12954v2
- Date: Thu, 16 Nov 2023 08:47:46 GMT
- Title: The Double-Edged Sword of Diversity: How Diversity, Conflict, and
Psychological Safety Impact Software Teams
- Authors: Christiaan Verwijs and Daniel Russo
- Abstract summary: Team diversity can be seen as a double-edged sword, bringing cognitive resources to teams at the risk of increased conflict.
This study views diversity through the lens of the categorization-elaboration model (CEM)
We investigated how diversity in gender, age, role, and cultural background impacts team effectiveness and conflict.
- Score: 6.190511747986327
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Team diversity can be seen as a double-edged sword. It brings additional
cognitive resources to teams at the risk of increased conflict. Few studies
have investigated how different types of diversity impact software teams. This
study views diversity through the lens of the categorization-elaboration model
(CEM). We investigated how diversity in gender, age, role, and cultural
background impacts team effectiveness and conflict, and how these associations
are moderated by psychological safety. Our sample consisted of 1,118
participants from 161 teams and was analyzed with Covariance-Based Structural
Equation Modeling (CB-SEM). We found a positive effect of age diversity on team
effectiveness and gender diversity on relational conflict. Psychological safety
contributed directly to effective teamwork and less conflict but did not
moderate the diversity-effectiveness link. While our results are consistent
with the CEM theory for age and gender diversity, other types of diversity did
not yield similar results. We discuss several reasons for this, including
curvilinear effects, moderators such as task interdependence, or the presence
of a diversity mindset. With this paper, we argue that a dichotomous nature of
diversity is oversimplified. Indeed, it is a complex relationship where context
plays a pivotal role. A more nuanced understanding of diversity through the
lens of theories, such as the CEM, may lead to more effective teamwork.
Related papers
- Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups [29.720095331989064]
AI systems crucially rely on human ratings, but these ratings are often aggregated.
This is particularly concerning when evaluating the safety of generative AI, where perceptions and associated harms can vary significantly across socio-cultural contexts.
We conduct a large-scale study employing highly-parallel safety ratings of about 1000 text-to-image (T2I) generations from a demographically diverse rater pool of 630 raters.
arXiv Detail & Related papers (2024-10-22T13:59:21Z) - The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention [61.80236015147771]
We quantify the trade-off between using diversity interventions and preserving demographic factuality in T2I models.
Experiments on DoFaiR reveal that diversity-oriented instructions increase the number of different gender and racial groups.
We propose Fact-Augmented Intervention (FAI) to reflect on verbalized or retrieved factual information about gender and racial compositions of generation subjects in history.
arXiv Detail & Related papers (2024-06-29T09:09:42Z) - Diversity's Double-Edged Sword: Analyzing Race's Effect on Remote Pair Programming Interactions [0.5999777817331317]
Mixed-race pairs excelled in task distribution, shared decision-making, and role-exchange but encountered communication challenges, discomfort, and anxiety.
Our study emphasizes race's impact on remote pair programming and underscores the need for diverse tools and methods to address racial disparities for collaboration.
arXiv Detail & Related papers (2024-04-11T01:58:38Z) - Diversify Question Generation with Retrieval-Augmented Style Transfer [68.00794669873196]
We propose RAST, a framework for Retrieval-Augmented Style Transfer.
The objective is to utilize the style of diverse templates for question generation.
We develop a novel Reinforcement Learning (RL) based approach that maximizes a weighted combination of diversity reward and consistency reward.
arXiv Detail & Related papers (2023-10-23T02:27:31Z) - A Unified Theory of Diversity in Ensemble Learning [4.773356856466191]
We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios.
This challenge has been referred to as the holy grail of ensemble learning, an open research issue for over 30 years.
arXiv Detail & Related papers (2023-01-10T13:51:07Z) - Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome
Homogenization? [90.35044668396591]
A recurring theme in machine learning is algorithmic monoculture: the same systems, or systems that share components, are deployed by multiple decision-makers.
We propose the component-sharing hypothesis: if decision-makers share components like training data or specific models, then they will produce more homogeneous outcomes.
We test this hypothesis on algorithmic fairness benchmarks, demonstrating that sharing training data reliably exacerbates homogenization.
We conclude with philosophical analyses of and societal challenges for outcome homogenization, with an eye towards implications for deployed machine learning systems.
arXiv Detail & Related papers (2022-11-25T09:33:11Z) - Pick Your Battles: Interaction Graphs as Population-Level Objectives for
Strategic Diversity [49.68758494467258]
We study how to construct diverse populations of agents by carefully structuring how individuals within a population interact.
Our approach is based on interaction graphs, which control the flow of information between agents during training.
We provide evidence for the importance of diversity in multi-agent training and analyse the effect of applying different interaction graphs on the training trajectories, diversity and performance of populations in a range of games.
arXiv Detail & Related papers (2021-10-08T11:29:52Z) - How diverse is the ACII community? Analysing gender, geographical and
business diversity of Affective Computing research [0.0]
ACII is the premier international forum for presenting the latest research on affective computing.
We measure diversity in terms of gender, geographic location and academia vs research centres vs industry, and consider three different actors: authors, keynote speakers and organizers.
Results raise awareness on the limited diversity in the field, in all studied facets, and compared to other AI conferences.
arXiv Detail & Related papers (2021-09-12T18:30:36Z) - Unifying Behavioral and Response Diversity for Open-ended Learning in
Zero-sum Games [44.30509625560908]
In open-ended learning algorithms, there are no widely accepted definitions for diversity, making it hard to construct and evaluate the diverse policies.
We propose a unified measure of diversity in multi-agent open-ended learning based on both Behavioral Diversity (BD) and Response Diversity (RD)
We show that many current diversity measures fall in one of the categories of BD or RD but not both.
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
arXiv Detail & Related papers (2021-06-09T10:11:06Z) - Simultaneous Relevance and Diversity: A New Recommendation Inference
Approach [81.44167398308979]
We propose a new approach, which extends the general collaborative filtering (CF) by introducing a new way of CF inference, negative-to-positive.
Our approach is applicable to a wide range of recommendation scenarios/use-cases at various sophistication levels.
Our analysis and experiments on public datasets and real-world production data show that our approach outperforms existing methods on relevance and diversity simultaneously.
arXiv Detail & Related papers (2020-09-27T22:20:12Z)
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.