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1. Space physics guide to STRIDE: Strategies and tactics for recruiting to improve diversity and excellence

   https://doi.org/10.3389/fspas.2023.1152567  

   Liemohn, Michael W.; Linderman, Jennifer J.; Settles, Isis H. (March 2023, Frontiers in Astronomy and Space Sciences)

   The space physics research community is not diverse. This is especially true at the senior experience levels, but is even true for our student populations, which are also not matching the demographics of the general public. Striving towards a demographic shift to match the general population promotes equity and inclusion. In addition, diversity increases research productivity. Unfortunately, bias exists, including within the space physics research community, and this negatively impacts hiring practices and perpetuates the demographic mismatch. Yet there are many strategies and tactics that can be adopted to counter this problem. A number of these methods are presented and discussed, specifically those regarding the search process for hiring new research group members. The key methods for achieving an equitable search process are as follows: develop a holistic rubric early, even before the job ad is posted; slow down the downselect from the full applicant pool to the short list of finalists so that the rubric can be carefully applied to each candidate; make the interview process as equitable as possible by considering the ways in which it could be biased; and conduct a fair decision-making process that focuses on the job-relevant criteria and avoids global rankings until the final vote. 

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2. Human Computation, Equitable, and Innovative Future of Work AI Tools

   https://doi.org/10.1609/hcomp.v12i1.31611  

   Imteyaz, Kashif; Flores_Saviaga, Claudia; Savage, Saiph (October 2024, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   As we enter an era where the synergy between AI technologies and human effort is paramount, the Future of Work is undergoing a radical transformation. Emerging AI tools will profoundly influence how we work, the tools we use, and the very nature of work itself. The ’Human Computation, Equitable, and Innovative Future of Work AI Tools’ workshop at HCOMP’24 aims to explore groundbreaking solutions for developing fair and inclusive AI tools that shape how we will work. This workshop will delve into the collaborative potential of human computation and artificial intelligence in crafting equitable Future of Work AI tools. Participants will critically examine the current challenges in designing fair and innovative AI systems for the evolving workplace, as well as strategies for effectively integrating human insights into these tools. The primary objective is to foster a rich discourse on scalable, sustainable solutions that promote equitable Future of Work tools for all, with a particular focus on empowering marginalized communities. By bringing together experts from diverse fields, we aim to catalyze ideas that bridge the gap between technological advancement and social equity. 

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3. Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency

   https://doi.org/10.1609/aaai.v38i20.30211  

   Haghighat, Parian; Gándara, Denisa; Kang, Lulu; Anahideh, Hadis (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Predictive analytics has been widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomprehensible to the officials who use them, reducing their trust and utility. Furthermore, predictive models may introduce or exacerbate bias and inequity, as they have done in many sectors of society. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. In this paper, we propose a fair predictive model based on multivariate adaptive regression splines (MARS) that incorporates fairness measures in the learning process. MARS is a non-parametric regression model that performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on the variables. Specifically, we integrate fairness into the knot optimization algorithm and provide theoretical and empirical evidence of how it results in a fair knot placement. We apply our fairMARS model to real-world data and demonstrate its effectiveness in terms of accuracy and equity. Our paper contributes to the advancement of responsible and ethical predictive analytics for social good. 

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4. Multi‐hazard probabilistic risk assessment and equitable multi‐objective optimization of building retrofit strategies in hurricane‐vulnerable communities

   https://doi.org/10.1111/mice.13445  

   Braik, Abdullah M; Gupta, Himadri Sen; Koliou, Maria; González, Andrés D (June 2025, Computer-Aided Civil and Infrastructure Engineering)

   Abstract Coastal communities are increasingly vulnerable to hurricanes, which cause billions of dollars in damage annually through wind, storm surge, and flooding. Mitigation efforts are essential to reduce these impacts but face significant challenges, including uncertainties in hazard prediction, damage estimation, and recovery costs. Resource constraints and the disproportionate burden borne by socioeconomically vulnerable groups further complicate retrofitting strategies. This study presents a probabilistic methodology to assess and mitigate hurricane risks by integrating hazard analysis, building fragility, and economic loss assessment. The methodology prioritizes retrofitting strategies using a risk‐informed, equity‐focused approach. Multi‐objective optimization balances cost‐effectiveness and risk reduction while promoting fair resource allocation among socioeconomic groups. The novelty of this study lies in its direct integration of equity as an objective in resource allocation through multi‐objective optimization, its comprehensive consideration of multi‐hazard risks, its inclusion of both direct and indirect losses in cost assessments, and its use of probabilistic hazard analysis to incorporate varying time horizons. A case study of the Galveston testbed demonstrates the methodology's potential to minimize damage and foster equitable resilience. Analysis of budget scenarios and trade‐offs between cost and equity underscores the importance of comprehensive loss assessments and equity considerations in mitigation and resilience planning. Key findings highlight the varied effectiveness of retrofitting strategies across different budgets and time horizons, the necessity of addressing both direct and indirect losses, and the importance of multi‐hazard considerations for accurate risk assessments. Multi‐objective optimization underscores that equitable solutions are achievable even under constrained budgets. Beyond a certain point, achieving equity does not necessarily increase expected losses, demonstrating that more equitable solutions can be implemented without compromising overall cost‐effectiveness. 

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5. Understanding the Interrelationships between Infrastructure Resilience and Social Equity Using Social Media Data

   https://doi.org/10.1061/9780784482858.065  

   Dhakal, Sunil; Zhang, Lu (November 2020, Construction Research Congress 2020)

   The 2030 Global Sustainable Development Agenda of United Nations highlighted the critical importance of understanding the integrated nature between enhancing infrastructure resilience and facilitating social equity. Social equity is defined as equal opportunities provided to different people by infrastructure. It addresses disparities and unequal distribution of goods, services, and amenities. Infrastructure resilience is defined as the ability of infrastructure to withstand, adapt, and quickly recover from disasters. Existing research shows that infrastructure resilience and social equity are closely related to each other. However, there is a lack of research that explicitly understands the complex relationships between infrastructure resilience and social equity. To address this gap, this study aims to examine such interrelationships using social media data. Social media data is increasingly being used by researchers and proven to be a reliable source of valuable information for understanding human activities and behaviors in a disaster setting. The spatiotemporal distribution of disaster-related messages helps with real-time and quick assessment of the impact of disasters on infrastructure and human society across different regions. Using social media data also offers the advantages of saving time and cost, compared to other traditional data collection methods. As a first step of this study, this paper presents our work on collecting and analyzing the Twitter activities during 2018 Hurricane Michael in disaster-affected counties of Florida Panhandle area. The collected Twitter data was organized based on the geolocations of affected counties and was compared against the infrastructure resilience and social equity data of the affected counties. The results of the analysis indicate that (1) Twitter activities can be used as an important indicator of infrastructure resilience conditions, (2) socially vulnerable populations are not as active as general populations on social media in a disaster setting, and (3) vulnerable populations require a longer time for disaster recovery. 

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