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In this contribution to EDI's professional insights, the authors develop practical and evidence-based recommendations that are developed for bias mitigation, discretion elimination and process optimization in panel evaluations and decisions in research funding. An analysis is made of how the expectation of “selling science” adds layers of complexity to the evaluation and decision process. The insights are relevant for optimization of similar processes, including publication, recruitment and selection, tenure and promotion.

The recommendations are informed by experiences and evidence from commissioned projects with European research funding organizations. The authors distinguish between three aspects of the evaluation process: written applications, enacted performance and group dynamics. Vignettes are provided to set the stage for the analysis of how bias and (lack of) fit to an ideal image makes it easier for some than for others to be funded.

In research funding decisions, (over)selling science is expected but creates shifting standards for evaluation, resulting in a narrow band of acceptable behavior for applicants. In the authors' recommendations, research funding organizations, evaluators and panel chairs will find practical ideas and levers for process optimization, standardization and customization, in terms of awareness, accountability, biased language, criteria, structure and time.

Showing how “selling science” in research funding adds to the cumulative disadvantage of bias, the authors offer design specifications for interventions to mitigate the negative effects of bias on evaluations and decisions, improve selection habits, eliminate discretion and create a more inclusive process.

The operational capability of mass customization (MC) allows consumers to obtain products tailored to their idiosyncratic needs. This study aims to provide insights into the potential of this capability for countering a product's liability of foreignness – the negative effect of the out-group status of a product's country of origin (COO) on consumers' evaluations of the product.

Based on the social identity approach, it is hypothesized that this liability is reduced when a consumer product is mass-customized rather than standardized as per a mass-production strategy. This hypothesis is tested using a mixed between- and within-subject experiment.

When evaluating mass-produced sneakers, native German-speaking (Italian-speaking) South Tyrolean consumers rated the quality of Italian (German) sneakers significantly lower than that of German (Italian) sneakers. However, when the sneakers were mass-customized, this difference in perceived product quality was non-significant for both groups of consumers, supporting the research hypothesis.

Future research could replicate this study in other samples, with other product types, COOs and countries of destination, as well as at different degrees of product customization.

Business-to-consumer firms contemplating the development of their MC capability are made aware that the benefits of this operational capability might go beyond the typical advantages highlighted by the existing literature.

This paper joins the discussion on MC value by offering a theoretical explanation and empirical support for another mechanism through which the operational capability of MC can create value, at least in business-to-consumer industries: by countering a product's possible liability of foreignness and thus increasing perceived product quality in export markets.

As adaptation and mitigation are separated in international and national policies, there is also a division in the financial resources mobilized by the international community to help developing countries deal with climate change. Given that mitigation activities can benefit or hinder adaptation, and vice versa, promoting activities that contribute to both objectives can increase the efficiency of fund allocation and minimize trade-offs, particularly in land-related activities such as agriculture and forestry. The purpose of this study is to analyze how climate funding organizations consider the integration of adaptation and mitigation.

The authors interviewed representatives of climate funds directed toward forestry and agriculture to gain a better understanding of how they perceive the benefits, risks and barriers of an integrated approach; whether they have concrete activities for promoting this approach; and how they foresee the future of adaptation–mitigation integration.

Interviews revealed a diverse range of perceived benefits, risks and barriers at local, national and global scales. Most interviewees focused on the local benefits of this integration (e.g. increasing the resilience of forest carbon projects), whereas others emphasized global risks (e.g. decreasing global funding efficiency because of project complexity). Despite the general interest in projects and policies integrating adaptation and mitigation, few relevant actions have been implemented by organizations engaged in climate change finance.

This paper provides new insight into how the representatives of climate funds perceive and act on the integration of adaptation and mitigation in forestry and agriculture. The findings by the authors can inform the development of procedures for climate change finance, such as the Green Climate Fund. While managers of climate funds face barriers in promoting an integrated approach to adaptation and mitigation, they also have the capacity and the ambition to overcome them.

This systematic literature review focuses on the following future advanced information and communication technologies (ICTs) applied in the maritime transport of cargo: Internet of Things (IoT), big data, cloud computing and autonomous ships/vessels (including unmanned ships/vessels). The review question is: “RQ: In what context and by means of what mechanism does the implementation of future advanced ICTs have disruptive impact on maritime transport?”.

The paper complies with the methodological requirements of systematic reviews. The information analysis and synthesis are based on the CIMO logic, referring to the context (C), intervention (I), mechanism (M) and outcome (O) of the implementation of future advanced ICTs in maritime transport.

The review identifies the contextual factors and components of the mechanism that lead to the disruptive impact of different types of future advanced ICT interventions on maritime transport.

The review approaches only the most important future advanced ICTs that will disrupt maritime transport.

The maritime transport organizations should consider: intended outcome as intervention trigger; increased efficiency and responsiveness; benchmarking.

For the first time, the CIMO logic is applied in a systematic review focused on future advanced ICTs in maritime transport. The CIMO-DMT model is elaborated as a basis for further research. Ten directions of study are recommended in a future research agenda.

The paper aims to expand on the works well documented by Joy Boulamwini and Ruha Benjamin by expanding their critique to the African continent. The research aims to assess if algorithmic biases are prevalent in DALL-E 2 and Starry AI. The aim is to help inform better artificial intelligence (AI) systems for future use.

The paper utilised a desktop study for literature and gathered data from Open AI’s DALL-E 2 text-to-image generator and StarryAI text-to-image generator.

The DALL-E 2 significantly underperformed when it was tasked with generating images of “An African Family” as opposed to images of a “Family”. The pictures lacked any conceivable detail as compared to the latter of this comparison. The StarryAI significantly outperformed the DALL-E 2 and rendered visible faces. However, the accuracy of the culture portrayed was poor.

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further. The implications, however, are that more inclusion is warranted to help address the issue of cultural inaccuracies noted in a few of the paper’s experiments.

The paper is useful for advocates who advocate for algorithmic equality and fairness by highlighting evidence of the implications of systemic-induced algorithmic bias.

The reduction in offensive racism and more socially appropriate AI can be a better product for commercialisation and general use. If AI is trained on diversity, it can lead to better applications in contemporary society.

The paper’s use of DALL-E 2 and Starry AI is an under-researched area, and future studies on this matter are welcome.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

This paper presents emerging findings from an ongoing research project which aimed to explore online lesson study (OLS) as a vehicle for teacher collaborative professional learning.

Two parallel OLS cycles with two OLS teams were facilitated by the author using Zoom and Google Drive as digital collaborative tools. Each OLS team comprised three primary teachers who taught in three different schools, with both teams' research lessons taking cross-curricular science, technology, engineering and maths (STEM) focus. In order to explore the influence of OLS on teachers' collaborative professional learning outcomes in STEM, a qualitative case study approach was adopted, with data drawn and thematically analysed from OLS meeting transcripts, semi-structured interviews with teachers and the author's reflective diary. Boundary crossing is used as a theoretical lens to ascertain the potential of OLS as a vehicle for teacher collaborative professional learning.

Findings suggest that OLS facilitated collaborative learning and positively contributed to teacher participants' co-construction of knowledge in relation to STEM teaching approaches.

The study described in this paper represents the first OLS conducted in the Irish context and also represents the first inter-school lesson study (LS) conducted in the Irish primary context.