Gloss, Carr, Reichman, Abdul-Nasiru, and Oestereich (2017) present a compelling argument (or rallying call) for there being a “moral imperative for I-O psychology to overrepresent people living in the deepest forms of poverty in both science and practice” (p. 330). We agree. Our research has been dominated by a POSH perspective, and it is incumbent upon us to ensure that our science benefits those who are most affected by poverty. We believe the interest in engaging in humanitarian work psychology is growing among industrial and organizational (I-O) psychologists, yet many of us may not feel prepared to conduct such research and/or we may feel that we lack the skills to do so. Further, as Gloss et al. (2017) note, to the extent that we are unprepared to engage in research that benefits those living in poverty, in particular, we run the added risk of harming the very populations we are wanting to help. As such, the interest is there, but we may be daunted by the method. We argue that in order to heed that rallying call, without harm, we need to develop our own capabilities to engage in this important work.

Academics sometimes forget that the purpose of a university is to educate: our students, our local communities, each other, and the world. Although each university is unique in its constituency, all share the charge to generate knowledge for the protection and benefit of the public good. The goal of an academic should be to beneficially impact society, broadly defined, with scholarly activity. As editor and columnist for The Industrial-Organizational Psychologist, one publication highlighted by the focal article, we applaud the efforts of Aguinis et al. (2017) to put forth alternative approaches to defining impact. Like them, we are concerned that many of the measures of “impact” we currently use do not capture this charge.

Jones, Arena, Nittrouer, Alonso, and Lindsey (2017) make the case that discrimination is multifaceted and can be identified along several continua. They also emphasize the role that every individual may play in the propagation of discrimination. As such, they make note of several interventions from bystanders and allies to combat subtle discrimination. Although we agree that subtle discrimination causes harm and that interventions targeted at such discrimination are necessary, we propose some additional considerations for the science and practice of subtle discrimination reduction. Specifically, we discuss the limitations of focusing on subtle discrimination at the individual level, the ambiguous nature of intentionality, the view of subtle discrimination as a manifestation of a hostile environment that falls under the broader umbrella of negative interpersonal treatment, and the emphasis placed by Jones et al. on the potential for organizational level interventions by proposing several considerations for tackling a climate of negative interpersonal treatment.

In their article, Ree, Carretta, and Teachout (2015) argued that a dominant general factor (DGF) is present in most, if not all, psychological measures (e.g., personality, leadership, attitudes, skills). A DGF, according to Ree et al., is identified by two characteristics. First, the DGF accounts for the largest amount of a measure's systematic variance, and second, it influences every subdimension within the construct domain. They indicate that researchers ignore DGFs and pay inappropriate amounts of attention to the specific dimensions (DSs) even though the DGF provides most of the predictive power and the DS adds little predictive power.

Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important issue in evaluating whether our current approaches for key driver analysis on employee opinion survey data are indeed best practices. As has been argued elsewhere (Putka & Oswald, 2016; Scherbaum, Putka, Naidoo, & Youssefnia, 2010), there is and can be misalignment between current and best practices. We agree with Cucina et al. that our field should engage in larger discussion of these issues. That discussion is critical, as industrial and organizational (I-O) psychologists are competing with those outside our field who have either little knowledge of best practices in data analysis (but who have been empowered by technology that automates the analysis) or little knowledge of psychology (but a great deal of knowledge in big data analytical techniques). I-O psychologists are in the vanguard of survey data analysis (Ducey et al., 2015), and we have a responsibility to maintain the standards of our field as well as to wield our influence to guide other practitioners outside our field on sound theoretical and analytical approaches.

In recent years, there has been an alarming increase in both proposed and enacted legislation that targets diversity, equity, and inclusion (DEI) initiatives within higher education and organizations. These laws aim to dismantle protections and support for vulnerable individuals and groups. In this focal article, we provide an overview of the motives driving these anti-DEI legislative initiatives and categorize the laws based on the type of restrictions they impose: (a) reduction in knowledge, (b) reduction in access, and (c) reduction in support. Next, we discuss the consequences these anti-DEI laws yield for individuals, organizations, and society at large and provide an overview of how individuals and organizations may counteract these regressive policies. Last, we conclude with a call to action for I-O psychologists to investigate and call attention to the consequences of anti-DEI laws for recruitment and selection, well-being and safety of minoritized individuals, organizational reputation, and organizational performance and profits.