Improving conditions or conditional improvements? A modern code, and mode, of I-O ethics

Three categories of modern dilemmas are immediately apparent in relation to reimagining and committing to a more proactive code in I-O, given their overlap with contemporary issues in technology and data management, health and accessibility, and justice, equity, diversity, and inclusion: ethical use of assessments (e.g., AI in selection);ethical conduct of research and data analysis;and ethical imperatives for fairness, inclusiveness, wellness, and equity in organizations, particularly in light of recent world events (e.g., the COVID-19 pandemic, social justice movements, inflation and economic challenges). Algorithms are then bound by the quality (and bias) incorporated into the data upon which they are trained and tested. [...]with the regulatory landscape in flux, it becomes even more important to utilize an ethical code to develop assessments. [...]relatedly, I-O psychologists who conduct research in organizations may not have regulatory oversight (e.g., institutional review board governance, peer review processes, expectations for pre-registration and open science practices) to guide their studies;if present and applicable, regulatory oversight may still be mismatched with organizational research, often ill fitted to certain types, disciplines, and contexts, or only focused on particular elements or stages of the research process while neglecting others (e.g., Bell & Wray-Bliss, 2009;Buchanan & Bryman, 2009;Greenwood, 2016). [...]I-O psychologists may also find themselves in ethical dilemmas as workers return to the office post pandemic. [...]proactive behavior is more likely and important within uncertain contexts, particularly those