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The purpose of this study is to examine whether and how analysts’ foreign ancestral origins would have an effect on analysts’ earning forecasts in particular and ultimately on firms’ information environment in general.

By inferring analysts’ ancestral countries based on their surnames, this study empirically examines whether analysts’ ancestral countries affect their earnings forecast errors.

Using novel data on analysts’ foreign ancestral origins from more than 110 countries, this study finds that relative to analysts with common American surnames, analysts with common foreign surnames tend to have higher earnings forecast errors. The positive relation between analyst foreign surnames and earnings forecast errors is more likely to be observed for African-American analysts and analysts whose ancestry countries are geographically apart from the USA. In contrast, this study finds that when analysts’ foreign countries of ancestry are aligned with that of the CEOs, analysts exhibit lower earnings forecast errors relative to analysts with common American surnames. More importantly, the results show that firms followed by more analysts with foreign surnames tend to exhibit higher earnings forecast errors.

Taken together, findings of this study are consistent with the conjecture that geographical, social and ethnical proximity between managers and analysts affect firms’ information environment. Therefore, this study contributes to the determinants of analysts’ earnings forecast errors and adds to the literature on firms’ information environment.

Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452