Understanding the future consequences of artificial intelligence requires a holistic consideration of its cultural dimensions, on par with its technological intricacies and potential applications. Individuals and institutions working closely with AI, and with considerable resources, have significant influence on how impact is considered, particularly with regard to how much attention is paid to epistemic concerns (including issues of bias in datasets or potential misinterpretations of data, for example) versus normative concerns (such as societal and ecological effects of AI in the medium- and long-term). In this paper we review qualitative studies conducted with AI researchers and developers to understand how they position themselves relative to each of these two dimensions of impact, and how geographies and conditions of work influence their positions. Our findings underscore the need to gather more perspectives from low- and middle-income countries, whose notions of impact extend beyond the immediate technical concerns or impacts in the short- to medium-term. Rather, they encapsulate a broader spectrum of impact considerations, including the deleterious effects perpetrated by global corporate entities, the unwarranted influence of wealthy nations, the encroachment of philanthrocapitalism, and the adverse consequences of excluding communities affected by these phenomena from active participation in discussions surrounding impact.

Social Networking Sites are home to different forms of hate, including "Misogynoir", which specifically targets Black women through a combination of racism and sexism. Detecting misogynoir presents challenges due to its subjective nature and the varied interpretations of hate speech. Using annotator justifications from four distinct demographic groups; including Black women, Black men, White women and White men, we seek to gain a deeper understanding of the factors that influence annotators' reasoning process and labelling decisions for potential cases of Misogynoir and Allyship. Given the unique experiences of Black women who face both racism and sexism, the study sought to understand how their intersectional identities shape their perspectives compared to other groups. The research employed a qualitative analysis of responses from participants to identify key themes and patterns. Three significant themes emerged from our in-depth qualitative analysis of these annotator justifications: prior knowledge and experience, the language of the social media post, and its context. Our results revealed that annotators historically at risk of abuse demonstrated a nuanced understanding of how their intersecting identities inform their interpretations and judgement of tweets, drawing on their personal encounters with misogyny and racism compared to their non-target counterparts of this type of hate. This study underscores the significance of diverse annotator perspectives and content comprehension in understanding and addressing hate speech, particularly when it intersects with multiple forms of discrimination. Our study contributes to the methodological advancements in social network analysis and mining, highlighting the importance of considering annotator characteristics in the development of tools and approaches for detecting and addressing intersectional hate.

"Misogynoir" refers to the specific forms of misogyny that Black women experience, which couple racism and sexism together. To better understand the online manifestations of this type of hate, and to propose methods that can automatically identify it, in this paper, we conduct a study on 4 cases of Black women in Tech reporting experiences of misogynoir on the Twitter platform. We follow the reactions to these cases (both supportive and non-supportive responses), and categorise them within a model of misogynoir that highlights experiences of Tone Policing, White Centring, Racial Gaslighting and Defensiveness. As an intersectional form of abusive or hateful speech, we investigate the possibilities and challenges to detect online instances of misogynoir in an automated way. We then conduct a closer qualitative analysis on messages of support and non-support to look at some of these categories in more detail. The purpose of this investigation is to understand responses to misogynoir online, including doubling down on misogynoir, engaging in performative allyship, and showing solidarity with Black women in tech.