Netta Avnoon
Dados Biográficos
| ID | 296543 |
|---|---|
| NOME | Netta Avnoon |
| PRENOMES | Netta |
| SOBRENOME | Avnoon |
| ASSINATURA | AVNOON N |
| AFILIAÇÕES | Ben-Gurion University of the Negev |
| ORCID | 0000-0002-3758-2097 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 9 |
| TOTAL DE CITAÇÕES | 27 |
| TOTAL COMO AUTOR | 9 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2021 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 2 |
Ethnographies of Human‐AI Collaborations
In this review, I outline four arrangements on the spectrum of human experts‐AI collaboration, as studied from an ethnographic, grounded perspective by sociology, management, and STS scholars. Starting with the sociology of expertise's definitions of what constitutes “expert work,” I first review cases in which AI almost replaced human expertise, those in which AI aims for ‘no human in the loop,’ offering reasons why such complete automation has …
It’s not a bug, it’s a feature
The opacity of machine learning (ML) algorithms is a significant concern in academic and regulatory circles. An emergent sociology of algorithms, however, argues that far from opacity being an inherent quality of algorithms, it is socially constructed and contingent upon certain choices and decisions. In this article, we show that a valorization of opacity is a key component of the epistemic culture of ML experts. While earlier campaigns for mech…
Prediction and explainability in AI
The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction an…
Replace, absorb, serve
How do data scientists frame their relations with domain experts? This study focuses on data scientists' aspired professional jurisdiction and their multiple narratives regarding data science's relations to other fields of expertise. Based on the analysis of 60 open-ended, in-depth interviews with data scientists, data science professors, and managers in Israel, the findings show that data scientists institutionalize three narratives regarding th…
The gates to the profession are open
Nothing new under the sun
This paper follows the reaction of the radiology profession to artificial intelligence (AI). We examine the effort of radiology as a powerful medical specialty to maintain its professional jurisdiction while allowing AI's disruption. We study the discursive work of radiologists as evident in their academic publications. Our results suggest that radiologists hold simultaneously multiple perspectives in regard to AI, which allow them to be both con…
Contextualizing the ethics of algorithms
Research on AI ethics tends to examine the subject through philosophical, legal, or technical perspectives, largely neglecting the sociocultural one. This literature also predominantly focuses on Europe and the United States. Addressing these gaps, this article explores how data scientists justify and explain the ethics of their algorithmic work. Based on a pragmatist social analysis, and of 60 semi-structured interviews with Israeli data scienti…
Contextualizing Ai Ethics in Time and Space
While the interest in AI ethics has overwhelmingly intensified over the last decade, and while various initiatives seek its institutionalization, the literature on algorithmic ethics tends to examine the subject through philosophical, legal, or technocratic perspectives, largely neglecting the empirical, socio-cultural ones. Moreover, this literature tends to focus on the United States, and to overlook other tech centers around the world. This pa…
Data Scientists' Identity Work
Drawing on theories from the sociology of work and the sociology of culture, this article argues that members of nascent technical occupations construct their professional identity and claim status through an omnivorous approach to skills acquisition. Based on a discursive analysis of 56 semi-structured in-depth interviews with data scientists, data science professors and managers in Israel, it was found that data scientists mobilise the followin…
Data Scientists' Identity Work
Drawing on theories from the sociology of work and the sociology of culture, this article argues that members of nascent technical occupations construct their professional identity and claim status through an omnivorous approach to skills acquisition. Based on a discursive analysis of 56 semi-structured in-depth interviews with data scientists, data science professors and managers in Israel, it was found that data scientists mobilise the followin…
Contextualizing the ethics of algorithms
Research on AI ethics tends to examine the subject through philosophical, legal, or technical perspectives, largely neglecting the sociocultural one. This literature also predominantly focuses on Europe and the United States. Addressing these gaps, this article explores how data scientists justify and explain the ethics of their algorithmic work. Based on a pragmatist social analysis, and of 60 semi-structured interviews with Israeli data scienti…
Replace, absorb, serve
How do data scientists frame their relations with domain experts? This study focuses on data scientists' aspired professional jurisdiction and their multiple narratives regarding data science's relations to other fields of expertise. Based on the analysis of 60 open-ended, in-depth interviews with data scientists, data science professors, and managers in Israel, the findings show that data scientists institutionalize three narratives regarding th…
Prediction and explainability in AI
The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction an…
The gates to the profession are open
Contextualizing Ai Ethics in Time and Space
While the interest in AI ethics has overwhelmingly intensified over the last decade, and while various initiatives seek its institutionalization, the literature on algorithmic ethics tends to examine the subject through philosophical, legal, or technocratic perspectives, largely neglecting the empirical, socio-cultural ones. Moreover, this literature tends to focus on the United States, and to overlook other tech centers around the world. This pa…
Data Scientists' Identity Work
Drawing on theories from the sociology of work and the sociology of culture, this article argues that members of nascent technical occupations construct their professional identity and claim status through an omnivorous approach to skills acquisition. Based on a discursive analysis of 56 semi-structured in-depth interviews with data scientists, data science professors and managers in Israel, it was found that data scientists mobilise the followin…
Nothing new under the sun
This paper follows the reaction of the radiology profession to artificial intelligence (AI). We examine the effort of radiology as a powerful medical specialty to maintain its professional jurisdiction while allowing AI's disruption. We study the discursive work of radiologists as evident in their academic publications. Our results suggest that radiologists hold simultaneously multiple perspectives in regard to AI, which allow them to be both con…
Contextualizing the ethics of algorithms
Research on AI ethics tends to examine the subject through philosophical, legal, or technical perspectives, largely neglecting the sociocultural one. This literature also predominantly focuses on Europe and the United States. Addressing these gaps, this article explores how data scientists justify and explain the ethics of their algorithmic work. Based on a pragmatist social analysis, and of 60 semi-structured interviews with Israeli data scienti…
Prediction and explainability in AI
The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction an…
Replace, absorb, serve
How do data scientists frame their relations with domain experts? This study focuses on data scientists' aspired professional jurisdiction and their multiple narratives regarding data science's relations to other fields of expertise. Based on the analysis of 60 open-ended, in-depth interviews with data scientists, data science professors, and managers in Israel, the findings show that data scientists institutionalize three narratives regarding th…
The gates to the profession are open
Ethnographies of Human‐AI Collaborations
In this review, I outline four arrangements on the spectrum of human experts‐AI collaboration, as studied from an ethnographic, grounded perspective by sociology, management, and STS scholars. Starting with the sociology of expertise's definitions of what constitutes “expert work,” I first review cases in which AI almost replaced human expertise, those in which AI aims for ‘no human in the loop,’ offering reasons why such complete automation has …
It’s not a bug, it’s a feature
The opacity of machine learning (ML) algorithms is a significant concern in academic and regulatory circles. An emergent sociology of algorithms, however, argues that far from opacity being an inherent quality of algorithms, it is socially constructed and contingent upon certain choices and decisions. In this article, we show that a valorization of opacity is a key component of the epistemic culture of ML experts. While earlier campaigns for mech…
Sociology (7 obras) · Computer Science (6 obras) · Political science (6 obras) · Engineering ethics (5 obras) · Law (5 obras) · Artificial Intelligence in Healthcare and Education (3 obras) · Epistemology (3 obras) · Ethics and Social Impacts of AI (3 obras) · Politics (3 obras) · Public relations (3 obras)