Sina Fazelpour
Biographic Data
| ID | 384587 |
|---|---|
| NAME | Sina Fazelpour |
| GIVEN NAMES | Sina |
| FAMILY NAME | Fazelpour |
| SIGNATURE | FAZELPOUR S |
| AFFILIATIONS | Northeastern University |
| ORCID | 0000-0002-4447-318X |
| VERIFIED | Yes |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 43 |
| AUTHOR COUNT | 10 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Navigating Epistemic Monocultures in AI-Driven Science: A Simulation Study
AI integration into scientific communities promises accelerated discovery but raises concerns about detrimental homogenization. We develop an NK landscape model to explore these promises and risks.We find that non-personalized AI systems that offer uniform guidance yield benefits only under a narrow conjunction of problem structure, practices, and baseline research capabilities, becoming harmful otherwise. We implement two proposed mitigations: r…
Authenticity and exclusion: A simulation study of how social media algorithms shape visibility in epistemic communities
Recent philosophical work has explored how the social identity of knowers influences how their contributions are received, assessed, and credited. However, a critical gap remains regarding the role of technology in mediating and enabling communication within today’s epistemic communities. This paper addresses this gap by examining how social media platforms and their recommendation algorithms shape the professional visibility and opportunities of…
Disciplining Deliberation: A Socio-technical Perspective on Machine Learning Trade-Offs
Algorithmic Fairness and the Situated Dynamics of Justice
Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. W…
Diversity, Trust, and Conformity: A Simulation Study
Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distin…
Diversity in sociotechnical machine learning systems
There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, differe…
Information elaboration and epistemic effects of diversity
We suggest that philosophical accounts of epistemic effects of diversity have given insufficient attention to the relationship between demographic diversity and information elaboration (IE), the process whereby knowledge dispersed in a group is elicited and examined. We propose an analysis of IE that clarifies hypotheses proposed in the empirical literature and their relationship to philosophical accounts of diversity effects. Philosophical accou…
Algorithmic bias: Senses, sources, solutions
Data-driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so-called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm-b…
Norms in Counterfactual Selection
In the hopes of finding supporting evidence for various accounts of actual causation, many philosophers have recently turned to psychological findings about the influence of norms on counterfactual cognition. Surprisingly little philosophical attention has been paid, however, to the question of why considerations of normality should be relevant to counterfactual cognition to begin with. In this paper, I follow two aims. First, against the methodo…
Attention in the predictive mind
Algorithmic bias: Senses, sources, solutions
Data-driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so-called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm-b…
Diversity, Trust, and Conformity: A Simulation Study
Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distin…
Information elaboration and epistemic effects of diversity
We suggest that philosophical accounts of epistemic effects of diversity have given insufficient attention to the relationship between demographic diversity and information elaboration (IE), the process whereby knowledge dispersed in a group is elicited and examined. We propose an analysis of IE that clarifies hypotheses proposed in the empirical literature and their relationship to philosophical accounts of diversity effects. Philosophical accou…
Attention in the predictive mind
Norms in Counterfactual Selection
In the hopes of finding supporting evidence for various accounts of actual causation, many philosophers have recently turned to psychological findings about the influence of norms on counterfactual cognition. Surprisingly little philosophical attention has been paid, however, to the question of why considerations of normality should be relevant to counterfactual cognition to begin with. In this paper, I follow two aims. First, against the methodo…
Information elaboration and epistemic effects of diversity
We suggest that philosophical accounts of epistemic effects of diversity have given insufficient attention to the relationship between demographic diversity and information elaboration (IE), the process whereby knowledge dispersed in a group is elicited and examined. We propose an analysis of IE that clarifies hypotheses proposed in the empirical literature and their relationship to philosophical accounts of diversity effects. Philosophical accou…
Algorithmic bias: Senses, sources, solutions
Data-driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so-called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm-b…
Algorithmic Fairness and the Situated Dynamics of Justice
Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. W…
Diversity, Trust, and Conformity: A Simulation Study
Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distin…
Diversity in sociotechnical machine learning systems
There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, differe…
Authenticity and exclusion: A simulation study of how social media algorithms shape visibility in epistemic communities
Recent philosophical work has explored how the social identity of knowers influences how their contributions are received, assessed, and credited. However, a critical gap remains regarding the role of technology in mediating and enabling communication within today’s epistemic communities. This paper addresses this gap by examining how social media platforms and their recommendation algorithms shape the professional visibility and opportunities of…
Disciplining Deliberation: A Socio-technical Perspective on Machine Learning Trade-Offs
Navigating Epistemic Monocultures in AI-Driven Science: A Simulation Study
AI integration into scientific communities promises accelerated discovery but raises concerns about detrimental homogenization. We develop an NK landscape model to explore these promises and risks.We find that non-personalized AI systems that offer uniform guidance yield benefits only under a narrow conjunction of problem structure, practices, and baseline research capabilities, becoming harmful otherwise. We implement two proposed mitigations: r…
Computer Science (7 works) · Epistemology (7 works) · Psychology (6 works) · Artificial Intelligence (5 works) · Ethics and Social Impacts of AI (5 works) · Sociology (5 works) · Economics (4 works) · Management science (4 works) · Philosophy (4 works) · Artificial Intelligence (3 works)