Joachim Baumann
Biographic Data
| ID | 9030287 |
|---|---|
| NAME | Joachim Baumann |
| GIVEN NAMES | Joachim |
| FAMILY NAME | Baumann |
| SIGNATURE | BAUMANN J |
| AFFILIATIONS | University of Zurich |
| ORCID | 0000-0003-2019-4829 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A parametric LCA platform for modelling environmental impacts in plant protein processing
The central dietary role of proteins and the high environmental burden of animal-based food require a transition to more sustainable protein sources. Plant protein extracts are the primary commercial alternatives, yet their environmental impacts remain poorly quantified. We present the first regionalized life cycle assessment of soy, pea, and wheat proteins, based on a harmonized modelling platform to evaluate 19 global value chains supplying the…
On prediction-modelers and decision-makers
An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to th…
Fairness and Risk
Algorithmic predictions are promising for insurance companies to develop personalized risk models for determining premiums. In this context, issues of fairness, discrimination, and social injustice might arise: Algorithms for estimating the risk based on personal data may be biased towards specific social groups, leading to systematic disadvantages for those groups. Personalized premiums may thus lead to discrimination and social injustice. It is…
No prominent works on this page.
Fairness and Risk
Algorithmic predictions are promising for insurance companies to develop personalized risk models for determining premiums. In this context, issues of fairness, discrimination, and social injustice might arise: Algorithms for estimating the risk based on personal data may be biased towards specific social groups, leading to systematic disadvantages for those groups. Personalized premiums may thus lead to discrimination and social injustice. It is…
On prediction-modelers and decision-makers
An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to th…
A parametric LCA platform for modelling environmental impacts in plant protein processing
The central dietary role of proteins and the high environmental burden of animal-based food require a transition to more sustainable protein sources. Plant protein extracts are the primary commercial alternatives, yet their environmental impacts remain poorly quantified. We present the first regionalized life cycle assessment of soy, pea, and wheat proteins, based on a harmonized modelling platform to evaluate 19 global value chains supplying the…
Computer Science (2 works) · Ethics and Social Impacts of AI (2 works) · Actuarial science (1 work) · Adversarial Robustness in Machine Learning (1 work) · Agriculture Sustainability and Environmental Impact (1 work) · Artificial Intelligence (1 work) · Biorefining (1 work) · Business (1 work) · Climate change (1 work) · Economics (1 work)