Franziska Sofia Hafner
Biographic Data
| ID | 8547363 |
|---|---|
| NAME | Franziska Sofia Hafner |
| GIVEN NAMES | Franziska Sofia |
| FAMILY NAME | Hafner |
| SIGNATURE | HAFNER F S |
| AFFILIATIONS | American University |
| ORCID | 0000-0003-1070-8267 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
‘Slightly disappointing’ vs. ‘worst sh** ever’
Advertisers, politicians, and social scientists alike have an interest in the current zeitgeist. With people expressing their sentiments in online comments, the Internet has become a great source for ‘reading’ the zeitgeist via artificial intelligence-based sentiment analysis (SA). At this, negative sentiments are of special concern. They can serve as early indicators for events that require action, such as dropping customer satisfaction, discont…
Equal accuracy for Andrew and Abubakar—detecting and mitigating bias in name-ethnicity classification algorithms
Uncovering the world’s ethnic inequalities is hampered by a lack of ethnicity-annotated datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people’s ethnicities from their names. However, since the latest generation of NECs rely on machine learning and artificial intelligence (AI), they may suffer from the same racist and sexist biases found in many AIs. Therefore, this paper offers an algorithmic fairness audit of thr…
Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design
Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased against some demographic groups, based on social attributes such as gender, age, and ethnicity. Sentiment classification works dealt with this issue by means of data balancing and algorithmic approaches. However,…
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Equal accuracy for Andrew and Abubakar—detecting and mitigating bias in name-ethnicity classification algorithms
Uncovering the world’s ethnic inequalities is hampered by a lack of ethnicity-annotated datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people’s ethnicities from their names. However, since the latest generation of NECs rely on machine learning and artificial intelligence (AI), they may suffer from the same racist and sexist biases found in many AIs. Therefore, this paper offers an algorithmic fairness audit of thr…
Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design
Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased against some demographic groups, based on social attributes such as gender, age, and ethnicity. Sentiment classification works dealt with this issue by means of data balancing and algorithmic approaches. However,…
‘Slightly disappointing’ vs. ‘worst sh** ever’
Advertisers, politicians, and social scientists alike have an interest in the current zeitgeist. With people expressing their sentiments in online comments, the Internet has become a great source for ‘reading’ the zeitgeist via artificial intelligence-based sentiment analysis (SA). At this, negative sentiments are of special concern. They can serve as early indicators for events that require action, such as dropping customer satisfaction, discont…
Computer Science (3 works) · Artificial Intelligence (2 works) · Ethnic group (2 works) · Machine learning (2 works) · Natural language processing (2 works) · Sentiment analysis (2 works) · Sentiment Analysis and Opinion Mining (2 works) · Topic Modeling (2 works) · Algorithm (1 work) · Audit (1 work)