Reuben Binns
Biographic Data
| ID | 3650481 |
|---|---|
| NAME | Reuben Binns |
| GIVEN NAMES | Reuben |
| FAMILY NAME | Binns |
| SIGNATURE | BINNS R |
| AFFILIATIONS | University of Oxford |
| ORCID | 0000-0001-8717-6559 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 96 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 3 |
If the Difference Principle Won’t Make a Real Difference in Algorithmic Fairness, What Will
Directly Discriminatory Algorithms
Discriminatory bias in algorithmic systems is widely documented. How should the law respond? A broad consensus suggests approaching the issue principally through the lens of indirect discrimination, focusing on algorithmic systems’ impact. In this article, we set out to challenge this analysis, arguing that while indirect discrimination law has an important role to play, a narrow focus on this regime in the context of machine learning algorithms …
Human Judgment in algorithmic loops
Arguments in favor of tempering algorithmic decision making with human judgment often appeal to concepts and criteria derived from legal philosophy about the nature of law and legal reasoning, arguing that algorithmic systems cannot satisfy them (but humans can). Such arguments often make implicit appeal to the notion that each case needs to be assessed on its own merits, without comparison to or generalization from previous cases. This article a…
Before and after GDPR
Third-party tracking, the collection and sharing of behavioural data about individuals, is a significant and ubiquitous privacy threat in mobile apps. The EU General Data Protection Regulation (GDPR) was introduced in 2018 to protect personal data better, but there exists, thus far, limited empirical evidence about its efficacy. This paper studies tracking in nearly two million Android apps from before and after the introduction of the GDPR. Our …
Algorithmic content moderation
As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such a…
Algorithmic Accountability and Public Reason
The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system's outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in te…
Fairer machine learning in the real world
Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial r…
Algorithmic content moderation
As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such a…
Human Judgment in algorithmic loops
Arguments in favor of tempering algorithmic decision making with human judgment often appeal to concepts and criteria derived from legal philosophy about the nature of law and legal reasoning, arguing that algorithmic systems cannot satisfy them (but humans can). Such arguments often make implicit appeal to the notion that each case needs to be assessed on its own merits, without comparison to or generalization from previous cases. This article a…
Fairer machine learning in the real world
Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial r…
Directly Discriminatory Algorithms
Discriminatory bias in algorithmic systems is widely documented. How should the law respond? A broad consensus suggests approaching the issue principally through the lens of indirect discrimination, focusing on algorithmic systems’ impact. In this article, we set out to challenge this analysis, arguing that while indirect discrimination law has an important role to play, a narrow focus on this regime in the context of machine learning algorithms …
Fairer machine learning in the real world
Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial r…
Algorithmic Accountability and Public Reason
The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system's outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in te…
Algorithmic content moderation
As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such a…
Before and after GDPR
Third-party tracking, the collection and sharing of behavioural data about individuals, is a significant and ubiquitous privacy threat in mobile apps. The EU General Data Protection Regulation (GDPR) was introduced in 2018 to protect personal data better, but there exists, thus far, limited empirical evidence about its efficacy. This paper studies tracking in nearly two million Android apps from before and after the introduction of the GDPR. Our …
Human Judgment in algorithmic loops
Arguments in favor of tempering algorithmic decision making with human judgment often appeal to concepts and criteria derived from legal philosophy about the nature of law and legal reasoning, arguing that algorithmic systems cannot satisfy them (but humans can). Such arguments often make implicit appeal to the notion that each case needs to be assessed on its own merits, without comparison to or generalization from previous cases. This article a…
Directly Discriminatory Algorithms
Discriminatory bias in algorithmic systems is widely documented. How should the law respond? A broad consensus suggests approaching the issue principally through the lens of indirect discrimination, focusing on algorithmic systems’ impact. In this article, we set out to challenge this analysis, arguing that while indirect discrimination law has an important role to play, a narrow focus on this regime in the context of machine learning algorithms …
If the Difference Principle Won’t Make a Real Difference in Algorithmic Fairness, What Will
Computer Science (6 works) · Ethics and Social Impacts of AI (6 works) · Artificial Intelligence (3 works) · Computer security (3 works) · Epistemology (3 works) · Law (3 works) · Political science (3 works) · Sociology (3 works) · Accountability (2 works) · Appeal (2 works)