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The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?
On the whole, the US Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated...
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A Genealogical Approach to Algorithmic Bias
The Fairness, Accountability, and Transparency (FAccT) literature tends to focus on bias as a problem that requires ex post solutions (e.g. fairness...
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Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics
This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial Intelligence/Machine learning algorithms) in the...
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Automated news recommendation in front of adversarial examples and the technical limits of transparency in algorithmic accountability
Algorithmic decision making is used in an increasing number of fields. Letting automated processes take decisions raises the question of their...
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Algorithmic evidence in U.S criminal sentencing
The use of automated risk assessment tools to predict a defendant’s risk of recidivism is necessarily unfair. There is a tradeoff between equal...
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Doubt or punish: on algorithmic pre-emption in acute psychiatry
Machine learning algorithms have begun to enter clinical settings traditionally resistant to digitalisation, such as psychiatry. This raises...
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Accountability in artificial intelligence: what it is and how it works
Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its...
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Just accountability structures – a way to promote the safe use of automated decision-making in the public sector
The growing use of automated decision-making (ADM) systems in the public sector and the need to control these has raised many legal questions in...
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On phantom publics, clusters, and collectives: be(com)ing subject in algorithmic times
This article starts from the observation that practices of ‘algorithmic governmentality’ or ‘governance by data’ are reconfiguring modes of social...
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On Algorithmic Content Moderation
This chapter provides an overview of the challenges involved in algorithmic content moderation. Content moderation is the organized practice of...
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From algorithmic governance to govern algorithm
Algorithm is the core category and basic methods of the digital age, and advanced technologies such as big data, artificial intelligence, and...
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Algorithmic discrimination in the credit domain: what do we know about it?
The widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for...
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Algorithmic decision-making in financial services: economic and normative outcomes in consumer credit
Consider how much data is created and used based on our online behaviours and choices. Converging foundational technologies now enable analytics of...
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Social context of the issue of discriminatory algorithmic decision-making systems
Algorithmic decision-making systems have the potential to amplify existing discriminatory patterns and negatively affect perceptions of justice in...
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Public health measures and the rise of incidental surveillance: Considerations about private informational power and accountability
The public health measures implemented in response to the COVID-19 pandemic have resulted in a substantially increased shared reliance on private...
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Algorithmic Bias and Risk Assessments: Lessons from Practice
In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for...
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Algorithmic fairness datasets: the story so far
Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a...
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Public opinion and persuasion of algorithmic fairness: assessment of communication protocol performance for use in simulation-based reinforcement learning training
As the popularity of AI continues to grow, the techniques used to train AI systems have become increasingly intriguing. Reinforcement learning (RL)...
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Understanding user sensemaking in fairness and transparency in algorithms: algorithmic sensemaking in over-the-top platform
A number of artificial intelligence (AI) systems have been proposed to assist users in identifying the issues of algorithmic fairness and...
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Proliferations in Algorithmic Control: Review of the Phenomenon and Its Implications
This research explores algorithmic controls, observing the phenomenon through micro, meso and macro perspectives of contextual analysis. Discovered...