arXiv subjects

Max Van Kleek

Publications and source records attributed to Max Van Kleek.

39 records · Page 3Linked to original sources

arXiv2018

'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

cs.HC↗

arXiv2017

Like trainer, like bot? Inheritance of bias in algorithmic content moderation

The internet has become a central medium through which `networked publics' express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this problem using machine learning classifiers trained on large corpora of texts manually annotated for offence. While such systems could help encourage more civil debate, they must navigate inherently normatively contestable boundaries, and are subject to the idiosyncratic norms of the human raters who provide the training data. An important objective for platforms implementing such measures might be to ensure that they are not unduly biased towards or against particular norms of offence. This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence. We train classifiers on comments labelled by different demographic subsets (men and women) to understand how differences in conceptions of offence between these groups might affect the performance of the resulting models on various test sets. We conclude by discussing some of the ethical choices facing the implementers of algorithmic moderation systems, given various desired levels of diversity of viewpoints amongst discussion participants.

cs.CY↗

arXiv2014

Collective Intelligence in Citizen Science -- A Study of Performers and Talkers

The recent emergence of online citizen science is illustrative of an efficient and effective means to harness the crowd in order to achieve a range of scientific discoveries. Fundamentally, citizen science projects draw upon crowds of non-expert volunteers to complete short Tasks, which can vary in domain and complexity. However, unlike most human-computational systems, participants in these systems, the `citizen scientists' are volunteers, whereby no incentives, financial or otherwise, are offered. Furthermore, encouraged by citizen science platforms such as Zooniverse, online communities have emerged, providing them with an environment to discuss, share ideas, and solve problems. In fact, it is the result of these forums that has enabled a number of scientific discoveries to be made. In this paper we explore the phenomenon of collective intelligence via the relationship between the activities of online citizen science communities and the discovery of scientific knowledge. We perform a cross-project analysis of ten Zooniverse citizen science projects and analyse the behaviour of users with regards to their Task completion activity and participation in discussion and discover collective behaviour amongst highly active users. Whilst our findings have implications for future citizen science design, we also consider the wider implications for understanding collective intelligence research in general.

cs.SI↗