2,891 research outputs found
Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy
Fairness concerns about algorithmic decision-making systems have been mainly
focused on the outputs (e.g., the accuracy of a classifier across individuals
or groups). However, one may additionally be concerned with fairness in the
inputs. In this paper, we propose and formulate two properties regarding the
inputs of (features used by) a classifier. In particular, we claim that fair
privacy (whether individuals are all asked to reveal the same information) and
need-to-know (whether users are only asked for the minimal information required
for the task at hand) are desirable properties of a decision system. We explore
the interaction between these properties and fairness in the outputs (fair
prediction accuracy). We show that for an optimal classifier these three
properties are in general incompatible, and we explain what common properties
of data make them incompatible. Finally we provide an algorithm to verify if
the trade-off between the three properties exists in a given dataset, and use
the algorithm to show that this trade-off is common in real data
Observing and recommending from a social web with biases
The research question this report addresses is: how, and to what extent,
those directly involved with the design, development and employment of a
specific black box algorithm can be certain that it is not unlawfully
discriminating (directly and/or indirectly) against particular persons with
protected characteristics (e.g. gender, race and ethnicity)?Comment: Technical Report, University of Southampton, March 201
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Graph Neural Networks (GNNs) have made rapid developments in the recent
years. Due to their great ability in modeling graph-structured data, GNNs are
vastly used in various applications, including high-stakes scenarios such as
financial analysis, traffic predictions, and drug discovery. Despite their
great potential in benefiting humans in the real world, recent study shows that
GNNs can leak private information, are vulnerable to adversarial attacks, can
inherit and magnify societal bias from training data and lack interpretability,
which have risk of causing unintentional harm to the users and society. For
example, existing works demonstrate that attackers can fool the GNNs to give
the outcome they desire with unnoticeable perturbation on training graph. GNNs
trained on social networks may embed the discrimination in their decision
process, strengthening the undesirable societal bias. Consequently, trustworthy
GNNs in various aspects are emerging to prevent the harm from GNN models and
increase the users' trust in GNNs. In this paper, we give a comprehensive
survey of GNNs in the computational aspects of privacy, robustness, fairness,
and explainability. For each aspect, we give the taxonomy of the related
methods and formulate the general frameworks for the multiple categories of
trustworthy GNNs. We also discuss the future research directions of each aspect
and connections between these aspects to help achieve trustworthiness
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Geographies of HIV/AIDS in Bangladesh: Vunerability, Stigma and Place.
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Living with HIV/AIDS: turning points, transitions and transformations in the lives of women from Bombay and Edinburgh
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Exacerbating Algorithmic Bias through Fairness Attacks
Algorithmic fairness has attracted significant attention in recent years,
with many quantitative measures suggested for characterizing the fairness of
different machine learning algorithms. Despite this interest, the robustness of
those fairness measures with respect to an intentional adversarial attack has
not been properly addressed. Indeed, most adversarial machine learning has
focused on the impact of malicious attacks on the accuracy of the system,
without any regard to the system's fairness. We propose new types of data
poisoning attacks where an adversary intentionally targets the fairness of a
system. Specifically, we propose two families of attacks that target fairness
measures. In the anchoring attack, we skew the decision boundary by placing
poisoned points near specific target points to bias the outcome. In the
influence attack on fairness, we aim to maximize the covariance between the
sensitive attributes and the decision outcome and affect the fairness of the
model. We conduct extensive experiments that indicate the effectiveness of our
proposed attacks
Health communication theories: Implications for HIV reporting in Asia and the Pacific
This paper focuses on the expanding HIV (Human Immunodeficiency Virus) epidemic in parts of Asia and the Pacific region and recommends the adoption of insights from particular health communication theories. The author argues that these paradigms can assist in broadening the current scope and content of HIV reporting. One theory in particular - Social Change Communication (SCC) - challenges the media to extend the framing of HIV from primarily a health story to one that is linked to more macro socio-economic, cultural and political factors. Asian and Pacific countries that have an emerging or expanding HIV epidemic need to realise a common reality when reporting on the disease; that is, the complexity and interconnectedness of the web of issues into which the HIV pandemic is woven
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