16 research outputs found
On Measuring Bias in Online Information
Bias in online information has recently become a pressing issue, with search
engines, social networks and recommendation services being accused of
exhibiting some form of bias. In this vision paper, we make the case for a
systematic approach towards measuring bias. To this end, we discuss formal
measures for quantifying the various types of bias, we outline the system
components necessary for realizing them, and we highlight the related research
challenges and open problems.Comment: 6 pages, 1 figur
Bias mitigation with AIF360: A comparative study
The use of artificial intelligence for decision making raises concerns about the societal impact of such systems. Traditionally, the product of a human decision-maker are governed by laws and human values. Decision-making is now being guided - or in some cases, replaced by machine learning classification which may reinforce and introduce bias. Algorithmic bias mitigation is explored as an approach to avoid this, however it does come at a cost: efficiency and accuracy. We conduct an empirical analysis of two off-the-shelf bias mitigation techniques from the AIF360 toolkit on a binary classification task. Our preliminary results indicate that bias mitigation is a feasible approach to ensuring group fairness
Bias mitigation with AIF360: A comparative study
The use of artificial intelligence for decision making raises concerns about the societal impact of such systems. Traditionally, the product of a human decision-maker are governed by laws and human values. Decision-making is now being guided - or in some cases, replaced by machine learning classification which may reinforce and introduce bias. Algorithmic bias mitigation is explored as an approach to avoid this, however it does come at a cost: efficiency and accuracy. We conduct an empirical analysis of two off-the-shelf bias mitigation techniques from the AIF360 toolkit on a binary classification task. Our preliminary results indicate that bias mitigation is a feasible approach to ensuring group fairness
Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms
One of the hallmarks of a free and fair society is the ability to conduct a
peaceful and seamless transfer of power from one leader to another.
Democratically, this is measured in a citizen population's trust in the
electoral system of choosing a representative government. In view of the well
documented issues of the 2016 US Presidential election, we conducted an
in-depth analysis of the 2018 US Midterm elections looking specifically for
voter fraud or suppression. The Midterm election occurs in the middle of a 4
year presidential term. For the 2018 midterms, 35 senators and all the 435
seats in the House of Representatives were up for re-election, thus, every
congressional district and practically every state had a federal election. In
order to collect election related tweets, we analyzed Twitter during the month
prior to, and the two weeks following, the November 6, 2018 election day. In a
targeted analysis to detect statistical anomalies or election interference, we
identified several biases that can lead to wrong conclusions. Specifically, we
looked for divergence between actual voting outcomes and instances of the
#ivoted hashtag on the election day. This analysis highlighted three states of
concern: New York, California, and Texas. We repeated our analysis discarding
malicious accounts, such as social bots. Upon further inspection and against a
backdrop of collected general election-related tweets, we identified some
confounding factors, such as population bias, or bot and political ideology
inference, that can lead to false conclusions. We conclude by providing an
in-depth discussion of the perils and challenges of using social media data to
explore questions about election manipulation
Joint Inference on Truth/Rumor and Their Sources in Social Networks
In the contemporary era of information explosion, we are often faced with the
mixture of massive \emph{truth} (true information) and \emph{rumor} (false
information) flooded over social networks. Under such circumstances, it is very
essential to infer whether each claim (e.g., news, messages) is a truth or a
rumor, and identify their \emph{sources}, i.e., the users who initially spread
those claims. While most prior arts have been dedicated to the two tasks
respectively, this paper aims to offer the joint inference on truth/rumor and
their sources. Our insight is that a joint inference can enhance the mutual
performance on both sides.
To this end, we propose a framework named SourceCR, which alternates between
two modules, i.e., \emph{credibility-reliability training} for truth/rumor
inference and \emph{division-querying} for source detection, in an iterative
manner. To elaborate, the former module performs a simultaneous estimation of
claim credibility and user reliability by virtue of an Expectation Maximization
algorithm, which takes the source reliability outputted from the latter module
as the initial input. Meanwhile, the latter module divides the network into two
different subnetworks labeled via the claim credibility, and in each subnetwork
launches source detection by applying querying of theoretical budget guarantee
to the users selected via the estimated reliability from the former module. The
proposed SourceCR is provably convergent, and algorithmic implementable with
reasonable computational complexity. We empirically validate the effectiveness
of the proposed framework in both synthetic and real datasets, where the joint
inference leads to an up to 35\% accuracy of credibility gain and 29\% source
detection rate gain compared with the separate counterparts