46 research outputs found
Re-formalization of Individual Fairness
The notion of individual fairness is a formalization of an ethical principle,
"Treating like cases alike," which has been argued such as by Aristotle. In a
fairness-aware machine learning context, Dwork et al. firstly formalized the
notion. In their formalization, a similar pair of data in an unfair space
should be mapped to similar positions in a fair space. We propose to
re-formalize individual fairness by the statistical independence conditioned by
individuals. This re-formalization has the following merits. First, our
formalization is compatible with that of Dwork et al. Second, our formalization
enables to combine individual fairness with the fairness notion, equalized odds
or sufficiency, as well as statistical parity. Third, though their
formalization implicitly assumes a pre-process approach for making fair
prediction, our formalization is applicable to an in-process or post-process
approach.Comment: Published at the 6th FAccTRec Workshop: Responsible Recommendatio
Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems
The increasing role of recommender systems in many aspects of society makes
it essential to consider how such systems may impact social good. Various
modifications to recommendation algorithms have been proposed to improve their
performance for specific socially relevant measures. However, previous
proposals are often not easily adapted to different measures, and they
generally require the ability to modify either existing system inputs, the
system's algorithm, or the system's outputs. As an alternative, in this paper
we introduce the idea of improving the social desirability of recommender
system outputs by adding more data to the input, an approach we view as
providing `antidote' data to the system. We formalize the antidote data
problem, and develop optimization-based solutions. We take as our model system
the matrix factorization approach to recommendation, and we propose a set of
measures to capture the polarization or fairness of recommendations. We then
show how to generate antidote data for each measure, pointing out a number of
computational efficiencies, and discuss the impact on overall system accuracy.
Our experiments show that a modest budget for antidote data can lead to
significant improvements in the polarization or fairness of recommendations.Comment: References to appendices are fixe
2nd FATREC Workshop: Responsible Recommendation
The second Workshop on Responsible Recommendation (FATREC 2018) was held in conjunction with the 12th ACM Conference on Recommender Systems on October 6th, 2018 in Vancouver, Canada. This full-day workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
Model-based and actual independence for fairness-aware classification
The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The main purpose of this paper is to analyze this formal fairness in order to achieve better trade-offs between fairness and prediction accuracy, which is important for applying fairness-aware classifiers in practical use. We focus on a fairness-aware classifier, Calders and Verwer’s two-naive-Bayes (CV2NB) method, which has been shown to be superior to other classifiers in terms of fairness. We hypothesize that this superiority is due to the difference in types of independence. That is, because CV2NB achieves actual independence, rather than satisfying model-based independence like the other classifiers, it can account for model bias and a deterministic decision rule. We empirically validate this hypothesis by modifying two fairness-aware classifiers, a prejudice remover method and a reject option-based classification (ROC) method, so as to satisfy actual independence. The fairness of these two modified methods was drastically improved, showing the importance of maintaining actual independence, rather than model-based independence. We additionally extend an approach adopted in the ROC method so as to make it applicable to classifiers other than those with generative models, such as SVMs
Prediction with Model-Based Neutrality
With recent developments in machine learning technology, the predictions by systems incorporating machine learning can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly dependent on personal attributes, such as gender or ethnicity, hiring decisions might be discriminatory. This paper investigates the neutralization of a probabilistic model with respect to another probabilistic model, referred to as a viewpoint. We present a novel definition of neutrality for probabilistic models, η-neutrality, and introduce a systematic method that uses the maximum likelihood estimation to enforce the neutrality of a prediction model. Our method can be applied to various machine learning algorithms, as demonstrated by η-neutral logistic regression and η-neutral linear regression
Making Fair ML Software using Trustworthy Explanation
Machine learning software is being used in many applications (finance,
hiring, admissions, criminal justice) having a huge social impact. But
sometimes the behavior of this software is biased and it shows discrimination
based on some sensitive attributes such as sex, race, etc. Prior works
concentrated on finding and mitigating bias in ML models. A recent trend is
using instance-based model-agnostic explanation methods such as LIME to find
out bias in the model prediction. Our work concentrates on finding shortcomings
of current bias measures and explanation methods. We show how our proposed
method based on K nearest neighbors can overcome those shortcomings and find
the underlying bias of black-box models. Our results are more trustworthy and
helpful for the practitioners. Finally, We describe our future framework
combining explanation and planning to build fair software.Comment: New Ideas and Emerging Results (NIER) track; The 35th IEEE/ACM
International Conference on Automated Software Engineering; Melbourne,
Australi
Exploring Author Gender in Book Rating and Recommendation
Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as gender or ethnic discrimination in publishing. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution