25,108 research outputs found
Fairness of Exposure in Rankings
Rankings are ubiquitous in the online world today. As we have transitioned
from finding books in libraries to ranking products, jobs, job applicants,
opinions and potential romantic partners, there is a substantial precedent that
ranking systems have a responsibility not only to their users but also to the
items being ranked. To address these often conflicting responsibilities, we
propose a conceptual and computational framework that allows the formulation of
fairness constraints on rankings in terms of exposure allocation. As part of
this framework, we develop efficient algorithms for finding rankings that
maximize the utility for the user while provably satisfying a specifiable
notion of fairness. Since fairness goals can be application specific, we show
how a broad range of fairness constraints can be implemented using our
framework, including forms of demographic parity, disparate treatment, and
disparate impact constraints. We illustrate the effect of these constraints by
providing empirical results on two ranking problems.Comment: In Proceedings of the 24th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, London, UK, 201
Causal inference using the algorithmic Markov condition
Inferring the causal structure that links n observables is usually based upon
detecting statistical dependences and choosing simple graphs that make the
joint measure Markovian. Here we argue why causal inference is also possible
when only single observations are present.
We develop a theory how to generate causal graphs explaining similarities
between single objects. To this end, we replace the notion of conditional
stochastic independence in the causal Markov condition with the vanishing of
conditional algorithmic mutual information and describe the corresponding
causal inference rules.
We explain why a consistent reformulation of causal inference in terms of
algorithmic complexity implies a new inference principle that takes into
account also the complexity of conditional probability densities, making it
possible to select among Markov equivalent causal graphs. This insight provides
a theoretical foundation of a heuristic principle proposed in earlier work.
We also discuss how to replace Kolmogorov complexity with decidable
complexity criteria. This can be seen as an algorithmic analog of replacing the
empirically undecidable question of statistical independence with practical
independence tests that are based on implicit or explicit assumptions on the
underlying distribution.Comment: 16 figure
An Algorithmic Approach to Information and Meaning
I will survey some matters of relevance to a philosophical discussion of
information, taking into account developments in algorithmic information theory
(AIT). I will propose that meaning is deep in the sense of Bennett's logical
depth, and that algorithmic probability may provide the stability needed for a
robust algorithmic definition of meaning, one that takes into consideration the
interpretation and the recipient's own knowledge encoded in the story attached
to a message.Comment: preprint reviewed version closer to the version accepted by the
journa
Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence
This article is a brief personal account of the past, present, and future of
algorithmic randomness, emphasizing its role in inductive inference and
artificial intelligence. It is written for a general audience interested in
science and philosophy. Intuitively, randomness is a lack of order or
predictability. If randomness is the opposite of determinism, then algorithmic
randomness is the opposite of computability. Besides many other things, these
concepts have been used to quantify Ockham's razor, solve the induction
problem, and define intelligence.Comment: 9 LaTeX page
Uncovering missing links with cold ends
To evaluate the performance of prediction of missing links, the known data
are randomly divided into two parts, the training set and the probe set. We
argue that this straightforward and standard method may lead to terrible bias,
since in real biological and information networks, missing links are more
likely to be links connecting low-degree nodes. We therefore study how to
uncover missing links with low-degree nodes, namely links in the probe set are
of lower degree products than a random sampling. Experimental analysis on ten
local similarity indices and four disparate real networks reveals a surprising
result that the Leicht-Holme-Newman index [E. A. Leicht, P. Holme, and M. E. J.
Newman, Phys. Rev. E 73, 026120 (2006)] performs the best, although it was
known to be one of the worst indices if the probe set is a random sampling of
all links. We further propose an parameter-dependent index, which considerably
improves the prediction accuracy. Finally, we show the relevance of the
proposed index on three real sampling methods.Comment: 16 pages, 5 figures, 6 table
Compressibility, laws of nature, initial conditions and complexity
We critically analyse the point of view for which laws of nature are just a
mean to compress data. Discussing some basic notions of dynamical systems and
information theory, we show that the idea that the analysis of large amount of
data by means of an algorithm of compression is equivalent to the knowledge one
can have from scientific laws, is rather naive. In particular we discuss the
subtle conceptual topic of the initial conditions of phenomena which are
generally incompressible. Starting from this point, we argue that laws of
nature represent more than a pure compression of data, and that the
availability of large amount of data, in general, is not particularly useful to
understand the behaviour of complex phenomena.Comment: 19 Pages, No figures, published on Foundation of Physic
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