11,577 research outputs found
Abstracting Fairness: Oracles, Metrics, and Interpretability
It is well understood that classification algorithms, for example, for
deciding on loan applications, cannot be evaluated for fairness without taking
context into account. We examine what can be learned from a fairness oracle
equipped with an underlying understanding of ``true'' fairness. The oracle
takes as input a (context, classifier) pair satisfying an arbitrary fairness
definition, and accepts or rejects the pair according to whether the classifier
satisfies the underlying fairness truth. Our principal conceptual result is an
extraction procedure that learns the underlying truth; moreover, the procedure
can learn an approximation to this truth given access to a weak form of the
oracle. Since every ``truly fair'' classifier induces a coarse metric, in which
those receiving the same decision are at distance zero from one another and
those receiving different decisions are at distance one, this extraction
process provides the basis for ensuring a rough form of metric fairness, also
known as individual fairness. Our principal technical result is a higher
fidelity extractor under a mild technical constraint on the weak oracle's
conception of fairness. Our framework permits the scenario in which many
classifiers, with differing outcomes, may all be considered fair. Our results
have implications for interpretablity -- a highly desired but poorly defined
property of classification systems that endeavors to permit a human arbiter to
reject classifiers deemed to be ``unfair'' or illegitimately derived.Comment: 17 pages, 1 figur
Agricultural Growth, Poverty Reduction and Millennium Development Goals in Africa: Outcomes of AAAE Conference
This report is a summary of emerging issues affecting African agriculture, recent experiences and policy proposals that can guide interventions in improving the sector’s productivity. Agriculture is at the centre of rural poverty reduction in Africa and urgent measures are needed to increase farm yields and incomes in order to stem collapse of economies and societies.AAAE, African Association of Agricultural Economists, millennium development goals in Africa, agricultural policies, agricultural research systems, poverty reduction, agricultural productivity, Agricultural and Food Policy, Community/Rural/Urban Development, Farm Management, Food Security and Poverty, Production Economics, Research and Development/Tech Change/Emerging Technologies, Q010, Q130, Q170, Q180, Q560,
Preference-Informed Fairness
We study notions of fairness in decision-making systems when individuals have
diverse preferences over the possible outcomes of the decisions. Our starting
point is the seminal work of Dwork et al. which introduced a notion of
individual fairness (IF): given a task-specific similarity metric, every pair
of individuals who are similarly qualified according to the metric should
receive similar outcomes. We show that when individuals have diverse
preferences over outcomes, requiring IF may unintentionally lead to
less-preferred outcomes for the very individuals that IF aims to protect. A
natural alternative to IF is the classic notion of fair division, envy-freeness
(EF): no individual should prefer another individual's outcome over their own.
Although EF allows for solutions where all individuals receive a
highly-preferred outcome, EF may also be overly-restrictive. For instance, if
many individuals agree on the best outcome, then if any individual receives
this outcome, they all must receive it, regardless of each individual's
underlying qualifications for the outcome.
We introduce and study a new notion of preference-informed individual
fairness (PIIF) that is a relaxation of both individual fairness and
envy-freeness. At a high-level, PIIF requires that outcomes satisfy IF-style
constraints, but allows for deviations provided they are in line with
individuals' preferences. We show that PIIF can permit outcomes that are more
favorable to individuals than any IF solution, while providing considerably
more flexibility to the decision-maker than EF. In addition, we show how to
efficiently optimize any convex objective over the outcomes subject to PIIF for
a rich class of individual preferences. Finally, we demonstrate the broad
applicability of the PIIF framework by extending our definitions and algorithms
to the multiple-task targeted advertising setting introduced by Dwork and
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