527 research outputs found
Optimal Counterfactual Explanations in Tree Ensembles
Counterfactual explanations are usually generated through heuristics that are
sensitive to the search's initial conditions. The absence of guarantees of
performance and robustness hinders trustworthiness. In this paper, we take a
disciplined approach towards counterfactual explanations for tree ensembles. We
advocate for a model-based search aiming at "optimal" explanations and propose
efficient mixed-integer programming approaches. We show that isolation forests
can be modeled within our framework to focus the search on plausible
explanations with a low outlier score. We provide comprehensive coverage of
additional constraints that model important objectives, heterogeneous data
types, structural constraints on the feature space, along with resource and
actionability restrictions. Our experimental analyses demonstrate that the
proposed search approach requires a computational effort that is orders of
magnitude smaller than previous mathematical programming algorithms. It scales
up to large data sets and tree ensembles, where it provides, within seconds,
systematic explanations grounded on well-defined models solved to optimality.Comment: Authors Accepted Manuscript (AAM), to be published in the Proceedings
of the 38th International Conference on Machine Learning, PMLR 139, 2021.
Additional typo corrections. Open source code available at
https://github.com/vidalt/OCEA
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
Counterfactual Explanations (CEs) have received increasing interest as a
major methodology for explaining neural network classifiers. Usually, CEs for
an input-output pair are defined as data points with minimum distance to the
input that are classified with a different label than the output. To tackle the
established problem that CEs are easily invalidated when model parameters are
updated (e.g. retrained), studies have proposed ways to certify the robustness
of CEs under model parameter changes bounded by a norm ball. However, existing
methods targeting this form of robustness are not sound or complete, and they
may generate implausible CEs, i.e., outliers wrt the training dataset. In fact,
no existing method simultaneously optimises for proximity and plausibility
while preserving robustness guarantees. In this work, we propose Provably
RObust and PLAusible Counterfactual Explanations (PROPLACE), a method
leveraging on robust optimisation techniques to address the aforementioned
limitations in the literature. We formulate an iterative algorithm to compute
provably robust CEs and prove its convergence, soundness and completeness.
Through a comparative experiment involving six baselines, five of which target
robustness, we show that PROPLACE achieves state-of-the-art performances
against metrics on three evaluation aspects.Comment: Accepted at ACML 2023, camera-ready versio
"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations
Counterfactual explanations are a popular type of explanation for making the
outcomes of a decision making system transparent to the user. Counterfactual
explanations tell the user what to do in order to change the outcome of the
system in a desirable way. However, it was recently discovered that the
recommendations of what to do can differ significantly in their complexity
between protected groups of individuals. Providing more difficult
recommendations of actions to one group leads to a disadvantage of this group
compared to other groups.
In this work we propose a model-agnostic method for computing counterfactual
explanations that do not differ significantly in their complexity between
protected groups
The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes
When users receive either a positive or negative outcome from an automated
system, Explainable AI (XAI) has almost exclusively focused on how to mutate
negative outcomes into positive ones by crossing a decision boundary using
counterfactuals (e.g., \textit{"If you earn 2k more, we will accept your loan
application"}). Here, we instead focus on \textit{positive} outcomes, and take
the novel step of using XAI to optimise them (e.g., \textit{"Even if you wish
to half your down-payment, we will still accept your loan application"}).
Explanations such as these that employ "even if..." reasoning, and do not cross
a decision boundary, are known as semifactuals. To instantiate semifactuals in
this context, we introduce the concept of \textit{Gain} (i.e., how much a user
stands to benefit from the explanation), and consider the first causal
formalisation of semifactuals. Tests on benchmark datasets show our algorithms
are better at maximising gain compared to prior work, and that causality is
important in the process. Most importantly however, a user study supports our
main hypothesis by showing people find semifactual explanations more useful
than counterfactuals when they receive the positive outcome of a loan
acceptance
Explainable History Distillation by Marked Temporal Point Process
Explainability of machine learning models is mandatory when researchers
introduce these commonly believed black boxes to real-world tasks, especially
high-stakes ones. In this paper, we build a machine learning system to
automatically generate explanations of happened events from history by \gls{ca}
based on the \acrfull{tpp}. Specifically, we propose a new task called
\acrfull{ehd}. This task requires a model to distill as few events as possible
from observed history. The target is that the event distribution conditioned on
left events predicts the observed future noticeably worse. We then regard
distilled events as the explanation for the future. To efficiently solve
\acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the
solution to the program by a model called \acrfull{model}. This work fills the
gap between our task and existing works, which only spot the difference between
factual and counterfactual worlds after applying a predefined modification to
the environment. Experiment results on Retweet and StackOverflow datasets prove
that \acrshort{model} significantly outperforms other \acrshort{ehd} baselines
and can reveal the rationale underpinning real-world processes
Rich nations, poor nations: how much can multiple equilibria explain?
This paper asks whether the income gap between rich and poor nations can be explained by multiple equilibria. We explore the quantitative implications of a simple two sector general equilibrium model that gives rise to multiplicity, and calibrate the model for a large number of countries. Under the assumptions of the model, around a quarter of the world’s economies are found to be in a low output equilibrium. The output gains associated with an equilibrium switch are sizeable, but well short of the vast income disparity observed in the data.poverty traps, multiple equilibria, TFP differences,calibration
Treatment Effects in Bunching Designs: The Impact of Mandatory Overtime Pay on Hours
The 1938 Fair Labor Standards Act mandates overtime premium pay for most U.S.
workers, but it has proven difficult to assess the policy's impact on the labor
market because the rule applies nationally and has varied little over time. I
use the extent to which firms bunch workers at the overtime threshold of 40
hours in a week to estimate the rule's effect on hours, drawing on data from
individual workers' weekly paychecks. To do so I generalize a popular
identification strategy that exploits bunching at kink points in a
decision-maker's choice set. Making only nonparametric assumptions about
preferences and heterogeneity, I show that the average causal response among
bunchers to the policy switch at the kink is partially identified. The bounds
indicate a relatively small elasticity of demand for weekly hours, suggesting
that the overtime mandate has a discernible but limited impact on hours and
employment
On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models
284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity
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