32 research outputs found
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
This work presents CounterNet, a novel end-to-end learning framework which
integrates Machine Learning (ML) model training and the generation of
corresponding counterfactual (CF) explanations into a single end-to-end
pipeline. Counterfactual explanations offer a contrastive case, i.e., they
attempt to find the smallest modification to the feature values of an instance
that changes the prediction of the ML model on that instance to a predefined
output. Prior techniques for generating CF explanations suffer from two major
limitations: (i) all of them are post-hoc methods designed for use with
proprietary ML models -- as a result, their procedure for generating CF
explanations is uninformed by the training of the ML model, which leads to
misalignment between model predictions and explanations; and (ii) most of them
rely on solving separate time-intensive optimization problems to find CF
explanations for each input data point (which negatively impacts their
runtime). This work makes a novel departure from the prevalent post-hoc
paradigm (of generating CF explanations) by presenting CounterNet, an
end-to-end learning framework which integrates predictive model training and
the generation of counterfactual (CF) explanations into a single pipeline.
Unlike post-hoc methods, CounterNet enables the optimization of the CF
explanation generation only once together with the predictive model. We adopt a
block-wise coordinate descent procedure which helps in effectively training
CounterNet's network. Our extensive experiments on multiple real-world datasets
show that CounterNet generates high-quality predictions, and consistently
achieves 100% CF validity and low proximity scores (thereby achieving a
well-balanced cost-invalidity trade-off) for any new input instance, and runs
3X faster than existing state-of-the-art baselines
Simultaneous influencing and mapping for health interventions
Influence Maximization is an active topic, but it was always assumed full knowledge of the social network graph. However, the graph may actually be unknown beforehand. For example, when selecting a subset of a homeless population to attend interventions concerning health, we deal with a network that is not fully known. Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph. We study a class of algorithms, where we show that: (i) traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when the independence of objectives hypothesis holds; (iii) when it does not hold, the upper bound for the influence loss converges to 0. We run extensive experiments over four real-life social networks, where we study two alternative models, and obtain significantly better results in both than traditional approaches
Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness
Youth experiencing homelessness (YEH) are subject to substantially greater
risk of HIV infection, compounded both by their lack of access to stable
housing and the disproportionate representation of youth of marginalized
racial, ethnic, and gender identity groups among YEH. A key goal for health
equity is to improve adoption of protective behaviors in this population. One
promising strategy for intervention is to recruit peer leaders from the
population of YEH to promote behaviors such as condom usage and regular HIV
testing to their social contacts. This raises a computational question: which
youth should be selected as peer leaders to maximize the overall impact of the
intervention? We developed an artificial intelligence system to optimize such
social network interventions in a community health setting. We conducted a
clinical trial enrolling 713 YEH at drop-in centers in a large US city. The
clinical trial compared interventions planned with the algorithm to those where
the highest-degree nodes in the youths' social network were recruited as peer
leaders (the standard method in public health) and to an observation-only
control group. Results from the clinical trial show that youth in the AI group
experience statistically significant reductions in key risk behaviors for HIV
transmission, while those in the other groups do not. This provides, to our
knowledge, the first empirical validation of the usage of AI methods to
optimize social network interventions for health. We conclude by discussing
lessons learned over the course of the project which may inform future attempts
to use AI in community-level interventions