Collaborating Authors

Is artificial intelligence ready for the great rehiring?


While AI has the potential to eliminate bias in hiring, it can also multiply inequalities exponentially if it is used carelessly. This is because of the way that AI works: algorithms correlate information from a finite data set in order to make predictions about job applicants. An algorithm's predictions are only as sound as the data set on which it relies. If an algorithm's training data consists of the employer's current workforce, it may simply replicate the status quo. This can be problematic if the current workforce is made up predominantly of employees of one race, gender, or age group, because a hiring algorithm may automatically screen out applicants who do not share those same characteristics.

Artificial Intelligence Can Help Leaders Make Better Decisions Faster


Decision-making remains one of the ultimate tests for leadership in new entrepreneurs. Even experienced leaders who have a track record of sound decision-making have, at some point, made a drastically poor decision that shook their reputation. As the talk about AI promises a radical transformation of the organization, leaders are especially curious to know if it will make it easier for them. While a lot of them are excited, some of them don't want decision-making made easier. Their ability to make sound decisions without complex technology is the very foundation of their reputation as good leaders.

Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses Artificial Intelligence

As predictive models are being increasingly deployed to make a variety of consequential decisions ranging from hiring decisions to loan approvals, there is growing emphasis on designing algorithms that can provide reliable recourses to affected individuals. To this end, several recourse generation algorithms have been proposed in recent literature. However, there is little to no work on systematically assessing if these algorithms are actually generating recourses that are reliable. In this work, we assess the reliability of algorithmic recourses through the lens of distribution shifts i.e., we empirically and theoretically study if and what kind of recourses generated by state-of-the-art algorithms are robust to distribution shifts. To the best of our knowledge, this work makes the first attempt at addressing this critical question. We experiment with multiple synthetic and real world datasets capturing different kinds of distribution shifts including temporal shifts, geospatial shifts, and shifts due to data corrections. Our results demonstrate that all the aforementioned distribution shifts could potentially invalidate the recourses generated by state-of-the-art algorithms. In addition, we also find that recourse interventions themselves may cause distribution shifts which in turn invalidate previously prescribed recourses. Our theoretical results establish that the recourses (counterfactuals) that are close to the model decision boundary are more likely to be invalidated upon model updation. However, state-of-the-art algorithms tend to prefer exactly these recourses because their cost functions penalize recourses (counterfactuals) that require large modifications to the original instance. Our findings not only expose fundamental flaws in recourse finding strategies but also pave new way for rethinking the design and development of recourse generation algorithms.

Boosting Algorithms for Delivery Time Prediction in Transportation Logistics Machine Learning

Travel time is a crucial measure in transportation. Accurate travel time prediction is also fundamental for operation and advanced information systems. A variety of solutions exist for short-term travel time predictions such as solutions that utilize real-time GPS data and optimization methods to track the path of a vehicle. However, reliable long-term predictions remain challenging. We show in this paper the applicability and usefulness of travel time i.e. delivery time prediction for postal services. We investigate several methods such as linear regression models and tree based ensembles such as random forest, bagging, and boosting, that allow to predict delivery time by conducting extensive experiments and considering many usability scenarios. Results reveal that travel time prediction can help mitigate high delays in postal services. We show that some boosting algorithms, such as light gradient boosting and catboost, have a higher performance in terms of accuracy and runtime efficiency than other baselines such as linear regression models, bagging regressor and random forest.

GRADE: Machine Learning Support for Graduate Admissions

AI Magazine

This article describes GRADE, a statistical machine learning system developed to support the work of the graduate admissions committee at the University of Texas at Austin Department of Computer Science (UTCS). In recent years, the number of applications to the UTCS PhD program has become too large to manage with a traditional review process. GRADE makes the review process more efficient by enabling reviewers to spend most of their time on applicants near the decision boundary and by focusing their attention on parts of each applicant's file that matter the most. An evaluation over two seasons of PhD admissions indicates that the system leads to dramatic time savings, reducing the total time spent on reviews by at least 74 percent.