Position: Social Science Researcher, Senior Manager Department: Learning, Innovation, and Data Systems FLSA Status: Full-Time, Exempt Reports to: Director, Learning, Innovation and Data Systems Direct Reports: None Date Issued: October 2022 Date Revised: N/A Location: Washington, DC The Mission Polaris is leading a data-driven social justice movement to fight sex and labor trafficking at the massive scale of the problem – 25 million people worldwide deprived of the freedom to choose how they live and work. For more than a decade, Polaris has assisted thousands of victims and survivors through the U.S. National Human Trafficking Hotline, helped ensure countless traffickers were held accountable and built the largest known U.S. data set on actual trafficking experiences. With the guidance of survivors, we use that data to improve the way trafficking is identified, how victims and survivors are assisted, and how communities, businesses and governments can prevent human trafficking by transforming the underlying inequities and oppression that make it possible. The Learning, Innovation, and Data Systems team has the exciting task of utilizing research and data to inform and guide our approach to the fight against human trafficking with the ultimate end goal of eradicating the crime of modern-day slavery. About Opportunity The Social Science Researcher is a highly self-motivated, creative, and methodical professional.

The most common ways to explore latent document dimensions are topic models and clustering methods. However, topic models have several drawbacks: e.g., they require us to choose the number of latent dimensions a priori, and the results are stochastic. Most clustering methods have the same issues and lack flexibility in various ways, such as not accounting for the influence of different topics on single documents, forcing word-descriptors to belong to a single topic (hard-clustering) or necessarily relying on word representations. We propose PROgressive SImilarity Thresholds - ProSiT, a deterministic and interpretable method, agnostic to the input format, that finds the optimal number of latent dimensions and only has two hyper-parameters, which can be set efficiently via grid search. We compare this method with a wide range of topic models and clustering methods on four benchmark data sets. In most setting, ProSiT matches or outperforms the other methods in terms six metrics of topic coherence and distinctiveness, producing replicable, deterministic results.

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with wellknown metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants.

A characteristic feature of time-to-event data analysis is possible censoring of the event time. Most of the statistical learning methods for handling censored data are limited by the assumption of independent censoring, even if this can lead to biased predictions when the assumption does not hold. This paper introduces Clayton-boost, a boosting approach built upon the accelerated failure time model, which uses a Clayton copula to handle the dependency between the event and censoring distributions. By taking advantage of a copula, the independent censoring assumption is not needed any more. During comparisons with commonly used methods, Clayton-boost shows a strong ability to remove prediction bias at the presence of dependent censoring and outperforms the comparing methods either if the dependency strength or percentage censoring are considerable. The encouraging performance of Clayton-boost shows that there is indeed reasons to be critical about the independent censoring assumption, and that real-world data could highly benefit from modelling the potential dependency.

In recent years, there has been increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations inherent in observational data, among other factors. The recent attention to causal fairness, however, has been accompanied with great skepticism due to practical and epistemological challenges with applying current causal fairness approaches in the literature. Motivated by the long-standing empirical work on causality in econometrics, social sciences, and biomedical sciences, in this paper we lay out the conditions for appropriate application of causal fairness under the "potential outcomes framework." We highlight key aspects of causal inference that are often ignored in the causal fairness literature. In particular, we discuss the importance of specifying the nature and timing of interventions on social categories such as race or gender. Precisely, instead of postulating an intervention on immutable attributes, we propose a shift in focus to their perceptions and discuss the implications for fairness evaluation. We argue that such conceptualization of the intervention is key in evaluating the validity of causal assumptions and conducting sound causal analysis including avoiding post-treatment bias. Subsequently, we illustrate how causality can address the limitations of existing fairness metrics, including those that depend upon statistical correlations. Specifically, we introduce causal variants of common statistical notions of fairness, and we make a novel observation that under the causal framework there is no fundamental disagreement between different notions of fairness. Finally, we conduct extensive experiments where we demonstrate our approach for evaluating and mitigating unfairness, specially when post-treatment variables are present.

In this paper, we examine how generative machine learning systems produce a new politics of visual culture. We focus on DALL-E 2 and related models as an emergent approach to image-making that operates through the cultural techniques of feature extraction and semantic compression. These techniques, we argue, are inhuman, invisual, and opaque, yet are still caught in a paradox that is ironically all too human: the consistent reproduction of whiteness as a latent feature of dominant visual culture. We use Open AI's failed efforts to 'debias' their system as a critical opening to interrogate how systems like DALL-E 2 dissolve and reconstitute politically salient human concepts like race. This example vividly illustrates the stakes of this moment of transformation, when so-called foundation models reconfigure the boundaries of visual culture and when 'doing' anti-racism means deploying quick technical fixes to mitigate personal discomfort, or more importantly, potential commercial loss.

In context: French authorities have imposed the maximum possible fine against Clearview AI, a biometric startup selling its controversial facial recognition technology to governments and law enforcement worldwide. The company must delete the data already acquired on French citizens or face an additional €100,000 fine per day. Clearview AI received yet another fine for its biometric profiling activities in Europe, this time for illegally collecting and using data belonging to French citizens without their knowledge. The Commission nationale de l'informatique et des libertés (CNIL), France's data protection authority, imposed a 20 million euros penalty against the American company after a lengthy investigation and an unfruitful cooperation attempt. Clearview markets facial recognition tools to companies, individuals, and law enforcement, boasting its algorithm can detect any individual with "99% accuracy" in a database with 30 billion images of faces.

In this #5 post of the Symposium "Hitchhikers Guide to Law & Tech", we continue analyzing the EU's Digital Strategy and the intersection between law and tech. The EU's proposed AI Act, if adopted, has the potential to modify the existing legal framework for the protection of workers, faced with an increasing prevalence of AI technologies. This blogpost presents an outline of the significant legal scrutiny and numerous safeguards most workers' data collection and processing activities would need to meet, as falling within the scope of high-risk AI systems. This is accompanied by a brief reflection on the challenges ahead, and the path forward that needs to be followed for the desired results to be achieved. The software techniques that qualify as AI systems within the proposed AI Act are very extensive in their scope.

Explainable AI typically involves tools & techniques to understand how a complex model behaves, in a simple, straightforward and intuitive way so humans can understand it. It answers why an automated decision making tool resulted in a specific output that impacts customers, but doesn't explain how. It's predicted the explainable AI market size is estimated to reach $21.8 billion by 2030, up from $4.1 billion in 2021. And Gartner's crystal ball paints a picture that "by 2025, 30% of government and large enterprise contracts for the purchase of AI products and services will require the use of explainable and ethical AI." So, what's fueling predicted market growth? The accelerant for the explainable AI market is due in part to EU advent of GPDR's Article 13-15 and 22, which establishes rights specific to algorithmic decision making, including a right of both notification and access to meaningful information about the logic involved and the right of the significance of and envisioned effects of solely automated decision making.

A large amount of AI projects fail. Rackspace Technology's survey estimates the number at a whopping 34% [2]. Much of this failure is due to management not understanding the risks and intricacies of AI technologies and, vice versa, developers not knowing how to scale the technologies or the business needs. Many managers seem to think that AI projects will be just like typical software project, however, there are specific challenges involved. These challenges may relate to the skills required in the team, legal issues requiring model transparency, big data governance, cultural challenges for adoption, and many more.