EAAMO

Computer vision is a broad field of study that encompasses different tasks (e.g., object detection, semantic segmentation, 3D reconstruction). Although computer vision is relevant to the African communities in various applications, yet computer vision ...

We present counterfactual situation testing (CST), a causal data mining framework for detecting individual discrimination in a dataset of classifier decisions. CST answers the question “what would have been the model outcome had the individual, or ...

Prior work has established the importance of integrating AI ethics topics into computer and data sciences curricula. We provide evidence suggesting that one of the critical objectives of AI Ethics education must be to raise awareness of AI harms. While ...

Artificial intelligence (AI) stands to improve healthcare through innovative new systems ranging from diagnosis aids to patient tools. However, such “Health AI” systems are complicated and challenging to integrate into standing clinical practice. With ...

When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals? In this work, we propose a distinction between these two pathways of prediction, supported by theoretical, empirical, and ...

Manipulation is a concern in many domains, such as social media, advertising, and chatbots. As AI systems mediate more of our digital interactions, it is important to understand the degree to which AI systems might manipulate humans without the intent ...

Prediction-based decision-making systems are becoming increasingly prevalent in various domains. Previous studies have demonstrated that such systems are vulnerable to runaway feedback loops, e.g., when police are repeatedly sent back to the same ...

The rapid growth of AI in contemporary life has outpaced the public participation necessary for society to determine how these technologies should be used. As scholars respond to this challenge by exploring new modes of public participation in AI, we ...

In fair division applications, agents may have unequal entitlements reflecting their different contributions. Moreover, the contributions of agents may depend on the allocation itself. Previous fairness notions designed for agents with equal or pre-...

A fiduciary is a trusted agent that has the legal duty to act with loyalty and care towards a principal that employs them. When fiduciary organizations interact with users through a digital interface, or otherwise automate their operations with ...

Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research indicates that these models can learn biased representations leading to discriminatory outcomes. A few works have been ...

Nonprofit organizations (NPOs) lack resources, hindering the quality and quantity of service they can deliver. Meanwhile, NPOs at times have underutilized or even spare resources due to the inability to scale expertise in staffing and tangible resources ...

With Artificial Intelligence (AI) occupying the centre stage of technological advancements, its impact is affecting many sections of society. Because algorithmic decisions carry both economic and personal implications, fairness, accountability, ...

Detecting, measuring and mitigating various measures of unfairness are core aims of algorithmic fairness research. However, the most prominent approaches require access to individual level demographic information, such as sex or race. In practice, such ...

AI has revolutionized the processing of various services, including the automatic facial verification of people. Automated approaches have demonstrated their speed and efficiency in verifying a large volume of faces, but they can face challenges when ...

We study the admissions problem for academically selective K-12 education programs and magnet schools. Many states and public school districts are either required by law to, or choose to, offer specialized programs for students who show potential for ...

A broad current application of algorithms is in formal and quantitative measures of murky concepts – like merit – to make decisions. When people strategically respond to these sorts of evaluations in order to gain favorable decision outcomes, their ...

Prior work has provided strong evidence that, within organizational settings, teams that bring a diversity of information and perspectives to a task are more effective than teams that do not. If this form of informational diversity confers performance ...

As machine learning (ML) pipelines affect an increasing array of stakeholders, there is a growing need for documenting how input from stakeholders is recorded and incorporated. We propose FeedbackLogs, addenda to existing documentation of ML pipelines, ...

The United States has introduced a special humanitarian parole process for Ukrainian citizens in response to Russia’s 2022 invasion of Ukraine. To qualify for parole, Ukrainian applicants must have a sponsor in the United States. In collaboration with ...

In addition to being a health and fitness band, the Amazon Halo offers users information about how their voices sound, i.e., their ‘tones’. The Halo’s tone analysis capability leverages machine learning, which can lead to potentially biased inferences. ...

In 2021, the National Institute of Justice — the research arm of the United States Department of Justice — released the “Recidivism Forecasting Challenge” (“the Challenge”) with the stated goals of “increas[ing] public safety and improv[ing] the fair ...

Algorithmic decision-making driven by neural networks has become very prominent in applications that directly affect people’s quality of life. This paper focuses on the problem of ensuring individual fairness in neural network models during verification,...

Social media posts have been used to predict different user behaviors and attitudes, including mental health condition, political affiliation, and vaccine hesitancy. Unfortunately, while social media platforms make APIs available for collecting user ...

The UK Government’s ‘Algorithmic Transparency Recording Standard’ is intended to provide a standardised way for public bodies and government departments to provide information about how algorithmic tools are being used. To explore the implications of ...

Dynamic learning systems subject to selective labeling exhibit censoring, i.e. persistent negative predictions assigned to one or more subgroups of points. In applications like consumer finance, this results in groups of applicants that are persistently ...

In this position paper, we introduce a new epistemic lens for analyzing algorithmic harm. We argue that the epistemic lens we propose herein has two key contributions to help reframe and address some of the assumptions underlying inquiries into ...

The last decade has seen a wave of technological innovation by police forces. While this development has attracted significant scholarly attention, little is known about the accompanying media coverage, which can play a significant role in public ...

Algorithmic systems are often called upon to assist in high-stakes decision making. In light of this, algorithmic recourse, the principle wherein individuals should be able to take action against an undesirable outcome made by an algorithmic system, is ...

In real-world classification settings, such as loan application evaluation or content moderation on online platforms, individuals respond to classifier predictions by strategically updating their features to increase their likelihood of receiving a ...