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To address the growing concern of unfairness in Artificial Intelligence (AI), several bias mitigation algorithms have been introduced in prior research. Their capabilities are often evaluated on certain overly-used datasets without rigorously stress-...

Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpetuate existing biases against racial minorities, as bias can be implicitly ...

Rapid technological innovation threatens to leave much of the global workforce behind. Today's economy juxtaposes white-hot demand for skilled labor against stagnant employment prospects for workers unprepared to participate in a digital economy. It is ...

Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? In general, how should we account for and balance the ethical values, safety ...

Recent research has revealed that many machine-learning models and the datasets they are trained on suffer from various forms of bias, and a large number of different fairness metrics have been created to measure this bias. However, determining which ...

Prior work on fairness in machine learning has focused on settings where all the information needed about each individual is readily available. However, in many applications, further information may be acquired at a cost. For example, when assessing a ...

Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most research into algorithmic transparency has predominantly focused on ...

Machine learning models are prone to biased decisions due to biases in the datasets they are trained on. In this paper, we introduce a novel data augmentation technique to create a fairer dataset for model training that could also lend itself to ...

The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, ...

The ability of an AI agent to build mental models can open up pathways for manipulating and exploiting the human in the hopes of achieving some greater good. In fact, such behavior does not necessarily require any malicious intent but can rather be ...

We design a flexible algorithm that exploits deceased donor kidneys to initiate chains of living donor kidney paired donations, combining deceased and living donor allocation mechanisms to improve the quantity and quality of kidney transplants. The ...

Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a ...

Risk assessment is a growing use for machine learning models. When used in high-stakes applications, especially ones regulated by anti-discrimination laws or governed by societal norms for fairness, it is important to ensure that learned models do not ...

Life on earth presents elegant solutions to many of the challenges innovators and entrepreneurs across disciplines face every day. To facilitate innovations inspired by nature, there is an emerging need for systems that bring relevant biological ...

Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on ...

As algorithms are given responsibility to make decisions that impact our lives, there is increasing awareness of the need to ensure the fairness of these decisions. One of the first challenges then is to decide what fairness means in a particular ...

A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance. However, just as we expect humans to be ethical, the same expectation ...

If we want people to trust AI systems, we need to provide the systems we create with the ability to discriminate between what humans would consider good and bad decisions. The quality of a decision should not be based only on the preferences or ...