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Machine learning algorithms are increasingly used to make or support important decisions about people's lives. This has led to interest in the problem of fair classification, which involves learning to make decisions that are non-discriminatory with ...

In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual ...

In this project I have worked towards a method for critical, socially aligned research in Artificial Intelligence by merging the analysis of conceptual commitments in technical work, discourse analysis, and critical technical practice. While the goal of ...

Predictive models and algorithms are increasingly used to support human decision makers, raising concerns about how to ensure that these algorithms are fair. Additionally, these tools are generally designed to predict observable outcomes, but this is ...

Artificial Intelligence (AI) ethics is by no means a new discipline; thinkers like Asimov and Philip K Dick laid the foundations of this field decades ago. Both then and today, popular dilemmas in AI ethics largely focus on artificial consciousness, ...

Social robots are robots designed to interact and communicate directly with humans, following traditional social norms. However, many of these current robots operate in discrete settings with predefined expectations for specific social interactions. In ...

Fairness in machine learning has become a significant area of research as risk assessments and other algorithmic decision-making systems are increasingly used in high-stakes applications such as criminal justice, consumer lending, and child welfare ...

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the "long tail" is critical for ...

Every compassionate and functioning society requires its members to have a capacity to adopt others' perspectives. As Artificial Intelligence (AI) systems are given increasingly sensitive and impactful roles in society, it is important to enable AI to ...

Despite recent interest in both the critical and machine learning literature on "bias" in artificial intelligence (AI) systems, the nature of specific biases stemming from the interaction of machines, humans, and data remains ambiguous. Influenced by ...

Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind predictions and queries is important when assessing how the learner works and, in turn, trust. Consequently, ...

In recent news, organizations have been considering the use of facial and emotion recognition for applications involving youth such as tackling surveillance and security in schools. However, the majority of efforts on facial emotion recognition research ...

The wide adoption of machine learning in the critical domains such as medical diagnosis, law, education had propelled the need for interpretable techniques due to the need for end users to understand the reasoning behind decisions due to learning ...

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 ...

Sophisticated AI's will make decisions about how to respond to complex situations, and we may wonder whether those decisions will align with the moral values of human beings. I argue that pessimistic worries about this value alignment problem are ...

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 ...

Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel ...

This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on publicly available embeddings, including a supposedly "...

Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? Here, we show that applying machine learning to human texts can ...

The use of algorithmic (learning-based) decision making in scenarios that affect human lives has motivated a number of recent studies to investigate such decision making systems for potential unfairness, such as discrimination against subjects based on ...