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Found 17 papers, 4 papers with code
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color
While effective, the Fitzpatrick scale only focuses on the skin tone ranging from light to dark.
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context.
Considerations for Ethical Speech Recognition Datasets
In this talk, we use automatic speech recognition as a case study and examine the properties that ethical speech datasets should possess towards responsible AI applications.
A View From Somewhere: Human-Centric Face Representations
Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks.
Principlism Guided Responsible Data Curation
Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models.
From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis
Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images.
Men Also Do Laundry: Multi-Attribute Bias Amplification
We show models can learn to exploit correlations with respect to multiple attributes (e. g., {$\texttt{computer}$, $\texttt{keyboard}$}), which are not accounted for by current metrics.
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
In this paper, we report on ongoing work regarding (i) the development of an AI system for flagging and explaining low-quality medical images in real-time, (ii) an interview study to understand the explanation needs of stakeholders using the AI system at OurCompany, and, (iii) a longitudinal user study design to examine the effect of including explanations on the workflow of the technicians in our clinics.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPA
This paper analyzes what we know based on BIPA court proceedings and highlights these points of tension: areas where the technical operationalization of BIPA may create unintended and undesirable incentives for FPT development, as well as areas where BIPA litigation can bring to light the limitations of solely technical methods in achieving legal privacy values.
Promises and Challenges of Causality for Ethical Machine Learning
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.
Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques.
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders.
"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness
Even with the growing variety of toolkits and strategies for working towards algorithmic fairness, they almost invariably require access to demographic attributes or proxies.
Machine Learning Explainability for External Stakeholders
As machine learning is increasingly deployed in high-stakes contexts affecting people's livelihoods, there have been growing calls to open the black box and to make machine learning algorithms more explainable.
On the Legal Compatibility of Fairness Definitions
Past literature has been effective in demonstrating ideological gaps in machine learning (ML) fairness definitions when considering their use in complex socio-technical systems.
Explainable Machine Learning in Deployment
Yet there is little understanding of how organizations use these methods in practice.
