Algorithmic Impact Assessments and Accountability - The Co-construction of Impacts
Explore the challenges and complexities of algorithmic impact assessments, focusing on accountability, governance, and the diverse perspectives needed for effective evaluation.
Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
Explore how unsupervised pre-training in image representations can lead to human-like biases, examining methods, results, and implications for AI fairness and ethics.
On the Dangers of Stochastic Parrots - Can Language Models Be Too Big?
Explore the risks and ethical concerns of large language models, including data management, research implications, and potential mitigation strategies for AI development.
You Can't Sit With Us - Exclusionary Pedagogy in AI Ethics Education
Explores exclusionary practices in AI ethics education, highlighting methodological dogmatism, lack of co-creation, and siloed citations. Proposes ideas for more collaborative and inclusive pedagogical approaches.
Fairness Through Robustness - Investigating Robustness Disparity in Deep Learning
Explore robustness disparity in deep learning, its impact on fairness, and potential mitigation strategies for more equitable AI systems.
Measurement and Fairness
Explore the intersection of measurement and fairness in computing, examining reliability, validity, and group fairness concepts to enhance ethical decision-making in technology.
What We Can’t Measure, We Can’t Understand - Challenges to Demographic Data Procurement in the Pursuit of Fairness
Explore challenges in demographic data collection for fairness, covering recruitment, privacy laws, anti-discrimination policies, and organizational trustworthiness in pursuit of equitable AI systems.
The Use and Misuse of Counterfactuals in Ethical Machine Learning
Explore the ethical implications of counterfactuals in machine learning, examining their use, misuse, and associated challenges in ensuring fairness and accountability in AI systems.
Removing Spurious Features Can Hurt Accuracy and Affect Groups Disproportionately
Explore the impact of removing spurious features on model accuracy and group fairness in machine learning, examining potential unintended consequences and ethical implications.
Differential Tweetment - Mitigating Racial Dialect Bias in Harmful Tweet Detection
Explore techniques to mitigate racial dialect bias in harmful tweet detection, addressing data set bias and examining adversary debiasing methods for more equitable content moderation.
Computer Science Communities - Who is Speaking, and Who is Listening to the Women?
Explore gender representation in computer science communities, analyzing speaking and listening patterns to address inequalities and propose ethical solutions for inclusivity.
Fifty Shades of Grey - In Praise of a Nuanced Approach Towards Trustworthy Design
Explore nuanced approaches to trustworthy design in computing, emphasizing transparency and environmental data science. Learn key strategies for building trust in virtual labs and digital systems.
What Does Fairness Mean in Data Protection Law
Explore legal concepts of fairness in data protection, examining justice, equality, and AI ethics through practical scenarios and GDPR implications.
Assessing the Intersection of Organizational Structure and ACM FAT Efforts Within Industry
Explore the impact of organizational structure on AI fairness, accountability, and transparency efforts in industry. Gain insights into emerging theories and practical approaches.
The Meaning and Measurement of Bias - Lessons from NLP
Explore the complexities of bias in NLP, covering measurement processes, construct validity, and fairness evaluation. Gain insights into representational harms and practical examples in word embeddings.
Can an Algorithmic System Be a 'Friend' to a Police Officer's Discretion?
Explore the intersection of algorithmic systems and police discretion, examining challenges, legal frameworks, and real-world applications in domestic abuse risk assessment and decision-making.
Positionality-Aware Machine Learning
Explore positionality in machine learning, examining its impact on data, models, and outcomes through case studies and diverse perspectives.
From the Total Survey Error Framework to an Error Framework for Digital Traces of Humans
Explore error frameworks for digital human traces, bridging survey methodologies with computational approaches to enhance data quality and interpretation in social research.
Gender - What the GDPR Does Not Tell Us - But Maybe You Can
Explore gender's complex relationship with privacy, data protection, and AI through feminist perspectives and GDPR analysis. Uncover blind spots in current regulations and technologies.
Experimentation with Fairness-Aware Recommendation Using Librec-Auto
Explore fairness-aware recommendation systems using librec-auto, covering stakeholder concerns, diversity, individual fairness, and practical implementation techniques.
Translation Tutorial - Engineering for Fairness - How a Firm Conceptual Distinction between Unfairness and Bias Makes it Easier to Address Un-Fairness
Explore the distinction between bias and fairness in AI, and learn how to engineer for fairness through value-driven decision processes and organizational practices.