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Found 15 papers, 3 papers with code
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
Quantitative relevance of results, according to non-expert similarity, as well as localized image regions, are also significantly improved.
Teaching Meaningful Explanations
The adoption of machine learning in high-stakes applications such as healthcare and law has lagged in part because predictions are not accompanied by explanations comprehensible to the domain user, who often holds the ultimate responsibility for decisions and outcomes.
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers.
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach
Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy.
TED: Teaching AI to Explain its Decisions
Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions.
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes.
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness
Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers.
A Methodology for Creating AI FactSheets
This is the first work to describe a methodology for creating the form of AI documentation we call FactSheets.
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users
Systems aiming to aid consumers in their decision-making (e. g., by implementing persuasive techniques) are more likely to be effective when consumers trust them.
Decision Making
Fairness
Human-Computer Interaction
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
Evaluating a Methodology for Increasing AI Transparency: A Case Study
The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases.
Quantitative AI Risk Assessments: Opportunities and Challenges
Although AI-based systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified.
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
