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Mapping the Ethics of Generative AI: A Comprehensive Scoping Review
The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks.
Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
Our paper intends to serve as a guidance for discussions within the fair ML community to prevent or reduce the misuse of fairness metrics, and thus reduce overall harm from ML applications.
Deception Abilities Emerged in Large Language Models
Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life.
Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life.
Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods
Due to increasingly complex and novel behavioral patterns in current LLMs, this can be done by treating them as participants in psychology experiments that were originally designed to test humans.
Methodological reflections for AI alignment research using human feedback
The field of artificial intelligence (AI) alignment aims to investigate whether AI technologies align with human interests and values and function in a safe and ethical manner.
Thinking Fast and Slow in Large Language Models
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life.
Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems
This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards.
Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals
Speciesist biases are learned and solidified by AI applications when they are trained on datasets in which speciesist patterns prevail.
AI virtues -- The missing link in putting AI ethics into practice
Moreover, it defines two second-order AI virtues, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or the many hidden psychological forces that impair ethical decision making and that are hitherto disregarded in AI ethics.
Ethical behavior in humans and machines -- Evaluating training data quality for beneficial machine learning
Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities.
Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research
For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years - almost 11, 000 papers in total.
Forbidden knowledge in machine learning -- Reflections on the limits of research and publication
Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented.
Artificial Intelligence Governance and Ethics: Global Perspectives
Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, and its implementation is planned to become more prevalent in coming years.
The Ethics of AI Ethics -- An Evaluation of Guidelines
Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics.
