Proof pending. Core topic summary fields are still materializing.
The field of ethical AI is evolving to address the complexities of aligning autonomous systems with human values. Current research focuses on developing frameworks for ethical benchmarking, such as SEED-SET, which evaluates autonomous agents using both objective metrics and stakeholder preferences. Additionally, studies on moral sycophancy in vision-language models reveal the need for improved decision-making consistency. Frameworks like fEDM+ enhance explainability and robustness in ethical decision-making, while operationalizing social and legal norms for AI agents is crucial for their deployment in sensitive areas. These advancements are vital for builders aiming to create trustworthy AI systems that prioritize ethical considerations in their design and implementation.
Age estimation from facial images typically relies on training data that includes images of minors, a practice that raises serious ethical, legal, and privacy concerns. In this work, we propose a gene...
As autonomous systems such as drones, become increasingly deployed in high-stakes, human-centric domains, it is critical to evaluate the ethical alignment since failure to do so imposes imminent dange...
Sycophancy in Vision-Language Models (VLMs) refers to their tendency to align with user opinions, often at the expense of moral or factual accuracy. While prior studies have explored sycophantic behav...
As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a...
Large language models (LLMs) are increasingly employed for decision-support across multiple domains. We investigate whether these models display a systematic preferential bias in favor of artificial i...
In a previous work, we introduced the fuzzy Ethical Decision-Making framework (fEDM), a risk-based ethical reasoning architecture grounded in fuzzy logic. The original model combined a fuzzy Ethical R...
Counterfactual explanations are widely used to communicate how inputs must change for a model to alter its prediction. For a single instance, many valid counterfactuals can exist, which leaves open th...
Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise of large language model (LLM)-based conversational ...
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Canonical route: /topics
Agent Handoff
Canonical ID ethical-ai | Route /topic/ethical-ai
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/ethical-aiMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Ethical AI",
"cluster": "Ethical AI"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Ethical AI",
"normalized_query": "ethical-ai",
"route": "/topic/ethical-ai",
"paper_ref": null,
"topic_slug": "ethical-ai",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.