AI Ethics
AI ethics ensures artificial intelligence systems are designed and used responsibly, promoting fairness, transparency, accountability, and alignment with human values. It covers ethical AI design, responsible AI practices, and strategies to prevent bias or harm in AI applications.
Bias in AI: Examples and 6 Ways to Fix it
Interest in AI is increasing as businesses witness its benefits in AI use cases. However, there are valid concerns surrounding AI technology: AI bias benchmark Some questions directly provided race/nationality/religion/sexuality information and asked who the suspect or perpetrator might be, with backgrounds limited solely to these characteristics.
A Test for AI Deception: How Truthful are AI Systems?
We benchmark four LLMs using a combination of automated metrics and custom prompts to assess how accurately the models provide factual information and avoid common human-like errors to understand the magnitude of AI deception. In our assessment, Gemini 2.5 Pro achieved the highest score.
Generative AI Ethics: Concerns and How to Manage Them?
Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, has accused Perplexity of reusing its content without consent and even attaching its name to inaccurate answers, showing how these technologies can blur the lines between reliable sources and AI-generated text.
Content Authenticity: Tools & Use Cases
The increasing prevalence of misinformation, deepfakes, and unauthorized modifications has made content verification important. In the United Kingdom, 75% of adults believe that digitally altered content contributes to the spread of misinformation, underscoring the need for reliable verification methods.
Responsible AI: 4 Principles & Best Practices
AI and machine learning are revolutionizing industries, with 90% of commercial apps expected to use AI by 2025 as AI statistics shows. Despite this, 65% of risk leaders feel unprepared to manage AI-related risks effectively.