Pursuing AI Accountability with Tuhin Sarwar

Tuhin Sarwar’s AI Accountability category examines AI behaviour, malfunctions, and social impact. Algorithmic decision-making, bias, real-life misinformation, and AI responsibility are its topics.

This category also explains why AI can produce inaccurate results, why the same prompt can give different results, and how hallucination can mislead readers and decision-makers. These reports teach AI literacy and ethical technology use.

Thus, general readers, journalists, researchers, and policymakers trust AI accountability to provide verified, evidence-based insights into emerging AI risks and accountability issues.

What AI Accountability Covers
This category discusses AI’s pros and cons. It also teaches how to use AI tools responsibly and spot false results.

AI hallucination, deception, verification
AI answers users differently. Why?
Discriminatory algorithmic choice
Deepfakes, digital trust crises, synthetic media
AI surveillance, privacy risks, and digital authoritarianism
Citizens and journalists need responsible AI literacy.
Key Topics in This Category
AI accountability connects people and technology. AI-driven societies also promote accountability, transparency, and harm prevention. ties.

AI Hallucination & Verification
AI hallucination can generate false but convincing information. This section explains how to verify AI-generated claims using credible sources and investigative techniques.

Bias, Discrimination & Algorithmic Harm
AI systems may reflect bias from datasets, training environments, or design decisions. Consequently, they can amplify inequality and harm vulnerable communities.

Responsible AI Use for Readers & Journalists
AI tools can assist research and writing, but misuse can create misinformation. Therefore, this category offers practical guidelines for ethical and responsible use.