With increasing concern for data privacy, there have been several measures taken up to make AI applications privacy friendly. Of many such measures, the most commonly found and practiced method is Federated Learning. While an entire blog post will be dedicated to know how it works and its current application, this post is about yet another least discussed and probably a more theoretical approach as of now, and that is Machine Unlearning. There have been limited yet substantial research work done in this domain with diverse approaches used by the researchers to attain the objective. As the name suggests, an […]
Explainability vs. Confidentiality: A Conundrum
Ever since AI models have rendered biased results and have caused a major deal of dissatisfaction, panic, chaos, and insecurities, “Explainability” has become the buzz word. Indeed it’s genuine and a “Must-have” for an AI based product. The user has the right to question, “Why?” and “How?”. But how much of these queries are enough to set “Explainability” score? In other words, how much of the response to such queries by the model are enough to exceed “Confidentiality” threshold? For an ordinary user, may be a satisfactory response is enough as an explanation. But it’s not enough for a curious […]
AI : Let’s Get Serious
AI is ubiquitous and is finding its application in almost all domains, be it for simple sentence correction purpose or space navigation. The analogy of how AI behaves and thinks like a human, gives an impression that AI is quite simple and does not include much complicated programming. However, the seemingly simple technology of AI equally requires a lot of ground work to not just make it act like a human but also with greater deal of humanity. AI is not like just any other technology and yet is not any different either. Imagine teaching your toddler how to ride […]