On 5th October, MediaNama held a #NAMAprivacy conference in Bangalore focused on Privacy in the context of Artificial Intelligence, Internet of Things and the issue of consent, supported by Google, Amazon, Mozilla, ISOC, E2E Networks and Info Edge, with community partners HasGeek and Takshashila Institution. Part 1 of the notes from the discussion on AI and Privacy are here. Part 2 is here. Part 3: Algorithms are incredibly complex, and it is sometimes impossible to fully understand them, even for creators. Recommendations One of the most common implementations of algorithms come in recommendations, like those on search results or streaming services like Netflix. Anupam Manur from the Takshashila Foundation pointed out: "Recommendations like Netflix movie recommendations may not be harmful, but concentrated recommendations in Twitter will lead to echo chambers, and that can be harmful in an aggregated way." Indeed, filter bubbles are a known phenomenon. Sanjay Jain from iSPIRT argued that tangible harms should be targeted: "The whole question of personalization, recommendations, etc, is going to be a theoretical discussion. We should start from the harm. If a recommendation system causes me harm, and says that because of who I am, I have to pay a higher taxi rate, and the reason why I pay it is because of a certain protected category. Let’s say you’re a female and end up with a higher rate on the taxi company then that becomes unfair. Per se, recommendation systems cannot be argued to be fair or unfair, it comes back again to outcomes,…
