About Me

I am a Technology Fellow at Irish Council for Civil Liberties (ICCL) where I work on technology-policy with a focus on algorithmic decision making, surveillance, data rights, and privacy. Previously I was a researcher at TU Darmstadt where I worked on applied cryptography, privacy enhancing technologies (PETs) and Internet infrastructure security.

Recent Blog Posts


We make many decisions during our lifetime. Some more consequential than others. Some that rely on too few choices. Some where we are overloaded with choices.

There are times when we make a decision and move on. Then there are others when we go back to a decision and regret the road not taken. To decide, what kind of choices do we have?

An excess of choice could lead to the paradox of choice. It might seem that having plenty of options is always good and that you should be able to make the best decision. However, too many choices could overwhelm you, making it hard to decide.

There is more to AI than data-hungry AI

The European Union is regulating artificial intelligence (AI) systems under incorrect assumptions that could result in undesirable outcomes. The EU is assuming more data is always useful to AI and incentivising “big data” approach.

AI is not restricted to data-hungry techniques. There are several alternative techniques that rely on less data, require less computation and less memory. They are more in line with the principle of data minimisation in the GDPR. The Commission should take a smarter approach and incentivise AI techniques that require less data, that benefit society and that assists with climate change mitigation.

More data is not always useful for AI

EU Commission believes “AI can only thrive when there is smooth access to data [emphasis added].”1 It believes that it is in “the nature of AI” to rely on “large and varied datasets [emphasis added].”2 It believes more data automatically produces more insight and understanding. The AI Act, the Data Governance Act and the Data Act assume that more data is needed for AI.3 These beliefs are wrong.

What matters is not how large the datasets are, but the statistical properties of datasets such as how varied the data are. The context in which data is collected is also important. However, the Commission seems to believe that all data is interchangeable. Instead of focusing on “re-use, sharing and pooling of data,”[^12] the Commission should recognise that more data is not always useful to AI.

  1. Communication on Fostering a European approach to Artificial Intelligence, 21 April 2021. p.2.↩︎

  2. Proposal for a Regulation of the European parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts, 21 April 2021., p. 6↩︎

  3. Ibid., p. 5: “the promotion of AI-driven innovation is closely linked to the Data Governance Act, the Open Data Directive and other initiatives under the EU strategy for data, which will establish trusted mechanisms and services for the re-use, sharing and pooling of data that are essential for the development of data-driven AI models of high quality.”↩︎

While waiting

We spend much of our life waiting. We wait for someone. We wait for something.

We often wait. But, not all waits are made the same. Waiting for the train that we take every weekday morning is not the same as waiting for a friend to arrive. We know how long we have to wait for the train to arrive. At least approximately. We may not know when the friend will arrive if they are already late.

There are other times when the wait is longer. When you are waiting to receive the reviews for the first academic article you submitted to a conference or journal. When you wait for the approval of a visa application and don’t know whether it will be approved. We know how long we need to wait and yet, we are anxious.

In Agnès Varda’s film Cléo from 5 to 7 (1962), we see Cléo wait 90 minutes1 for her medical test results. She suspects that she might have cancer. In the beginning, we see her wait anxiously. She asks the opinion of a tarot card reader, shops with her assistant, practices a song with a composer who comes to her apartment and has a superficial afternoon chat with her lover.

She does everything possible not to feel that she is waiting. When she is not occupied with others, she is occupied with herself. She is surrounded by mirrors. Just as mirrors in elevators in high rises make us feel that the wait is shorter, Cléo uses mirrors to distract herself. But then, she is frustrated with what she sees, until she does not need to see herself in the mirror.

  1. The film runs from 5pm to 6:30pm. 5 to 7 is a joke. See Agnès Varda. Cléo from 5 to 7↩︎

Template-based facial recognition

This post is an edited version of my Twitter thread from 3 November 2021.

On 2 November 2021, Facebook announced that they will delete the data and shut down the facial recognition system on Facebook.

Which data is being deleted? Facebook’s blogpost does not say that they will delete the models that were generated using the data. It also does not say that they will not use people’s image data to train models. It only says “we will delete more than a billion people’s individual facial recognition templates.” Is deleting templates enough?

What are these templates? Templates are not images. Templates are generated using images. To understand how templates fit into a facial recognition system, it can be useful to understand the different steps involved in a template-based facial recognition system 1. Here is a simplified version of the steps involved:

  1. Image collection: A collection of images. People upload images in Facebook. So, collection is easy.
  2. Creating templates: Using a combination of algorithms to process the collected images of a person to smooth out non-facial elements and the background.

  1. Template-based facial recognition is only one of the methods. There are other methods for facial recognition systems such as relying on specific features of the object such as the boundary, shape, colour, etc.↩︎

Sun and shade

I enjoy the sun. Not for any particular reason. I enjoy the sun just as I enjoy the clouds, the rain and the snow. They are in the nature. I enjoy them as they are.

I have many friends who place the sunlight on a pedestal. They value the sun much more than many other natural elements. They have a much more favourable view of the sunlight than the rain, for instance. This is in part because they have grown up in parts of the world where sunlight and the warmth it offers is not prevalent throughout the year. They make the most of the opportunities they get to bask under the sunlight.

Would they feel the same if the sun was beating down, day after day, without any possibility to find shade? Many who live in hotter parts of the world, especially closer to the equator experience the sun in this way. Many others, even those further away from the equator, have tasted days when the heat was too much to bear.

The sun can also be used as a metaphor as in the film A Sun (2019). The elder son, Hao, of A-Wen is brilliant. He is the sun of the family. His parents are proud of him. His brother hates him for being brilliant. He is shy, helpful and caring. People around him know that he is thoughtful. But, they have no idea what he is thinking.

He hardly puts a foot wrong. He is garnered with praises at almost all times. But, how does he feel? Does he bask in the sunlight of praises? Does he expect to be praised regularly? Is he afraid of making mistakes? His family and friends do not know the answers to these questions. Not until, they read his last message before he jumped off a building.

Hao was exposed to so much sun that he felt as if he was being burnt. Unlike many humans and non-human animals, he felt he “had no water tanks and no hiding places, but only sunlight1.” He was exposed to the uninterrupted supply of sunlight. He was not seeking a place to get tanned. The sun was hunting him down and burning him.

  1. Hao’s last message in A Sun (2019)↩︎