Check out Part 1 of the conversation here.In part two of this special edition of Let’s Talk About, the Newslaundry team of Abhinandan Sekhri, Raman Kirpal, Manisha Pande, Anand Vardhan, Shardool Katyayan, and Mehraj D Lone continue their conversation on religion, this time focusing on the intersection of religion and politics.Abhinandan asks the panel whether it thinks mixing religion and politics is inevitable or desirable. He says politics has merely “replaced” religion while still keeping aspects of it intact. Manisha thinks religion is an important part of people’s lives and, therefore, “politics is always going to be a feature of it”.Listen.Subscribe and avail our Independence Day offer: https://bit.ly/NewslaundryIDayOffer See acast.com/privacy for privacy and opt-out information.

In this special edition of Let’s Talk About, the Newslaundry crew of Abhinandan Sekhri, Raman Kirpal, Manisha Pande, Anand Vardhan, Shardool Katyayan, and Mehraj D Lone sit down for a freewheeling discussion about religion, its place and significance in our world.Primarily, the theme is whether religion has done more good or more harm. Abhinandan and Mehraj both argue that religion has done more good than harm while the rest of the panel express mixed views.Subscribe and avail our Independence Day offer: https://bit.ly/NewslaundryIDayOffer See acast.com/privacy for privacy and opt-out information.

Feminism as a social, political and historical phenomenon has assumed many shapes and forms through the ages. But what does feminism mean in the Indian context? Can we chart out a linear history of the movement in India? India’s movement has its own complexities, varying in definition and scope across class, caste, and creed. Gender dynamics and power imbalances have been instrumental in the oppression of women, and suppression and punishment is a tool to curb “transgressions” and maintain the status quo. Women have not only challenged this forced subservience but have, in many instances, overturned these systems too.So, the goal of this podcast is not to define feminism, but to examine India’s social structures and the feminist resistance to them at specific moments in history. See acast.com/privacy for privacy and opt-out information.

When a standard or classification is built on an idea of “normal”, then the decision-making system will fail those outside that idea. It will fail the extraordinary. It’s similar to what happened in the case of athlete Dutee Chand, who went on to win a landmark case that declared void the sporting standard of testosterone levels. After all, nature is not neat. And it’s the outliers that push the species forward – something that automated decision-making systems, including algorithms, don’t really understand. In Dutee's case, understanding the decision making, and the transparency in it, was crucial to her right to redress.In the final episode of Let’s Talk About Big Data, we talk to Laura Reig, a PhD student at Denmark Technical University, on how AI makes mistakes in gender classification, and Chirag Agarwal, a research fellow at Harvard University, about what explainability in AI means. We also talk to Joy Lu, an associate professor at Carnegie Mellon University, on what makes for a good explanation of what an algorithm is doing.Listen. See acast.com/privacy for privacy and opt-out information.

Being perfectly rational is not an evolutionarily viable form of reasoning. It’s slow and requires a lot of information that may not always be available.What helps is reasoning through bias: a cognitive shortcut that is quick, doesn’t require perfect information, gets the job done, and reduces the kind of errors that have existential consequences.Algorithms have biases too. But over time, biases have started reinforcing themselves, further disenfranchising the disenfranchised. So, what do we do about it?We talk to Osoba Osonde, a senior information scientist, co-director of the Center for Scalable Computing and Analysis, and professor at the Pardee RAND Graduate School, to discuss how we can make algorithms fairer. We also talk to Benjamin Boudreaux, a professor at the Pardee RAND Graduate School, about what it means to make algorithms fairer.Listen.  See acast.com/privacy for privacy and opt-out information.

The right to privacy is our right to identity. And as human experience has evolved over time, privacy has changed with it.In this episode, we discuss with Arvind Narrain, cofounder of the Alternative Law Forum, how our understanding of privacy has evolved over time. We talk to Rega Jha about how the internet subverts our privacy without even intending to. We also speak to Rishabh Poddar, who is building a privacy-preserving technology at the University of California, Berkeley. See acast.com/privacy for privacy and opt-out information.

The genius of the metaphor is that it helps us understand complex models we can’t articulate. When the Hindu supremacists say “Bharat Mata”, for example, they are presenting a well understood cognitive model that explains nourishment and loyalty and using it to explain nationalism. When they say “Gau Mata”, the idea of nourishment and our loyalty to it is brought to the cow.In many ways the metaphor epitomises the kind of intelligence computers struggle with. Reasoning by metaphor is a form of reasoning that’s flexible and nimble, unfixed and still resilient. It uses ideas from outside systems to explain systems, maps a cognitive model from a familiar context to another. We, human beings, can do this because a number of things came together for us: we have very sophisticated abilities to speak, we recognise patterns, we understand the unspoken, we can imagine what isn’t in front of us, we have a pool of shared human experience (another metaphor!), we can see concepts and we can see relationships between concepts.So, what does human intelligence do that machine intelligence can’t? What can they both not do? In this episode, we explore the limits of artificial intelligence. We speak to Shubham Bindlish who runs an AI firm that scrapes cricket data to help make predictions for fantasy cricket. We also speak to Joseph Paul Cohen who has built AI that can diagnose diseases from chest x-rays. They shed light on how both their AI infers data and the limitations that come with it.Listen. See acast.com/privacy for privacy and opt-out information.

On July 12, 2020, a team from the National Investigation Agency and the Pune police landed at Sadiya Shaikh’s doorstep. Sadiya, 22, was arrested on suspicion of being in contact with “ISIS sympathisers” and helping to plan attacks in India. It wasn't her first interaction with the police; she had been “deradicalised” at the age of 17. By 2017, ISIS had lost most, if not all, of its territory in the Middle East, but it survived and thrived in the dark corners of the internet, keeping itself on life support.Tune into this snippet of Let's Talk About: ISIS in India, where host Priyank Mathur tries to understand if and how the terror group’s influence has reached India, using Sadiya’s case as context, and how law enforcement agencies have reacted to it. Subscribe to Newslaundry and listen to the full episode here. See acast.com/privacy for privacy and opt-out information.

When I began researching for this, the pilot episode, it was immediately apparent to me that Big Data was fundamentally different from data as we have known it for hundreds of years. We could just do so much more with it. It was changing industries, governments, people, and relationships between these entities rapidly and permanently. But what I couldn’t get a finger on was why.  Why was Big Data different from data as we have known it for hundreds of years? If it’s something ancient, just bigger, why does it change the world?I didn’t have to wait too long to find the answer. In the very first chapter of Victor Meyer Schonberger’s Big Data, a book written about seven years ago, he gets to it pretty straight. When things change a lot in scale, they begin to change in essence also. If the rate of change is dramatic (double differential is out of whack), the thing itself begins to change. This is what makes Big Data essentially different from data ever before in human civilization. This is how he explains it: an image is an image is an image. But speed these images up so the brain sees them as a continuum, and what you have made is a movie. A change in speed led to a change in essence. Here’s another example: gravity acts on all of us, but if you’re really small, like an insect, it barely has any effect on you. Again, change scale dramatically, and the essence begins to change. A glass of water is fundamentally different from a tsunami. A drizzle is fundamentally different from a hurricane. Think about the coronavirus pandemic. We’ve had economic downturns before. We’ve had unemployment before. We’ve had urban-rural migrations before. We’ve all experienced sickness before. Viruses come and go every year. We’ve had natural disasters before. What makes this pandemic so destructive is the scale and speed of it. The coming together of mass unemployment, global economic collapse, mental health falling off a precipice, distorted patterns of living and migration and human behaviour. Together, at this scale and at this speed, the essence of life today is different from the essence of life at the beginning of 2020. The idea that after a point quantity and quality are inherently related is not a new one. It’s one of the three forgotten laws of Dialectical Materialism which inspired a lot of Karl Marx’s work. When quantity changes a lot, after a point, quality begins to change as well. This is the idea that explains why big changes in wealth distribution bring big changes in the essence of social structures.This law, in turn, comes from a  much older philosophical idea: Aristotle’s Heap Paradox. This is the Heap Paradox: if you have a heap of sand and you remove one grain from it, it’s still a heap. Remove one more, it’s still a heap. One more, it’s still a heap. But if you do this long enough, if you scale up the removal of grains, it’ll no longer be a heap. The essence of the sand heap will change. This is at the core of understanding what Big Data is. When the quantity of data changed, its essence changed as well. Listen. See acast.com/privacy for privacy and opt-out information.

It is estimated that a child goes missing in India every eight minutes. That’s one full classroom of students disappearing every few hours. Almost 40% of these children are never found. Families are torn apart.Tune into this snippet of Let’s Talk About where Newslaundry’s Snigdha Sharma delves into the complexities of child trafficking in India: how and why is it so prevalent? What forms does it take? Who is most vulnerable? What is being done to prevent it? Through a series of sobering interviews, she speaks to victims, experts, and journalists on the frontlines to understand the many aspects of India’s child trafficking crisis.Subscribe to Newslaundry and listen to the full version here.https://www.newslaundry.com/subscription See acast.com/privacy for privacy and opt-out information.