Damini Satija is a Human Rights and Public Policy Professional, as well as Head of the Algorithmic Accountability Lab and Interim Director at Amnesty Tech. Satija has experience working on data and AI, with a focus on government surveillance, algorithmic discrimination, welfare automation, and tech equity and justice. She has her Master of Public Administration (MPA) from Columbia University, with a specialization in tech policy, and a BA in Economics from the University of California, Berkeley.

In this episode, she and Gutu discuss how:

• Bias and discrimination generally emerge in AI algorithms
• Human rights implications play a big role in data and consequently, in policy and regulation
• We need to understand what needs to be addressed to properly mitigate AI harms... is it the model that should be optimized or the data (i.e., model-centric vs data-centric)?
• Our biases are codified
• We can go about ensuring more inclusivity, more representation, and less bias in tech
• Net neutrality, encryption laws, copyright, and content moderation effect us
• AI is playing an increasingly bigger role in Hollywood, art, and media. Is it possible to reclaim our data? Is data ownership a myth? What are the implications of assigning property rights to personal data?
• The hype of ChatGPT and GenerativeAI are overdone; and how environmentally unsustainable they are. Should ChatGPT be trained on people's writing, such as their books, articles, and/or poetry? How do property rights and copyright law apply?
• To be more mindful with technology and the ways it uses our data

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