Ladies in AI: Urvashi Aneja is researching the social influence of AI in India


To present AI-focused ladies lecturers and others their well-deserved — and overdue — time within the highlight, TechCrunch is launching a sequence of interviews specializing in exceptional ladies who’ve contributed to the AI revolution. We’ll publish a number of items all year long because the AI growth continues, highlighting key work that usually goes unrecognized. Learn extra profiles right here.

Urvashi Aneja is the founding director of Digital Futures Lab, an interdisciplinary analysis effort that seeks to look at the interplay between know-how and society within the World South. She’s additionally an affiliate fellow on the Asia Pacific program at Chatham Home, an impartial coverage institute based mostly in London.

Aneja’s present analysis focuses on the societal influence of algorithmic decision-making techniques in India, the place she’s based mostly, and platform governance. Aneja just lately authored a research on the present makes use of of AI in India, reviewing use circumstances throughout sectors together with policing and agriculture.


Briefly, how did you get your begin in AI? What attracted you to the sector?

I began my profession in analysis and coverage engagement within the humanitarian sector. For a number of years, I studied the usage of digital applied sciences in protracted crises in low-resource contexts. I shortly realized that there’s a fantastic line between innovation and experimentation, notably when coping with weak populations. The learnings from this expertise made me deeply involved in regards to the techno-solutionist narratives across the potential of digital applied sciences, notably AI. On the identical time, India had launched its Digital India mission and Nationwide Technique for Synthetic Intelligence. I used to be troubled by the dominant narratives that noticed AI as a silver bullet for India’s advanced socio-economic issues, and the entire lack of important discourse across the situation.

What work are you most pleased with (within the AI subject)?

I’m proud that we’ve been in a position to attract consideration to the political economic system of AI manufacturing in addition to broader implications for social justice, labor relations and environmental sustainability. Fairly often narratives on AI deal with the features of particular functions, and at greatest, the advantages and dangers of that software. However this misses the forest for the timber — a product-oriented lens obscures the broader structural impacts such because the contribution of AI to epistemic injustice, deskilling of labor and the perpetuation of unaccountable energy within the majority world. I’m additionally proud that we’ve been in a position to translate these considerations into concrete coverage and regulation — whether or not designing procurement pointers for AI use within the public sector or delivering proof in authorized proceedings towards Huge Tech corporations within the World South.

How do you navigate the challenges of the male-dominated tech trade, and, by extension, the male-dominated AI trade?

By letting my work do the speaking. And by always asking: why?

What recommendation would you give to ladies searching for to enter the AI subject?

Develop your data and experience. Be certain your technical understanding of points is sound, however don’t focus narrowly solely on AI. As an alternative, research extensively in an effort to draw connections throughout fields and disciplines. Not sufficient individuals perceive AI as a socio-technical system that’s a product of historical past and tradition.

What are a few of the most urgent points dealing with AI because it evolves?

I feel probably the most urgent situation is the focus of energy inside a handful of know-how corporations. Whereas not new, this downside is exacerbated by new developments in massive language fashions and generative AI. Many of those corporations are actually fanning fears across the existential dangers of AI. Not solely is that this a distraction from the present harms, however it additionally positions these corporations as obligatory for addressing AI associated harms. In some ways, we’re shedding a few of the momentum of the ‘tech-lash’ that arose following the Cambridge Analytica episode. In locations like India, I additionally fear that AI is being positioned as obligatory for socioeconomic growth, presenting a possibility to leapfrog persistent challenges. Not solely does this exaggerate AI’s potential, however it additionally disregards the purpose that it isn’t attainable to leapfrog the institutional growth wanted to develop safeguards. One other situation that we’re not contemplating critically sufficient is the environmental impacts of AI — the present trajectory is prone to be unsustainable. Within the present ecosystem, these most weak to the impacts of local weather change are unlikely to be the beneficiaries of AI innovation.

What are some points AI customers ought to pay attention to?

Customers have to be made conscious that AI isn’t magic, nor something near human intelligence. It’s a type of computational statistics that has many useful makes use of, however is in the end solely a probabilistic guess based mostly on historic or earlier patterns. I’m positive there are a number of different points customers additionally want to pay attention to, however I wish to warning that we must be cautious of makes an attempt to shift accountability downstream, onto customers. I see this most just lately with the usage of generative AI instruments in low-resource contexts within the majority world — moderately than be cautious about these experimental and unreliable applied sciences, the main focus usually shifts to how end-users, resembling farmers or front-line well being employees, must up-skill.

What’s one of the best ways to responsibly construct AI?

This should begin with assessing the necessity for AI within the first place. Is there an issue that AI can uniquely remedy or are different means attainable? And if we’re to construct AI, is a fancy, black-box mannequin obligatory, or would possibly a less complicated logic-based mannequin just do as nicely? We additionally must re-center area data into the constructing of AI. Within the obsession with huge knowledge, we’ve sacrificed principle — we have to construct a principle of change based mostly on area data and this must be the premise of the fashions we’re constructing, not simply huge knowledge alone. That is in fact along with key points resembling participation, inclusive groups, labor rights and so forth.

How can buyers higher push for accountable AI?

Buyers want to think about your complete life cycle of AI manufacturing — not simply the outputs or outcomes of AI functions. This is able to require taking a look at a spread of points resembling whether or not labor is pretty valued, the environmental impacts, the enterprise mannequin of the corporate (i.e. is it based mostly on industrial surveillance?) and inside accountability measures inside the firm. Buyers additionally must ask for higher and extra rigorous proof in regards to the supposed advantages of AI.

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