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To giveAI - focused womenacademics and others their well - deserved — and delinquent — clock time in the spotlight , TechCrunch is launch aseries of interviewsfocusing on singular women who ’ve contributed to the AI revolution . We ’ll publish several piece of music throughout the year as the AI boom continues , highlight central piece of work that often goes unrecognised . Read more profileshere .
Urvashi Aneja is the establish director of Digital Futures Lab , an interdisciplinary research effort that seek to analyse the fundamental interaction between technology and society in the Global South . She ’s also an associate fellow at the Asia Pacific program at Chatham House , an independent policy institute based in London .
Aneja ’s current inquiry focalise on the societal impact of algorithmic decisiveness - make systems in India , where she ’s based , and platform governance . Aneja recently author a study on the current uses of AI in India , reviewing use cases across sectors including policing and agriculture .
Q&A
Briefly , how did you get your start in AI ? What attracted you to the playing area ?
I pop out my calling in inquiry and insurance employment in the humanitarian sector . For several years , I meditate the purpose of digital engineering science in protracted crises in low - resourcefulness contexts . I quickly learned that there ’s a fine line between innovation and experiment , particularly when dealing with vulnerable populations . The learnings from this experience made me deeply implicated about the techno - solutionist tale around the potential of digital technologies , peculiarly AI . At the same clip , India had launched itsDigital Indiamission andNational Strategy for Artificial Intelligence . I was bother by the dominant narratives that saw AI as a silver bullet for India ’s complex socio - economic problems , and the complete lack of critical discourse around the issue .
What work are you most majestic of ( in the AI theatre of operations ) ?
I ’m proud that we ’ve been able-bodied to draw attention to the political thriftiness of AI production as well as blanket implication for societal justice , labor relations and environmental sustainability . Very often story on AI nidus on the gains of specific applications , and at best , the benefits and risks of that app . But this misses the forest for the trees — a product - oriented lens obscures the broad structural encroachment such as the contribution of AI to epistemic injustice , deskilling of travail and the protraction of unexplainable power in the majority earthly concern . I ’m also proud that we ’ve been able-bodied to render these care into concrete policy and regulation — whether designing procurance guidelines for AI use in the public sector or delivering evidence in legal proceedings against Big Tech company in the Global South .
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How do you navigate the challenges of the male - dominated technical school industriousness , and , by extension , the male person - dominate AI diligence ?
By letting my work do the talk . And by constantly ask : why ?
What advice would you give to women seeking to enter the AI playing area ?
get your cognition and expertness . Make certain your technical understanding of issue is sound , but do n’t focus narrowly only on AI . Instead , study widely so that you may draw connections across fields and disciplines . Not enough people understand AI as a socio - technical system that ’s a product of history and polish .
What are some of the most pressing issue facing AI as it evolves ?
I think the most pressing issue is the engrossment of power within a handful of technology companies . While not new , this problem is exacerbated by unexampled development in large spoken communication models and generative AI . Many of these companies are now fanning fears around the existential risk of infection of AI . Not only is this a distraction from the subsist harms , but it also lay these companies as necessary for addressing AI - have-to doe with harm . In many way , we ’re losing some of the momentum of the “ tech - whip ” that arose keep an eye on the Cambridge Analytica sequence . In places like India , I also worry that AI is being pose as necessary for socioeconomic growth , present an opportunity to leapfrog persistent challenges . Not only does this exaggerate AI ’s potential drop , but it also disregard the point that it is n’t potential to leapfrog the institutional growing take to produce safeguard . Another military issue that we ’re not consider seriously enough is the environmental impacts of AI — the current trajectory is likely to be unsustainable . In the current ecosystem , those most vulnerable to the impacts of climate change are improbable to be the beneficiary of AI innovation .
What are some issues AI user should be aware of ?
substance abuser need to be made mindful that AI is n’t magic , nor anything tight to human intelligence . It ’s a mannequin of computational statistics that has many good use , but is at long last only a probabilistic guess found on historic or previous patterns . I ’m sure there are several other issues user also require to be aware of , but I need to monish that we should be leery of attempts to lurch responsibility downstream , onto exploiter . I see this most of late with the role of productive AI tools in gloomy - imagination context of use in the majority world — rather than be cautious about these experimental and treacherous technologies , the focus often shift to how end - users , such as farmers or front - line health worker , want to up - skill .
What is the best way to responsibly build up AI ?
This must start with evaluate the need for AI in the first place . Is there a trouble that AI can uniquely puzzle out or are other mean value possible ? And if we ’re to build up AI , is a complex , black - box model necessary , or might a simple-minded logic - based model do just as well ? We also take to re - center domain cognition into the building of AI . In the fixation with self-aggrandizing data point , we ’ve sacrificed possibility — we need to progress a theory of variety based on arena cognition and this should be the basis of the models we ’re building , not just with child datum alone . This is of course in addition to key issues such as involvement , inclusive teams , labour rights and so on .
How can investor best push for responsible for AI ?
Investors take to study the total biography round of AI output — not just the outputs or resultant of AI applications . This would command looking at a image of subject such as whether labor is somewhat valued , the environmental impacts , the business example of the company ( i.e. is it ground on commercial surveillance ? ) and internal answerability measure within the ship’s company . investor also need to ask for estimable and more rigorous evidence about the supposed benefits of AI .
