Context: Highlighting the need for a value-based global AI governance framework to realise the true potential of the technology.
AI for good: Google has identified over 2,600 use cases of “AI for good” worldwide.
Unprecedented growth: From beating human champions at Jeopardy in 2011 to vanquishing the world’s number one player of Go, to decoding proteins.
Current usage: Embedded in recommendations on streaming or shopping site, in GPS mapping technology, in the predictive text that completes our sentences etc.
The unlimited scope: AI can leapfrog us toward eradicating hunger, poverty and disease, opening up new and hitherto unimaginable pathways for climate change mitigation etc.
Direct benefits: AI has helped increase crop yields, raised business productivity, improved access to credit and made cancer detection faster and more precise.
Towards SDGs: A study published in Nature reviewing the impact of AI on Sustainable Development Goals (SDGs) finds that AI may act as an enabler on 134 or 79% of all SDG targets.
Economic potential: Can contribute >$15 trillion to world economy by 2030, adding 14% to global GDP.
Concerns of AI
Hindering SDGs: Study in Nature finds that AI can actively hinder 59 or 35% of SDG targets.
As AI requires massive computational capacity resulting in a big carbon footprint
Compounds digital exclusion: AI taking over jobs of low/middle income workers: E.g. Self-service kiosks to replace cashiers, fruit-picking robots to replace field workers, etc.
New inequalities: Without clear policies on reskilling workers, it will lead to new inequalities -
AI-related investments will shift to countries where AI is established, widening gaps among and within countries.
E.g. Big Tech’s big four, Alphabet/Google, Amazon, Apple and Facebook, added $2 trillion to their value in 2020, when the world was reeling under the impact of the pandemic.
Inherited biases: E.g. AI facial recognition and surveillance technology discriminating against people of colour and minorities and AI-enhanced recruitment engine being biased against females.
Data privacy concerns: The algorithm’s never-ending quest for data has led to our digital footprints being harvested and sold without our knowledge or informed consent.
E.g. Case of Cambridge Analytica, where such algorithms and big data were used to alter voting decisions.
Way forward
Establish ethical guard rails: Develop broad-based ethical principles, cultures and codes of conduct to inculcate transparency, accountability, inclusion and societal trust for AI.
“Whole of society” to“whole of world” approach: Need for wider platforms and collaborations.
UN Secretary-General’s Roadmap on Digital Cooperation: so that AI is used in a manner that is “trustworthy, human rights-based, safe and sustainable, and promotes peace”.
UNESCO: Developed a global, comprehensive standard-setting draft Recommendation on the Ethics of Artificial Intelligence.
Striking the right balance: Between AI promotion and AI governance.
NITI Aayog’s Responsible AI for All strategy: Recognises the importance of multi-stakeholder governance structures that ensure dividends are fair, inclusive, and just.
Agreeing and implementing common guiding principles: Real challenge lies in practically implementing the framework upholding right values.