With its problem-solving capabilities, Artificial Intelligence (AI) could very well be the unrivalled solution to the most intractable problems of our time. However, AI might not just yet be the icing on the cake we were looking for- there
are significant constraints to be considered that prevent it from being a perfect solution to all possible problems, and the debate on ethics of Artificial Intelligence remains ridden with complexities.
Artificial Intelligence (AI) has the potential to radically alter almost all spheres of human life. This is because it allows for quick processing of vast amounts of data and makes predictions or decisions that would be otherwise rather impossible for humans to do in a reasonable amount of time. AI holds the potential to substantially improve efficiency, accuracy, and safety in many fields beyond human capabilities. But is that enough?
AI also poses several dangers, such as the potential for bias and discrimination if the data used to train AI systems, made by humans themselves, is not diverse or representative of all populations. There is also the risk of job loss as AI takes over tasks previously performed by humans. Additionally, there are widespread concerns about AI being used for malicious purposes, such as cyber attacks or weapon-building and spreading misinformation.
To maximise the benefits of AI while minimising the risks, it is important for stakeholders like business magnates, technologists, policymakers, and society as a whole to consider the ethical implications of AI and ensure that it is developed and used responsibly.
AI and human intelligence: Are they the same?
Artificial intelligence and human intelligence are two distinct types of intelligence, each with its strengths and limitations. One of the key differences between artificial and human intelligence is the way they learn. AI systems attempt to replicate forms of human intelligence. However, even though AI can process vast amounts of information quickly and accurately, it can still come with its own limitations.
Risks posed by AI:
The fears that AI can take over all human jobs can be misleading. As Adiraj Chakraborty noted in his 2019 Letter to EPW, ‘Artificial vs Human Intelligence’ “Most of the jobs that we can think of require a multiplicity of skills. From technical expertise to intuitive mastery, jobs generally require a host of cognitive and socio-behavioural skills that are interconnected.”An EPW Engage article titled, ‘Interrogating the AI Hype: A Situated Politics of Machine Learning in Indian Healthcare’ authored by Radhika Radhakrishnan highlights the need to prioritise a more human-centred approach to technology development and deployment, to ensure that the benefits of technological advancements are equitable and accessible. This is due to the possibility that AI can, she warns, exacerbate existing inequalities or perpetuate the profit-driven interests of private corporations at the expense of marginalised communities. She suggests that “instead of asking, “How can AI solve this problem?” it is most worthwhile to ask “What problems can AI solve?” This has at least two advantages: one, it makes us focus on using AI-based technologies to solve only those problems that are within the scope of technology to solve, and two, it incentivises us to find need-based solutions as opposed to market-driven capitalist solutions to identified problems.”
Others even question the permeation of AI in creative spheres such as that of art. Neerej Dev and Vipula P C in their 2023 letter ‘Artificial Intelligence and Art’ show that there is great scope to philosophically investigate and “question the ethics of using artificial intelligence for commercial purposes without giving proper credit to the original creators.”
Additionally, large language models, such as ChatGPT, could exacerbate the concentration of information in the hands of a few, with the resources to develop and deploy such models. This trend goes against the principle of decentralisation, where power and access to information are distributed across broader networks.
Conclusion
These examples point towards the fact that there is a need to tread forward with caution. Artificial intelligence needs to be imagined in a way that it does not override ethical norms, it should be more inclusive, and we should aspire to make data and algorithms more accountable, by enabling transparency and initiating safeguards in its creation and deployment.
