Ethical Challenges in AI for Social Good

It’s day 23, and we’re diving into the ethical challenges of using AI for social good.

While AI has the potential to benefit society, it’s important to consider the ethical implications. For instance, facial recognition technology used for humanitarian purposes raises privacy concerns and the risk of misuse. AI interventions in public services like healthcare and education lead to complex ethical dilemmas, such as who to hold accountable for upholding standards, especially when things go wrong.

Further complicating matters is the bias inherent in AI algorithms. These biases are often a reflection of existing societal values and expectations, which can systematically oppress minorities. In the realm of 'Tech for Good', it's critical to ensure that AI doesn’t perpetuate these biases. After all, if there's bias in the data, there's bias in the output.

We also know that AI will lead to widespread job displacement, with many current jobs being wholly or partially automated. This presents new challenges for organisations that exist for more than just profits; the temptation to harness AI to do more with less, needs to be carefully balanced with the interests of the people that might face disruption as a result. Instead, these organisations should consider using AI to fill gaps in capability that they can’t afford to recruit for.

AI involves considerable computational expenses. Research from the University of Massachusetts Amherst revealed that the energy used to train just one generative AI model could amount to the usage of around 284,000 litres of water. This quantity is comparable to the water consumption of an average individual over a span of 27 years.

These ethical dilemmas highlight the need for careful consideration and responsible use of AI in social contexts.