AI algorithms have massive implications for human life and the wider society.
Ethical dilemmas surrounding AI include digital disparities and its weaponization
Autonomy should be balanced with human oversight while the responsible use of AI should be elevated, so it can be leveraged to tackle discrimination.
Copyright: weforum.org – “How the responsible use of AI can create safer online spaces”
Artificial intelligence (AI) has become an everyday reality and business tool spurred by computer advancement, data science and the availability of huge data sets. Big tech companies – Google, Amazon and Meta – are now developing AI-based systems. The technology can mimic human speech, detect cancer, predict criminal activity, draft legal contracts, solve accessibility problems, and accomplish tasks better than humans. For businesses, AI promises to predict business outcomes, improve processes and deliver efficiencies at substantial cost savings.
But there are growing concerns with AI, still.
AI algorithms have become so powerful – with some experts labelling AI as being sentient – that any corruption, tampering, bias or discrimination can have massive implications on organizations, human life and society.
AI decisions increasingly influence and impact people’s lives at scale. Using them irresponsibly can exacerbate existing human biases and discriminatory measures such as racial profiling, behavioural prediction or sexual orientation identification. This inbuilt prejudice occurs because AI is only as good as the amount of training data we can provide, which can be susceptible to human biases.
Biases can also occur when machine learning algorithms are trained and tested on data that under-represent certain subpopulations, such as women, people of colour or people in certain age demographics. For example, studies show that people of colour are particularly vulnerable to algorithmic bias in facial recognition technology.
Biases can also occur in usage. For example, algorithms designed for a particular application may be used for unintended purposes for which they were not built, which results in misinterpretation of outputs.
Validating AI performance
AI-led discrimination can be abstract, un-intuitive, subtle, intangible and difficult to detect. The source code may likely be restricted from the public or auditors may not know how an algorithm is deployed. The complexity of getting inside an algorithm to see how it’s been written and responding cannot be underestimated.[…]
Read more: www.weforum.org