Ethics in AI: Are we there yet?

As the tech space is getting increasingly fascinated by AI and its capabilities, many are turning a blind eye to the darker side of it. With great power comes great responsibility―this holds true, especially in the world of AI and machine learning (ML).

While automating and expediting tasks, AI-based models have been found to be exhibiting bias on several occasions. AI systems can also be hacked. As we rely more and more on automated decision-making, bad actors will and can employ techniques like adversarial machine learning and data poisoning to hack our AI systems.

Last month, Twitter announced a bounty challenge for detecting bias in its image cropping algorithm. The announcement comes months after its algorithm was found to be occasionally discriminating based on race and gender.

However, this is not an isolated case. In the last few years, several companies have discovered underlying bias and discrimination in their AI systems. For example, in 2018, Amazon had to scrap its AI recruiting tool after it was found to be unintentionally favoring men for open technical roles. Such biases could cause dangerous repercussions if utilized for making important decisions, for example, in credit decisions, criminal justice, healthcare, etc.

The biggest problem is that it is difficult to find bias in these AI models, and the damage may already be done by the time the bias is detected. When AI-based systems are fed with data to learn and derive outcomes, they end up amplifying embedded biases present within the data. Even datasets with billions of pieces of information may be biased and not diverse enough.

Contrary to popular belief, just avoiding parameters like age, gender, race, etc., in your AI model doesn’t mean you have eliminated the bias. It is crucial to audit the datasets are diverse and understand how the system is making decisions. But this requires more governance and rigor around AI.

The debate around ethical AI has been at the forefront of AI development for quite some time now. How we incorporate ethics and governance into AI is a challenge that needs to be addressed. It is imperative if we aim to make AI more accurate and useful.