Artificial Intelligence (AI) with the concept of common sense has the immense potential of benefiting our society, at large.
However, the main disturbance is how such realism, particularly in the mimicking of human form will result in increased acceptance and trust for AI devices.
Alternatively, we may argue that AI devices have the ability to tackle biases, and as such, are controllable.
The capabilities of AI machines are much dependent on the data you feed in them. Bad data could contain gender, racial and ideological biases.
However, many AI-based machines will be trained with bad data, thus making it an ongoing issue. But, technology experts believe that biases can be easily tamed and in fact AI machines that are capable of tackling human biases will attain maximum success.
It must be noted that both machines and humans look to avoid biases and thus prevent any kind of discrimination. Therefore, as AI is being increasingly adopted, the problem of reducing biases in the AI systems is also being escalated.
Bias in Artificial Intelligence based systems occur within the algorithmic or data model. When organizations come together to create AI systems that can be trusted, it’s important to develop and train these machines using data that isn’t biased. What is vital is to create algorithms that are easy to explain.
It is also crucial to constantly identify and mitigate biases in order to build trust and to ensure that these technologies will impact the society positively. To accomplish this, researchers in technological companies can focus their efforts on developing automated algorithms that are capable of detecting biases.
These algorithms can be adequately trained and created to mimic the anti-bias process in humans so as to facilitate decision making as well as mitigate the inbuilt biases.
This can also include evaluating how consistently we (or AI systems) take decisions. If there are any difference within the solution selected for two distinct problems, even though fundamentally,each situation is similar, then in such case there can be bias against or a couple of non-fundamental variables.
In terms of humans, this can emerge as xenophobia, racism, ageism or sexism.