Artificial Intelligence has had a magnanimous impact on our lives ever since the movies featuring robots began to highlight their role in our future. It has brought about a major change in every sphere of our lives. Correspondingly, data scientists, mathematicians, and coders have consistently scaled an extra mile to breathe life into the current dream. But, as Uncle Ben rightly said, “With great power, comes great responsibility”.
Businesses, which are looking forward to tap into the true potential of artificial intelligence (AI), have realized that it is imperative to train the technology to act and be used in a certain way. Just like a kid, it will not understand what to do with stuff (in this case, data) without proper training and guidelines. This process of training the technology to act responsibly is termed as Citizen AI.
AI has penetrated almost all spheres of our lives. From Siri or Google Assistant in mobile phones to Alexa in houses, human dependency on this technology is increasing gradually. However, it is important that this dependency must transform into a concurring co-existence. For this, business leaders and their respective teams are working towards the ’how’ of training AI to be unbiased. Broadly, this calls for the establishment of a framework aligned with the heightened machine capacity as well as human ethical grounds.
When AI will work for the advancement of business, they just don’t need to give out fast responses and eliminate the time consuming factor. The idea of its integration can only be fruitful when the system’s judgement can be trusted. This is accompanied by the ability of the system to explain the reasoning and approach behind the displayed output. Something more than probability figures and language processing estimates.
This is where data scientists and AI enthusiasts come into the picture. Semantic networks, knowledge graph understanding, deep learning, etc, are under constant permutations to place the ethical guidelines and reasoning at the system’s core. The fundamental concept of AI is based on learning and adaptive learning via various inputs and insights of data. However, if the training is not done right, the reported cases of AI’s self-understood behavior including Microsoft’s Tay chat bot and Facebook’s AI program shutdown, are subject to increase.
Paul Daugherty, Chief Technology & Innovation Officer at Accenture states that there are 5 key principles associated with creating a responsible AI:
This refers to decisions made by the machine with the provided inputs. It is crucial in determining the useful quotient of the output.
How are decisions made? It is not just about one plus one becoming two, it is about the approach taken by the technology.
Since data is the sole input, it is important to determine if certain classifications in it are not leading to an output that may not act in the best interests of a certain culture or community.
System intelligence cannot be the justification for not abiding with the legal presets.
The technology and supported system run in sync with the humans working so as to provide the best possible output for the business growth.
It is for a fact that training AI is not a piece of cake, but once achieved, it is bound to give the business’s competitors, a run for their money.