Aspects that can make Artificial Intelligence reliable and trustworthy.

Author(s): Raman Kumar Jha

Artificial Intelligence

Artificial Intelligence is an enormous field that has extreme power that can be used in ways that negatively affect society. To develop and use AI systems responsibly, AI developers must consider concerns regarding ethics, bias, and trust issues. They must think about the ethical concerns inherent in AI. They must have a realistic view of their algorithms and programs and be aware of the different forms which have the bias potential present in their systems. With this correct mindset, developers can minimize or almost avoid unintentionally creating AI systems that have a negative impact than a positive impact on our community.

One of the finest physicists in the world Stephen Hawking expressed his concerns about AI in this way.

The rise of powerful AI will either be the best or the worst thing to happen to humanity we do not yet know which — — — Stephen Hawking

An entrepreneur and business magnate Elon Musk once went even further while saying about AI.

AI is more dangerous than nuclear weapons. — — — Elon Musk

Trust is a key element for developing impactful, helpful, and successful AI systems. For the developers’ community, there are four aspects of Artificial Intelligence that they must consider while developing AI systems so that people could perceive it trustworthy and reliable:

1) Transparency — People should be aware when they are interacting with an AI system and understand what their expectations for the interaction should be. While lengthy, detailed policies could not be helpful for all consumers but this would be a crucial step towards the transparency of AI. People should know whom they are talking to whether it’s a bot or a human and how will it matter in their conversation?

All AI-based companies should replace their current policies with newer policies where they will clearly specify how the data will be taken, stored, and used in the future. So that people will be relaxed and less confused while using these systems.

2) Accountability — Developers should create AI systems with algorithmic accountability so that any unexpected results can be traced and undone if required. AI developers should make the company policies clear and accessible to the development team from day one so that no one will get confused about the issues of accountability.

Through this, the malpractices can be avoided so that people will believe in AI and it will reach more and more people.

3) Privacy — Personal information should always be protected. Developers should always pay attention to the privacy policies precisely. Consumers must be satisfied that their personal information is not going to be sold or shared with any other company. They must be assured that the privacy policies will surely be abided by their company.

Due to these privacy concerns, the European Union has made the rules regarding privacy policies. If any organization is found guilty then they have to pay 22 Million dollars or 4% of their annual global turnover as a fine.

4) Lack of bias — Developers to avoid data bias should use training data that are not associated with any person or identity with their color, gender, or looks. Regular audits are essential to detect any bias getting into the system.

Due to poorly used training data, some AI systems identified scenes showing kitchens, laundry, and shops with women and scenes showing sports coaching and shooting with men. These kinds of issues would create a massive impact on user’s mentality and experience. This should always be considered before building AI systems.

Aspects that can make Artificial Intelligence reliable and trustworthy. was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI