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What are the challenges of using artificial intelligence?

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posted Feb 25 by Meghana K S

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Artificial Intelligence (AI) is a source of both enthusiasm and skepticism, albeit in different measures. With humans and machines joining forces now more than ever before, AI is no longer confined to innovation labs and is being hailed for its immense transformational possibilities. However, businesses need to overcome certain challenges before they can realise the true potential of this emerging technology. The key lies in leveraging the right opportunities in AI.

Provability
Organisations involved in AI cannot demonstrate clearly why it does and what it does. No wonder AI is a “black box”. People are skeptical about it, as they fail to understand how it makes decisions. Provability – the level of mathematical certainty behind AI predictions – remains a grey area for organisations. There’s no way they can prove or guarantee that the reasoning behind the AI system’s decision-making is clear. The solution lies in making AI explainable, provable, and transparent. Organisations must embrace Explainable AI as a best practice.

Data privacy and security
Most AI applications rely on huge volumes of data to learn and make intelligent decisions. Machine Learning systems feast on data – often sensitive and personal in nature – to learn from them and enhance themselves. This makes it vulnerable to serious issues like data breach and identity theft. Here is some good news; the increasing awareness among consumers about the growing number of machine-made decisions using their own personal data, has prompted the European Union (EU) to implement the General Data Protection Regulation (GDPR), designed to ensure the protection of personal data. Besides, an emerging method – ‘Federated Learning’ – is all set to disrupt the AI paradigm. It will empower data scientists to develop AI without compromising users’ data security and confidentiality.

Algorithm bias
An inherent problem with AI systems is that they are only as good – or as bad – as the data they are trained on. Bad data is often laced with racial, gender, communal or ethnic biases. Proprietary algorithms are used to determine who’s called for a job interview, who’s granted bail, or whose loan is sanctioned. If the bias lurking in the algorithms that make vital decisions goes unrecognised, it could lead to unethical and unfair consequences. For instance, Google Photos service uses AI to identify people, objects and scenes. But there’s a risk of it displaying wrong results, such as when a camera missed the mark on racial sensitivity, or when a software used to predict future criminals showed bias against black people.

answer Feb 26 by Rashmi V
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