Four Challenges with Using AI in Tech

Artificial intelligence (AI) is a fascinating and fast growing technology, and one that now underpins a $241. 80 billion market. Between now and 2030, this market is expected to grow at a CAGR of 17.30%, as the technology unlocks more applications and become increasingly mainstream. 

Artificial intelligence is certainly the talk of the tech industry at present, with the undeniable success of language models and other machine learning algorithms pointing towards a future indelibly linked with smart programming. But as people are wowed by the capabilities of ChatGPT, wider ethical and professional concerns continue to mount; what are some of the key challenges, then, facing the tech industry when it comes to AI?


One of the leading concerns relating to recent AI innovation is that of privacy. The protection of private and sensitive data remains a primary concern in digital circles, a topic that has been at the forefront for decades upon decades. This issue continues to be a major focus for ethics scholars, who emphasize its importance in the evolving digital landscape. In response to these concerns, the development and implementation of legal tools that utilize AI technology have become increasingly relevant. These AI-powered legal tools are designed to address data privacy challenges, ensuring compliance with evolving regulations and safeguarding sensitive information in an increasingly digital world. Indeed, AI presents fresh challenges, which deserve scrutiny.

For one, AI algorithms are not airtight things, and the speed with which companies have rolled out AI ‘helpers’ has illuminated this well. Some have been able to ‘trick’ AI chatbots into changing the way they ‘speak’, illustrating the ease with which integrated systems could be fooled into giving out confidential data. For another, the collection of personal data by websites could be mirrored by AI, raising ethical concerns.


This brings us to the discussion of AI and ethics in a wider sense, where key questions need answering before AI can be reasonably rolled out as a new tool commercially and privately. Businesses, for example, should be examining the ethical and professional ramifications of AI integration on a case-by-case basis, ensuring that only positive consequences emerge from such integration.


One part of ensuring these positive consequences, outside of ensuring the quality of the AI system itself, is guaranteeing transparency as much as possible. AI algorithms are, as a function of their design, opaque. Machine learning is a process that happens, figuratively, behind closed doors; algorithms teach themselves in a process and language inscrutable to humans, making their learning process and growth a ‘black box’. Effectively, we cannot know how an AI reaches a given result or conclusion.

With such a lack of transparency in the AI’s fundamental operation, transparency elsewhere needs to be compensatory. This is especially true in a landscape where AI language models can ape human text with ease, misleading consumers into thinking their interactions are real.


But why is transparency so important? Fundamentally, it is because AI algorithms have the propensity, let alone potential, to be misinformation machines. Many already misunderstand the purpose and functionality of commercially available language models like ChatGPT, which is designed not to answer questions but simply to mimic language. 

As such, if asked a question, its directive is to generate what it ‘thinks’ an answer would look like – not provide a truthful answer. Transparency reduces the likelihood that such misinformation can be disseminated in more clandestine ways.

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