TELUS International launches new generative AI solutions to mitigate bias and hallucinations, complementing the company’s existing competencies in natural language processing (NLP), AI data solutions, computational linguistics and content development

A recent survey from TELUS International, a leading digital customer experience (CX) innovator with nearly two decades of artificial intelligence (AI) experience, reveals customer concerns regarding bias in AI algorithms and perceived lack of transparency in the utilization of generative AI.

Missed Opportunities and Irrelevant Content?: Consumers Say Bias is Preventing AI From Making Smart Recommendations & Connecting Them to New Opportunities

• More than two in five (43%) believe bias within an AI algorithm caused them to be served the “wrong content”, such as music or TV programs they didn’t like and irrelevant job opportunities
• Almost one-third (32%) believe bias within an AI algorithm caused them to miss out on an opportunity, such as a financial application approval or job opportunity

Generative AI brings forth a new realm of possibilities for brands to enhance the experiences they deliver to their customers, but to ensure its success, it has to respond accurately and be free of harmful content. However, generative AI’s propensity to occasionally deliver inaccurate or nonsensical information – a phenomenon known as a hallucination – could potentially impact hard-won customer loyalty.

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“Harnessing human intelligence in a manner that reduces bias is key to successful machine learning”

Consumers Want Brands to Use AI — Only If Brands Are Honest About How They’re Using It

According to the survey, 40% of American consumers don’t believe companies who are using generative AI technology in their platforms are doing enough to protect users from bias and false information. Additionally, more than three-quarters (77%) believe that before brands integrate generative AI into their platforms, they should be required to audit their algorithms to mitigate bias and prejudice.

“With the rise of generative AI, the need for good and fair data has become more important than ever. Unlike traditional AI, generative AI creates new outputs based on the data it has been trained on, magnifying the impact of data quality on its overall performance,” said Siobhan Hanna, managing director, AI Data Solutions, TELUS International. “It is crucial that companies proactively address biased data and reckless algorithms from the start to avoid severe consequences and inaccurate outcomes. Model validation and tuning are essential for improving the performance and reliability of AI models as they help identify and address potential errors, improve accuracy and ensure that the model can effectively adapt to and make accurate predictions on new, previously unseen data. Additionally, by implementing appropriate policy guardrails, companies can protect customer data and promote a safer user experience while mitigating hallucinations and bias.”

Humans in the loop

Respondents also emphasized the importance of human involvement, with 49% noting that an AI algorithm can’t operate successfully without human input. Surprisingly, nearly one in five (19%) admitted they didn’t know that humans reviewed AI algorithms.

“Harnessing human intelligence in a manner that reduces bias is key to successful machine learning,” continued Hanna. “Unlike AI, humans have the ability to understand context and tone, which is crucial to ensuring bias is responsibly mitigated. To effectively reduce bias in AI, companies must source trusted and diverse training data sets that incorporate a wide range of views and perspectives. By adopting a ‘human in the loop’ approach, companies can ensure increased accuracy and reduced bias in its AI datasets.”

Power your generative AI initiatives with TELUS International

Regardless of where you’re at in your generative AI journey, TELUS International’s end-to-end solutions can help quickly advance your AI initiatives. TELUS International’s comprehensive range of AI solutions include: dataset engineering, the creation of training and test datasets, content generation and enhancement, model testing and prompt generation and enhancement. The company also provides software engineering services to implementers of generative AI technologies, with extensive capabilities for application development through the consultancy, design, build, deployment and maintenance phases.

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