AI has a tricky relationship with facts. Can that be fixed?
Nov 14, 2024
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Brooke Hartley Moy, Co-founder and CEO of AI startup InFactory and former Salesforce and Google employee, joins to tackle the thorny issues around AI inaccuracies and biases. She emphasizes that AI models can often mislead with confidence. The discussion dives into the unique challenges of ensuring data integrity, combating misinformation, and fostering trust in AI systems. Moy also highlights the necessity for diversity in the tech workforce to drive ethical innovation and improve accountability within the AI landscape.
The conversation highlights the critical need for transparency in AI data sourcing to mitigate biases and misinformation.
Successful AI leaders must balance practical skills with ethical innovation, focusing on societal impacts over mere profit-making.
Deep dives
Concerns Surrounding AI Development
The dialogue at the tech conference centers around artificial intelligence, specifically the issues associated with large language models such as bias, transparency, and accuracy. These concerns have arisen as excitement about AI has been tempered by instances where generative AI produces misleading or incorrect results, referred to as ‘hallucinations.’ To address these pitfalls, experts are exploring ways to mitigate risks in AI development processes and build systems that can be trusted. The focus is on creating AI that can deliver accurate, factual results while minimizing the negative implications of potential inaccuracies.
Leadership Qualities Essential for AI Progress
Successful leaders in the emerging AI landscape must possess a blend of practical skills, diverse perspectives, and a commitment to ethical innovation. Those harnessing AI’s capabilities should aim to prioritize valuable advancements over merely chasing profits or rapid growth. The necessity for understanding societal impacts and advocating for responsible AI use is emphasized, as these leaders will fundamentally shape technological progress. Such leaders must go beyond traditional Silicon Valley mindsets and strive to create AI applications that genuinely benefit society.
Addressing Bias and Data Integrity in AI
Addressing biases inherent in AI systems requires recognizing that these biases stem from the datasets used to train models, which reflect societal prejudices. A transparent approach to data sourcing is crucial, ensuring careful selection of reliable inputs that differentiate quality sources from lower-grade content. Projects that aim to prevent the scraping of misinformation from the open web are preferred, as they establish a standard where respectful, factual content is prioritized. This focus on data quality is seen as vital for the credibility and trustworthiness of AI outputs.
Embracing AI Responsibly in Business
Businesses should begin implementing AI technologies thoughtfully rather than with reckless haste, recognizing the importance of a considered approach amidst burgeoning competition in the tech landscape. The long-term vision should prioritize safe and responsible use, fostering innovation that aligns with moral standards and community welfare. This foundational and ethical consideration can create a distinct competitive advantage, laying the groundwork for future success and sustainability in their AI endeavors. As AI continues to evolve, businesses must adapt to the changing dynamics while maintaining a commitment to ethical principles.
“AI models are confident liars.” That's the tagline for artificial intelligence application startup, Infactory. The company’s co-founder Brooke Hartley Moy joins CNBC’s Tom Chitty and Arjun Kharpal to discuss how to fix inaccuracies, bias and misinformation from AI.