Trust in AI Assistance

“The truly important events on the outside are not the trends. They are changes in the trends.”

-----(Peter Drucker’s book; The Effective Executive)

Trust in AI assistance is a complex and evolving concept. It refers to the level of confidence and reliance that individuals place in the capabilities and ethical use of AI systems designed to assist them in various tasks or decision-making processes. Trust is crucial because it affects how users interact with AI, depend on its recommendations, and integrate it into their lives.

For AI to truly be our assistant, it needs to be trustworthy. For it to be trustworthy, it must be under our control; it can’t be working behind the scenes for some tech monopoly. This means, at a minimum, the technology needs to be transparent. And we all need to understand how it works, at least a little bit.

AI tools can be so incredibly useful, they will increasingly pervade our lives, whether we trust them or not

Building trust in AI assistance involves several factors:

Reliability and Performance: AI systems should consistently provide accurate and reliable information or perform tasks as expected. When users observe that the AI consistently delivers helpful and accurate results, their trust in its abilities increases.

Transparency: Users need to understand how AI systems work, the data they use, and the algorithms they employ. Transparent AI systems that provide explanations for their recommendations or actions can help users understand and trust the technology better.

Privacy and Security: Users need assurance that their personal data is handled with care and protected against unauthorized access. Implementing robust privacy measures and security protocols helps build trust by demonstrating a commitment to user privacy.

Ethical Design and Use: AI systems should align with ethical principles and values, ensuring fairness, accountability, and avoiding biases. Users are more likely to trust AI assistance when they perceive it as unbiased, fair, and designed with their best interests in mind.

User Control and Empowerment: Allowing users to have control over AI assistance and its functionalities, such as customization options, feedback mechanisms, and the ability to override or modify suggestions, fosters a sense of empowerment and trust.

Users should be in control of the data used to train and fine-tune the AI system. When modern LLMs are built, they are first trained on massive, generic textual data from the Internet. Many systems go a step further by fine-tuning on more specific datasets purpose built for a narrow application, such as speaking in the language of an engineer, or mimicking the manner and style of their individual user. In the near future, corporate AIs will be routinely fed your data, probably without your awareness or your consent. 

Any trustworthy AI system should transparently allow users to control what data it uses.

It's worth noting that trust in AI assistance is not absolute and can vary among individuals. People's trust levels may be influenced by their past experiences, cultural factors, or personal beliefs. Therefore, it's important for developers and organizations to continuously engage with users, address concerns, and iterate on AI systems to foster trust and ensure their responsible deployment.

Realistically, we should all be preparing for a world where AI is not trustworthy.  Being a digital citizen, we should learn the basics of LLMs so that we can understand their risks and limitations for a given use case. This will prepare us to take advantage of AI tools, rather than being a data set.

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