This post is part of a series focused on AI and how we can harness its tools, systems, capabilities and potential to further and optimize our creative practises.
In last week's post, we discussed the various shared fears of artificial intelligence, like the replacement of human workforces with machines, and AI technology "taking over the world" once it surpasses human intelligence.
These are all valid fears, as the future ability of artificial technology and machine learning is still unclear.
However, future fears can have a significant impact on present developments, overshadowing the current benefits of artificial general intelligence you could be experiencing throughout the majority of the world, and especially in the workplace.
In this blog post, we want to discuss how to combat some of the most common artificial intelligence fears: lack of privacy, the threat of bias, and being replaced.
We will delve into how these fears can be faced and eased, especially in real world examples like the marketing industry and the workplace in general.
How to combat your artificial intelligence fears
Fear 1 - being replaced at work
The fear - that as AI technology advances and improves, the need for humans in the workplace will become unnecessary because AI systems will be able to do our jobs more effectively and efficiently.
The cure - don't think of the situation as AI vs humans, but as a symbiotic relationship. Like with the rise of PCs in the workplace in the 80s-00s, that actually created more jobs for humans in order to keep the PCs running while the PCs augmented human work practises.
It still is a win-win situation, and the further introduction of AI tools into workplaces and society as a whole has every chance of being the same.
Fear 2 - AI and bias
The fear - like humans, artificial intelligence can be biased. Large language models, like google bard and generative ai, like stable diffusion, are susceptible to certain types of bias like measurement bias and stereotyping bias.
However, artificial intelligence systems may not be able to recognize and correct their bias.
This may lead to AI producing inaccurate information, or making poor decisions and negative outcomes that otherwise could have been avoided with a human brain.
The cure - recognize the confirmation bias and own the fear. Any bias found in an AI system is the fault of the programmers, data collection, data labeling or just incomplete data. So simply ignoring any bias rather than correcting it is a human error, not a machine learning one.
Regular evaluation of AI systems, or taking samples of your AI system's actions and having them examined by professionals, will offer insight into any present bias, and therefore how to correct the bias in the system.
Biased AI is a huge ethics concern, meaning it's a potential issue for every industry and business, especially those like finance and healthcare.
Fear 3 - lack of privacy
The fear - the potential lack of privacy, or anonymity, regarding our data, and what will be done with it. An AI system's parameters, once trained, shouldn't include any leading representation of the data it was trained with.
These data points could be individuals, meaning if the system was accessed unauthorized or hijacked, and the infiltrators were able to make deductions about the training data, their privacy and/or anonymity could be at risk.
There is also the case for what companies with access to personal private data will do with said data, like refusing certain services or "ranking" individuals socially .
The cure - AI security should be a priority for anyone who uses machine learning capabilities, meaning a proactive approach is required. Remember that, with the right security software in place and on-going research into new, less susceptible machine learning techniques, these threats can be reduced.
Artificial intelligence and the marketing industry
Where do we use AI in marketing?
The introduction of AI systems into the marketing process is just a step towards what we at Loops call the "modern creative process".
Introducing AI software to marketing research (like we do) and strategy offers companies and agencies the opportunity to understand their target consumers and their habits better than before.
As an AI system analyzes consumer habits, it can feed back to the teams what it's learnt, giving teams a better insight into how they can improve the consumer's journey, or their overall strategy, for example.
Companies could also use the insight to adapt their AI software and gear it more towards what the consumers are responding to in order to create a boost in sales or traction.
Loops and AI
Loops uses a form of natural language processing (NLP) called natural language understanding (NLU) in order to make sense of the masses of qualitative data we collect on your projects.
NLU allows AI software to interpret text or speech, and in some cases, allow the AI to communicate back through an ai model.
Think of the AI chatbots that pop up in the corner of an online shopping page. You're not actually talking to someone called Sarah from customer support - you're really talking to a NLU system.
At Loops, we use NLU to optimize the feedback and design processes, swiftly turning wordy comments into easy-to-digest, supportive statistics, and allowing you to make informed design changes in a timely manner. What would've taken potentially weeks before, Loops can do in a day.
Including Loops in your creative strategy is to take a step towards employing the "modern creative process", something companies need to embrace if they intend to keep up with the times.
