Why We Need AI Literacy
Reason #5: Just because you CAN develop an algorithm, doesn’t mean you SHOULD
Source: Jordan Harrod
In recent years, jobs across all levels require understanding and usage of technology. As a result, computer and digital literacy is the #1 entry-level skill needed in the job market.
Computer literacy allows us to engage with society — finding a job, ordering takeout, searching an answer to a question — in ways previously unimaginable. Similarly, AI literacy is becoming increasingly necessary as well, as artificial intelligence systems become more integrated into our daily lives.
Here are the top five lessons I learned from her TEDx talk.
1. Knowing how humans and algorithms interact
AI literacy: a person’s ability to confidently understand and interact with AI-based systems
While we don’t need to understand the specific code behind all AI algorithms, understanding the interactions between humans and algorithms will help us in the long-term.
These algorithms affect our daily lives, and knowing how we interact with them could help us understand how we could use them for our own benefits, no matter small (see #2) or big (see #3).
2. AI literacy gives us more options.
Learning how we interact or can interact with AI systems gives us more options in our consumption of technology.
One good example of this comes from a technology use case familiar to many: social media. Twitter’s timeline shows users what their AI recommendation system has classified as “top tweets” — tweets they think you should see first. However, this might mean you miss your friend’s updates.
Thus, knowing that this recommendation algorithm exists lets you understand you have the option of turning that setting off and going back to reverse chronological sorting of letting the newest tweets pop up first.
3. Understanding AI will be a future work requirement
Some of us will need to learn new skills to find new employment replaced by AI.
Some of us will need to learn to work alongside AI systems.
Some of us will need to learn how to not impart our own biases upon the AI systems we design.
All of us will need AI literacy to do it.
4. What AI literacy entails depends on who we are
The average person doesn’t need to be bombarded with the nitty-gritty specifics of code, but some general understanding would help. Differentiating high-level concepts (“algorithm” v. “machine learning”). Learning the popular algorithms. Understanding what is hype and what is reality.
All of these would help the average person make more informed decisions.
For a policy maker, not only would they need to have the general understanding above, they also need to understand how to create policies to regulate these algorithms.
For AI developers, the fairness and ethics of the AI system is just as important as the code itself. Which brings us to the last lesson…
5. Just because you CAN develop an algorithm, doesn’t mean you SHOULD.
As this publication has previously and repeatedly highlighted, there exist many issues of fairness and bias in a variety of AI tools and systems. Just this week, we’ve seen prominent facial recognition systems from IBM and Amazon get eliminated or temporarily suspended due to their systems’ embedded racial bias.
AI literacy helps us better consider how a system affects people both inside and outside of our community. It leads us to recognize who is impacted, how they are impacted, and whether the AI systems work as they should be.
If you haven’t watched Jordan’s talk yet, I highly recommend you to do so, as well as check out her YouTube channel, a wonderful resource for gaining AI literacy. Until then, I hope you’ve enjoyed this summary.